"""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   UpperCAmelCase__				(    lowerCAmelCase__			):


	"""simple docstring"""







	def __call__(			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     **_SCREAMING_SNAKE_CASE )    ->	Optional[int]:
							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(_lowerCamelCase       ,     _lowerCamelCase ) and table.get("query" ) is not None and table.get("table" ) is not None:
																			a_							:			Any     		=					[table]
													elif isinstance(_lowerCamelCase       ,     _lowerCamelCase ) and len(_lowerCamelCase ) > 0:
																			if not all(isinstance(_lowerCamelCase       ,     _lowerCamelCase ) for d in table ):
																									raise ValueError(
																									    f'''Keyword argument `table` should be a list of dict, but is {(type(_lowerCamelCase ) for d in table)}''' )

																			if table[0].get("query" ) is not None and table[0].get("table" ) is not None:
																									a_							:			Dict     		=					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(_lowerCamelCase       ,     _lowerCamelCase ) or isinstance(_lowerCamelCase       ,     types.GeneratorType ):
																			return table
													else:
																			raise ValueError(
																			    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
																			    f'''is {type(_lowerCamelCase )})''' )
							else:
													a_							:			Union[str, Any]     		=					[{"""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." )

																			a_							:			List[str]     		=					pd.DataFrame(tqa_pipeline_input["table"] )

							return tqa_pipeline_inputs




@add_end_docstrings(lowerCAmelCase__			)
class   UpperCAmelCase__				(    lowerCAmelCase__			):


	"""simple docstring"""
	lowerCAmelCase__    :					Optional[int]						      =							"""table,query"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=TableQuestionAnsweringArgumentHandler()       ,     *_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	Optional[Any]:
							super().__init__(*_lowerCamelCase       ,     **_lowerCamelCase )
							a_							:			int     		=					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() ) )

							a_							:			List[Any]     		=					bool(getattr(self.model.config       ,     "aggregation_labels"       ,     _lowerCamelCase ) ) and bool(
							    getattr(self.model.config       ,     "num_aggregation_labels"       ,     _lowerCamelCase ) )
							a_							:			Union[str, Any]     		=					"""tapas""" if hasattr(self.model.config       ,     "aggregation_labels" ) else None







	def       A    (			self       ,     **_SCREAMING_SNAKE_CASE )    ->	Optional[Any]:
							return self.model(**_lowerCamelCase )







	def       A    (			self       ,     **_SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:

							if self.framework == "pt":
													a_							:			Optional[Any]     		=					[]
													a_							:			Union[str, Any]     		=					[]
													a_							:			Union[str, Any]     		=					None
													a_							:			List[str]     		=					inputs["""input_ids"""].shape[0]

													a_							:			Optional[Any]     		=					inputs["""input_ids"""].to(self.device )
													a_							:			List[Any]     		=					inputs["""attention_mask"""].to(self.device )
													a_							:			Dict     		=					inputs["""token_type_ids"""].to(self.device )
													a_							:			Optional[int]     		=					None

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

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

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

																									a_							:			List[str]     		=					torch.from_numpy(_lowerCamelCase ).type(torch.long ).to(self.device )

																			a_							:			Optional[int]     		=					input_ids[index]
																			a_							:			Any     		=					attention_mask[index]  # shape (seq_len,)
																			a_							:			str     		=					token_type_ids[index]  # shape (seq_len, 7)
																			a_							:			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 )       ,     )
																			a_							:			List[str]     		=					outputs.logits

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

																			all_logits.append(_lowerCamelCase )

																			a_							:			List[Any]     		=					torch.distributions.Bernoulli(logits=_lowerCamelCase )
																			a_							:			str     		=					dist_per_token.probs * attention_mask_example.type(torch.floataa ).to(
																			    dist_per_token.probs.device )

																			a_							:			List[Any]     		=					collections.defaultdict(_lowerCamelCase )
																			for i, p in enumerate(probabilities.squeeze().tolist() ):
																									a_							:			Optional[Any]     		=					token_type_ids_example[:, 0].tolist()[i]
																									a_							:			Tuple     		=					token_type_ids_example[:, 1].tolist()[i] - 1
																									a_							:			List[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(_lowerCamelCase )

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

													a_							:			Optional[Any]     		=					torch.cat(tuple(_lowerCamelCase )       ,     0 )

													return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase )       ,     0 ))
							else:
													a_							:			Optional[int]     		=					[]
													a_							:			Tuple     		=					[]
													a_							:			List[str]     		=					None
													a_							:			Union[str, Any]     		=					inputs["""input_ids"""].shape[0]

													a_							:			Dict     		=					inputs["""input_ids"""]
													a_							:			Tuple     		=					inputs["""attention_mask"""]
													a_							:			Tuple     		=					inputs["""token_type_ids"""].numpy()
													a_							:			Dict     		=					None

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

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

																															if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																					a_							:			Optional[Any]     		=					int(prev_answers[(col_id, row_id)] )

																									a_							:			Dict     		=					model_labels

																			a_							:			int     		=					input_ids[index]
																			a_							:			int     		=					attention_mask[index]  # shape (seq_len,)
																			a_							:			Any     		=					token_type_ids[index]  # shape (seq_len, 7)
																			a_							:			Optional[int]     		=					self.model(
																			    input_ids=np.expand_dims(_lowerCamelCase       ,     axis=0 )       ,     attention_mask=np.expand_dims(_lowerCamelCase       ,     axis=0 )       ,     token_type_ids=np.expand_dims(_lowerCamelCase       ,     axis=0 )       ,     )
																			a_							:			Dict     		=					outputs.logits

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

																			all_logits.append(_lowerCamelCase )

																			a_							:			List[str]     		=					tfp.distributions.Bernoulli(logits=_lowerCamelCase )
																			a_							:			Dict     		=					dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase       ,     tf.floataa )

																			a_							:			int     		=					collections.defaultdict(_lowerCamelCase )
																			a_							:			List[Any]     		=					token_type_ids_example
																			for i, p in enumerate(tf.squeeze(_lowerCamelCase ).numpy().tolist() ):
																									a_							:			Optional[int]     		=					token_type_ids_example[:, 0].tolist()[i]
																									a_							:			Dict     		=					token_type_ids_example[:, 1].tolist()[i] - 1
																									a_							:			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(_lowerCamelCase )

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

													a_							:			Tuple     		=					tf.concat(tuple(_lowerCamelCase )       ,     0 )

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







	def __call__(			self       ,     *_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_							:			str     		=					self._args_parser(*_lowerCamelCase       ,     **_lowerCamelCase )

							a_							:			Any     		=					super().__call__(_lowerCamelCase       ,     **_lowerCamelCase )
							if len(_lowerCamelCase ) == 1:
													return results[0]
							return results







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     **_SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:
							a_							:			Optional[Any]     		=					{}
							if padding is not None:
													a_							:			Optional[Any]     		=					padding
							if truncation is not None:
													a_							:			Optional[Any]     		=					truncation

							a_							:			List[str]     		=					{}
							if sequential is not None:
													a_							:			str     		=					sequential
							return preprocess_params, forward_params, {}







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=None )    ->	List[str]:

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

							a_							:			Optional[int]     		=					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" )
							a_							:			Optional[Any]     		=					self.tokenizer(_lowerCamelCase       ,     _lowerCamelCase       ,     return_tensors=self.framework       ,     truncation=_lowerCamelCase       ,     padding=_lowerCamelCase )
							a_							:			Optional[int]     		=					table
							return inputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=False )    ->	Tuple:
							a_							:			Tuple     		=					model_inputs.pop("table" )

							if self.type == "tapas":
													if sequential:
																			a_							:			Dict     		=					self.sequential_inference(**_lowerCamelCase )
													else:
																			a_							:			Tuple     		=					self.batch_inference(**_lowerCamelCase )
							else:
													a_							:			Optional[Any]     		=					self.model.generate(**_lowerCamelCase )
							a_							:			int     		=					{"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
							return model_outputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Optional[int]:
							a_							:			str     		=					model_outputs["""model_inputs"""]
							a_							:			Optional[Any]     		=					model_outputs["""table"""]
							a_							:			Any     		=					model_outputs["""outputs"""]
							if self.type == "tapas":
													if self.aggregate:
																			a_							:			Optional[Any]     		=					outputs[:2]
																			a_							:			List[str]     		=					self.tokenizer.convert_logits_to_predictions(_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase )
																			a_							:			Optional[int]     		=					predictions
																			a_							:			int     		=					{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase )}

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

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

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





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




