




'''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      _a       (		__lowerCAmelCase							):






     def        _lowercase			(				self					)							->    int:
            _snake_case							=   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      _a       :






     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, 128]      ,_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      ,)							->    Optional[int]:
            _snake_case							=   parent
            _snake_case							=   batch_size
            _snake_case							=   image_size
            _snake_case							=   num_channels
            _snake_case							=   num_encoder_blocks
            _snake_case							=   sr_ratios
            _snake_case							=   depths
            _snake_case							=   hidden_sizes
            _snake_case							=   downsampling_rates
            _snake_case							=   num_attention_heads
            _snake_case							=   is_training
            _snake_case							=   use_labels
            _snake_case							=   hidden_act
            _snake_case							=   hidden_dropout_prob
            _snake_case							=   attention_probs_dropout_prob
            _snake_case							=   initializer_range
            _snake_case							=   decoder_hidden_size
            _snake_case							=   num_labels
            _snake_case							=   scope






     def        _lowercase			(				self					)							->    Optional[Any]:
            _snake_case							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            _snake_case							=   None
            if self.use_labels:
                   _snake_case							=   ids_tensor([self.batch_size, self.image_size, self.image_size]      ,self.num_labels					)

            _snake_case							=   self.get_config()
            return config, pixel_values, labels






     def        _lowercase			(				self					)							->    Tuple:
            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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
            _snake_case							=   GLPNModel(config=_SCREAMING_SNAKE_CASE					)
            model.to(_SCREAMING_SNAKE_CASE					)
            model.eval()
            _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
            _snake_case							=   _snake_case							=   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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Tuple:
            _snake_case							=   self.num_labels
            _snake_case							=   GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE					)
            model.to(_SCREAMING_SNAKE_CASE					)
            model.eval()
            _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
            self.parent.assertEqual(result.predicted_depth.shape      ,(self.batch_size, self.image_size, self.image_size)					)
            _snake_case							=   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        _lowercase			(				self					)							->    Union[str, Any]:
            _snake_case							=   self.prepare_config_and_inputs()
            _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
            _snake_case							=   {"pixel_values": pixel_values}
            return config, inputs_dict







@require_torch
class      _a       (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		(
         {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
     )

     SCREAMING_SNAKE_CASE_							:					Any					       =		False
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		False
     SCREAMING_SNAKE_CASE_							:					int					       =		False






     def        _lowercase			(				self					)							->    List[str]:
            _snake_case							=   GLPNModelTester(self					)
            _snake_case							=   GLPNConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    int:
            self.config_tester.run_common_tests()






     def        _lowercase			(				self					)							->    Tuple:
            _snake_case							=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[int]:
            _snake_case							=   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        _lowercase			(				self					)							->    Union[str, Any]:
            pass






     @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"					)
     def        _lowercase			(				self					)							->    List[str]:
            pass






     def        _lowercase			(				self					)							->    Any:
            _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

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

                   _snake_case							=   ["pixel_values"]
                   self.assertListEqual(arg_names[:1]      ,_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Any:
            _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
            _snake_case							=   True

            for model_class in self.all_model_classes:
                   _snake_case							=   True
                   _snake_case							=   False
                   _snake_case							=   True
                   _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
                   model.to(_SCREAMING_SNAKE_CASE					)
                   model.eval()
                   with torch.no_grad():
                          _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
                   _snake_case							=   outputs.attentions

                   _snake_case							=   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"]
                   _snake_case							=   True
                   _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
                   model.to(_SCREAMING_SNAKE_CASE					)
                   model.eval()
                   with torch.no_grad():
                          _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
                   _snake_case							=   outputs.attentions

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

                   # verify the first attentions (first block, first layer)
                   _snake_case							=   (self.model_tester.image_size // 4) ** 2
                   _snake_case							=   (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)
                   _snake_case							=   (self.model_tester.image_size // 32) ** 2
                   _snake_case							=   (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]      ,)
                   _snake_case							=   len(_SCREAMING_SNAKE_CASE					)

                   # Check attention is always last and order is fine
                   _snake_case							=   True
                   _snake_case							=   True
                   _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
                   model.to(_SCREAMING_SNAKE_CASE					)
                   model.eval()
                   with torch.no_grad():
                          _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)

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

                   _snake_case							=   outputs.attentions

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)
                   # verify the first attentions (first block, first layer)
                   _snake_case							=   (self.model_tester.image_size // 4) ** 2
                   _snake_case							=   (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        _lowercase			(				self					)							->    str:
            def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					):
                   _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
                   model.to(_SCREAMING_SNAKE_CASE					)
                   model.eval()

                   with torch.no_grad():
                          _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)

                   _snake_case							=   outputs.hidden_states

                   _snake_case							=   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,
                       ]      ,)

            _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _snake_case							=   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"]
                   _snake_case							=   True

                   check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[Any]:
            if not self.model_tester.is_training:
                   return

            _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
            _snake_case							=   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":
                          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   inputs_dict["pixel_values"].shape
                          _snake_case							=   torch.zeros(
                              [self.model_tester.batch_size, height, width]      ,device=_SCREAMING_SNAKE_CASE					).long()
                   _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
                   model.to(_SCREAMING_SNAKE_CASE					)
                   model.train()
                   _snake_case							=   self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,return_labels=_SCREAMING_SNAKE_CASE					)
                   _snake_case							=   model(**_SCREAMING_SNAKE_CASE					).loss
                   loss.backward()






     @slow
     def        _lowercase			(				self					)							->    List[str]:
            for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _snake_case							=   GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







def 			__a			(     )		->				Tuple:

       """simple docstring"""




       _snake_case							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
       return image





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






     @slow
     def        _lowercase			(				self					)							->    int:
            _snake_case							=   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
            _snake_case							=   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_SCREAMING_SNAKE_CASE					)

            _snake_case							=   prepare_img()
            _snake_case							=   image_processor(images=_SCREAMING_SNAKE_CASE      ,return_tensors="pt"					).to(_SCREAMING_SNAKE_CASE					)

            # forward pass
            with torch.no_grad():
                   _snake_case							=   model(**_SCREAMING_SNAKE_CASE					)

            # verify the predicted depth
            _snake_case							=   torch.Size([1, 480, 640]					)
            self.assertEqual(outputs.predicted_depth.shape      ,_SCREAMING_SNAKE_CASE					)

            _snake_case							=   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					)					)








'''simple docstring'''





import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )

class      _a       (		unittest.TestCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=24      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=4      ,)							->    List[str]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   seq_length
          _snake_case							=   is_training
          _snake_case							=   use_attention_mask
          _snake_case							=   use_token_type_ids
          _snake_case							=   use_labels
          _snake_case							=   vocab_size
          _snake_case							=   hidden_size
          _snake_case							=   embedding_size
          _snake_case							=   num_hidden_layers
          _snake_case							=   num_attention_heads
          _snake_case							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   type_vocab_size
          _snake_case							=   type_sequence_label_size
          _snake_case							=   initializer_range
          _snake_case							=   num_choices






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

          _snake_case							=   None
          if self.use_attention_mask:
               _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size					)

          _snake_case							=   ElectraConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True

     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   FlaxElectraModelTester(self					)






     @slow
     def        _lowercase			(				self					)							->    List[Any]:
          for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-generator"					)
               else:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-discriminator"					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)


