





"""simple docstring"""
import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  )

if is_tf_available() and is_tensorflow_probability_available():
  import tensorflow as tf
  import tensorflow_probability as tfp

  from ..models.auto.modeling_tf_auto import (
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  )






class       _SCREAMING_SNAKE_CASE(     A						):




       def __call__(		self	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,**SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             requires_backends(self	,'''pandas'''							)
             import pandas as pd

             if table is None:
                   raise ValueError('''Keyword argument `table` cannot be None.'''							)
             elif query is None:
                   if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) and table.get('''query'''							) is not None and table.get('''table'''							) is not None:
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						[table]
                   elif isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) and len(SCREAMING_SNAKE_CASE__							) > 0:
                         if not all(isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) for d in table							):
                               raise ValueError(
                                   f'''Keyword argument `table` should be a list of dict, but is {(type(SCREAMING_SNAKE_CASE__							) for d in table)}'''							)

                         if table[0].get('''query'''							) is not None and table[0].get('''table'''							) is not None:
                               __SCREAMING_SNAKE_CASE     :Tuple	       =						table
                         else:
                               raise ValueError(
                                   '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                                   f''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''							)
                   elif Dataset is not None and isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) or isinstance(SCREAMING_SNAKE_CASE__	,types.GeneratorType							):
                         return table
                   else:
                         raise ValueError(
                             '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
                             f'''is {type(SCREAMING_SNAKE_CASE__							)})'''							)
             else:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[{'''table''': table, '''query''': query}]

             for tqa_pipeline_input in tqa_pipeline_inputs:
                   if not isinstance(tqa_pipeline_input['''table''']	,pd.DataFrame							):
                         if tqa_pipeline_input["table"] is None:
                               raise ValueError('''Table cannot be None.'''							)

                         __SCREAMING_SNAKE_CASE     :Tuple	       =						pd.DataFrame(tqa_pipeline_input['''table''']							)

             return tqa_pipeline_inputs






@add_end_docstrings(A						)
class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   List[str]					=						'''table,query'''




       def __init__(		self	,SCREAMING_SNAKE_CASE__=TableQuestionAnsweringArgumentHandler()	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             super().__init__(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :str	       =						args_parser

             self.check_model_type(
                 dict(
                     TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
                     + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)
                 if self.framework == '''tf'''
                 else dict(
                     MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)							)

             __SCREAMING_SNAKE_CASE     :int	       =						bool(getattr(self.model.config	,'''aggregation_labels'''	,SCREAMING_SNAKE_CASE__							)							) and bool(
                 getattr(self.model.config	,'''num_aggregation_labels'''	,SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						'''tapas''' if hasattr(self.model.config	,'''aggregation_labels'''							) else None




       def 					_UpperCamelCase  (		self	,**SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             return self.model(**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,**SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             if self.framework == "pt":
                   __SCREAMING_SNAKE_CASE     :Any	       =						[]
                   __SCREAMING_SNAKE_CASE     :str	       =						[]
                   __SCREAMING_SNAKE_CASE     :Any	       =						None
                   __SCREAMING_SNAKE_CASE     :Any	       =						inputs['''input_ids'''].shape[0]

                   __SCREAMING_SNAKE_CASE     :Any	       =						inputs['''input_ids'''].to(self.device							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						inputs['''attention_mask'''].to(self.device							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						inputs['''token_type_ids'''].to(self.device							)
                   __SCREAMING_SNAKE_CASE     :Dict	       =						None

                   for index in range(SCREAMING_SNAKE_CASE__							):
                         # If sequences have already been processed, the token type IDs will be created according to the previous
                         # answer.
                         if prev_answers is not None:
                               __SCREAMING_SNAKE_CASE     :List[str]	       =						token_type_ids_example[:, 3]  # shape (seq_len,)
                               __SCREAMING_SNAKE_CASE     :Any	       =						np.zeros_like(prev_labels_example.cpu().numpy()							)  # shape (seq_len,)

                               __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						token_type_ids[index]  # shape (seq_len, 7)
                               for i in range(model_labels.shape[0]							):
                                     __SCREAMING_SNAKE_CASE     :Dict	       =						token_type_ids_example[:, 0].tolist()[i]
                                     __SCREAMING_SNAKE_CASE     :Optional[int]	       =						token_type_ids_example[:, 1].tolist()[i] - 1
                                     __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						token_type_ids_example[:, 2].tolist()[i] - 1

                                     if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                           __SCREAMING_SNAKE_CASE     :Dict	       =						int(prev_answers[(col_id, row_id)]							)

                               __SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.from_numpy(SCREAMING_SNAKE_CASE__							).type(torch.long							).to(self.device							)

                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						input_ids[index]
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						attention_mask[index]  # shape (seq_len,)
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						token_type_ids[index]  # shape (seq_len, 7)
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model(
                             input_ids=input_ids_example.unsqueeze(0							)	,attention_mask=attention_mask_example.unsqueeze(0							)	,token_type_ids=token_type_ids_example.unsqueeze(0							)	,)
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						outputs.logits

                         if self.aggregate:
                               all_aggregations.append(outputs.logits_aggregation							)

                         all_logits.append(SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						dist_per_token.probs * attention_mask_example.type(torch.floataa							).to(
                             dist_per_token.probs.device							)

                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						collections.defaultdict(SCREAMING_SNAKE_CASE__							)
                         for i, p in enumerate(probabilities.squeeze().tolist()							):
                               __SCREAMING_SNAKE_CASE     :Optional[int]	       =						token_type_ids_example[:, 0].tolist()[i]
                               __SCREAMING_SNAKE_CASE     :Dict	       =						token_type_ids_example[:, 1].tolist()[i] - 1
                               __SCREAMING_SNAKE_CASE     :Any	       =						token_type_ids_example[:, 2].tolist()[i] - 1
                               if col >= 0 and row >= 0 and segment_id == 1:
                                     coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

                   __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.cat(tuple(SCREAMING_SNAKE_CASE__							)	,0							)

                   return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(SCREAMING_SNAKE_CASE__							)	,0							))
             else:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						[]
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[]
                   __SCREAMING_SNAKE_CASE     :Dict	       =						None
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						inputs['''input_ids'''].shape[0]

                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						inputs['''input_ids''']
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						inputs['''attention_mask''']
                   __SCREAMING_SNAKE_CASE     :Dict	       =						inputs['''token_type_ids'''].numpy()
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						None

                   for index in range(SCREAMING_SNAKE_CASE__							):
                         # If sequences have already been processed, the token type IDs will be created according to the previous
                         # answer.
                         if prev_answers is not None:
                               __SCREAMING_SNAKE_CASE     :Any	       =						token_type_ids_example[:, 3]  # shape (seq_len,)
                               __SCREAMING_SNAKE_CASE     :Dict	       =						np.zeros_like(SCREAMING_SNAKE_CASE__	,dtype=np.intaa							)  # shape (seq_len,)

                               __SCREAMING_SNAKE_CASE     :List[str]	       =						token_type_ids[index]  # shape (seq_len, 7)
                               for i in range(model_labels.shape[0]							):
                                     __SCREAMING_SNAKE_CASE     :Dict	       =						token_type_ids_example[:, 0].tolist()[i]
                                     __SCREAMING_SNAKE_CASE     :Dict	       =						token_type_ids_example[:, 1].tolist()[i] - 1
                                     __SCREAMING_SNAKE_CASE     :Any	       =						token_type_ids_example[:, 2].tolist()[i] - 1

                                     if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                           __SCREAMING_SNAKE_CASE     :str	       =						int(prev_answers[(col_id, row_id)]							)

                               __SCREAMING_SNAKE_CASE     :str	       =						model_labels

                         __SCREAMING_SNAKE_CASE     :Tuple	       =						input_ids[index]
                         __SCREAMING_SNAKE_CASE     :int	       =						attention_mask[index]  # shape (seq_len,)
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						token_type_ids[index]  # shape (seq_len, 7)
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model(
                             input_ids=np.expand_dims(SCREAMING_SNAKE_CASE__	,axis=0							)	,attention_mask=np.expand_dims(SCREAMING_SNAKE_CASE__	,axis=0							)	,token_type_ids=np.expand_dims(SCREAMING_SNAKE_CASE__	,axis=0							)	,)
                         __SCREAMING_SNAKE_CASE     :int	       =						outputs.logits

                         if self.aggregate:
                               all_aggregations.append(outputs.logits_aggregation							)

                         all_logits.append(SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE__	,tf.floataa							)

                         __SCREAMING_SNAKE_CASE     :List[str]	       =						collections.defaultdict(SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						token_type_ids_example
                         for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE__							).numpy().tolist()							):
                               __SCREAMING_SNAKE_CASE     :Tuple	       =						token_type_ids_example[:, 0].tolist()[i]
                               __SCREAMING_SNAKE_CASE     :Optional[int]	       =						token_type_ids_example[:, 1].tolist()[i] - 1
                               __SCREAMING_SNAKE_CASE     :str	       =						token_type_ids_example[:, 2].tolist()[i] - 1
                               if col >= 0 and row >= 0 and segment_id == 1:
                                     coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE     :str	       =						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						tf.concat(tuple(SCREAMING_SNAKE_CASE__							)	,0							)

                   return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(SCREAMING_SNAKE_CASE__							)	,0							))




       def __call__(		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						self._args_parser(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :Dict	       =						super().__call__(SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)
             if len(SCREAMING_SNAKE_CASE__							) == 1:
                   return results[0]
             return results




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,**SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						{}
             if padding is not None:
                   __SCREAMING_SNAKE_CASE     :Any	       =						padding
             if truncation is not None:
                   __SCREAMING_SNAKE_CASE     :Any	       =						truncation

             __SCREAMING_SNAKE_CASE     :str	       =						{}
             if sequential is not None:
                   __SCREAMING_SNAKE_CASE     :Any	       =						sequential
             return preprocess_params, forward_params, {}




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=None							)    ->   int:





             """simple docstring"""






             if truncation is None:
                   if self.type == "tapas":
                         __SCREAMING_SNAKE_CASE     :Dict	       =						'''drop_rows_to_fit'''
                   else:
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''do_not_truncate'''

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[Any]	       =						pipeline_input['''table'''], pipeline_input['''query''']
             if table.empty:
                   raise ValueError('''table is empty'''							)
             if query is None or query == "":
                   raise ValueError('''query is empty'''							)
             __SCREAMING_SNAKE_CASE     :int	       =						self.tokenizer(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_tensors=self.framework	,truncation=SCREAMING_SNAKE_CASE__	,padding=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						table
             return inputs




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						model_inputs.pop('''table'''							)

             if self.type == "tapas":
                   if sequential:
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.sequential_inference(**SCREAMING_SNAKE_CASE__							)
                   else:
                         __SCREAMING_SNAKE_CASE     :str	       =						self.batch_inference(**SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model.generate(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :str	       =						{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
             return model_outputs




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model_outputs['''model_inputs''']
             __SCREAMING_SNAKE_CASE     :Dict	       =						model_outputs['''table''']
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model_outputs['''outputs''']
             if self.type == "tapas":
                   if self.aggregate:
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						outputs[:2]
                         __SCREAMING_SNAKE_CASE     :int	       =						self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[int]	       =						predictions
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(SCREAMING_SNAKE_CASE__							)}

                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model.config.no_aggregation_label_index
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						{
                             i: aggregators[i] + ''' > ''' for i, pred in enumerate(SCREAMING_SNAKE_CASE__							) if pred != no_agg_label_index
                         }
                   else:
                         __SCREAMING_SNAKE_CASE     :Dict	       =						outputs[0]
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						predictions[0]
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						{}
                         __SCREAMING_SNAKE_CASE     :Any	       =						{}
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						[]
                   for index, coordinates in enumerate(SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						[table.iat[coordinate] for coordinate in coordinates]
                         __SCREAMING_SNAKE_CASE     :Dict	       =						aggregators.get(SCREAMING_SNAKE_CASE__	,''''''							)
                         __SCREAMING_SNAKE_CASE     :Any	       =						aggregators_prefix.get(SCREAMING_SNAKE_CASE__	,''''''							)
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						{
                             '''answer''': aggregator_prefix + ''', '''.join(SCREAMING_SNAKE_CASE__							),
                             '''coordinates''': coordinates,
                             '''cells''': [table.iat[coordinate] for coordinate in coordinates],
                         }
                         if aggregator:
                               __SCREAMING_SNAKE_CASE     :Tuple	       =						aggregator

                         answers.append(SCREAMING_SNAKE_CASE__							)
                   if len(SCREAMING_SNAKE_CASE__							) == 0:
                         raise PipelineException('''Empty answer'''							)
             else:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[{'''answer''': answer} for answer in self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE__	,skip_special_tokens=SCREAMING_SNAKE_CASE__							)]

             return answers if len(SCREAMING_SNAKE_CASE__							) > 1 else answers[0]






"""simple docstring"""
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor






class       _SCREAMING_SNAKE_CASE(     A						):




       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.config_class(**self.inputs_dict							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''hidden_sizes'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_attention_heads'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_encoder_blocks'''							)							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=64	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=[2, 2, 2, 2]	,SCREAMING_SNAKE_CASE__=[8, 4, 2, 1]	,SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]	,SCREAMING_SNAKE_CASE__=[1, 4, 8, 16]	,SCREAMING_SNAKE_CASE__=[1, 2, 4, 8]	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=None	,)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						parent
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						batch_size
             __SCREAMING_SNAKE_CASE     :str	       =						image_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						num_channels
             __SCREAMING_SNAKE_CASE     :str	       =						num_encoder_blocks
             __SCREAMING_SNAKE_CASE     :Any	       =						sr_ratios
             __SCREAMING_SNAKE_CASE     :Tuple	       =						depths
             __SCREAMING_SNAKE_CASE     :Dict	       =						hidden_sizes
             __SCREAMING_SNAKE_CASE     :Any	       =						downsampling_rates
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						num_attention_heads
             __SCREAMING_SNAKE_CASE     :Dict	       =						is_training
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						use_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						hidden_act
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						hidden_dropout_prob
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE     :int	       =						initializer_range
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						decoder_hidden_size
             __SCREAMING_SNAKE_CASE     :Any	       =						num_labels
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						scope




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             __SCREAMING_SNAKE_CASE     :Any	       =						None
             if self.use_labels:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels							)

             __SCREAMING_SNAKE_CASE     :int	       =						self.get_config()
             return config, pixel_values, labels




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return GLPNConfig(
                 image_size=self.image_size	,num_channels=self.num_channels	,num_encoder_blocks=self.num_encoder_blocks	,depths=self.depths	,hidden_sizes=self.hidden_sizes	,num_attention_heads=self.num_attention_heads	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,initializer_range=self.initializer_range	,decoder_hidden_size=self.decoder_hidden_size	,)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GLPNModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :int	       =						model(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						self.image_size // (self.downsampling_rates[-1] * 2)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.num_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNForDepthEstimation(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(SCREAMING_SNAKE_CASE__	,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						config_and_inputs
             __SCREAMING_SNAKE_CASE     :Any	       =						{'''pixel_values''': pixel_values}
             return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE(     A					,			A					,			unittest.TestCase						):
       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_     :   int					=						(
           {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
       )

       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						False
       SCREAMING_SNAKE_CASE_     :   str					=						False
       SCREAMING_SNAKE_CASE_     :   Dict					=						False




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						GLPNModelTester(self							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE__							)




       @unittest.skip('''GLPN does not use inputs_embeds'''							)
       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             pass




       @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             pass




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Any	       =						inspect.signature(model.forward							)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						[*signature.parameters.keys()]

                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						['''pixel_values''']
                   self.assertListEqual(arg_names[:1]	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :Any	       =						True

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :str	       =						True
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						False
                   __SCREAMING_SNAKE_CASE     :int	       =						True
                   __SCREAMING_SNAKE_CASE     :Dict	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :str	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.attentions

                   __SCREAMING_SNAKE_CASE     :List[str]	       =						sum(self.model_tester.depths							)
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   __SCREAMING_SNAKE_CASE     :Dict	       =						True
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :str	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)

                   # verify the last attentions (last block, last layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 32) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                   self.assertListEqual(
                       list(attentions[-1].shape[-3:]							)	,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						len(SCREAMING_SNAKE_CASE__							)

                   # Check attention is always last and order is fine
                   __SCREAMING_SNAKE_CASE     :Any	       =						True
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                   __SCREAMING_SNAKE_CASE     :Any	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Dict	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

                   self.assertEqual(out_len + 1	,len(SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :int	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)
                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             def check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :Any	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()

                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.hidden_states

                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.model_tester.num_encoder_blocks
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-3:]							)	,[
                           self.model_tester.hidden_sizes[0],
                           self.model_tester.image_size // 4,
                           self.model_tester.image_size // 4,
                       ]	,)

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   __SCREAMING_SNAKE_CASE     :int	       =						True

                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             if not self.model_tester.is_training:
                   return

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						True

             for model_class in self.all_model_classes:
                   if model_class in get_values(SCREAMING_SNAKE_CASE__							):
                         continue
                   # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                   # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                   if model_class.__name__ == "GLPNForDepthEstimation":
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Any	       =						inputs_dict['''pixel_values'''].shape
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.zeros(
                             [self.model_tester.batch_size, height, width]	,device=SCREAMING_SNAKE_CASE__							).long()
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.train()
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :str	       =						model(**SCREAMING_SNAKE_CASE__							).loss
                   loss.backward()




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE     :str	       =						GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




def   __lowerCamelCase     (							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :int	       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







@require_torch
@require_vision
@slow
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						prepare_img()
             __SCREAMING_SNAKE_CASE     :int	       =						image_processor(images=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)

             # forward pass
             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						model(**SCREAMING_SNAKE_CASE__							)

             # verify the predicted depth
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_80, 6_40]							)
             self.assertEqual(outputs.predicted_depth.shape	,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						torch.tensor(
                 [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(SCREAMING_SNAKE_CASE__							)

             self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)