

'''simple docstring'''

import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase_     =      False


if is_vision_available():
						from PIL import Image
else:







						class lowerCamelCase   :

										@staticmethod
										def 					_lowerCamelCase    (       *lowercase_,						**lowercase_ )    ->							List[Any]:
											pass







@is_pipeline_test
@require_vision
class lowerCamelCase   (    unittest.TestCase				):
				snake_case_							     = MODEL_FOR_VISION_2_SEQ_MAPPING
				snake_case_							     = TF_MODEL_FOR_VISION_2_SEQ_MAPPING

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model=lowercase_,						tokenizer=lowercase_,						image_processor=lowercase_ )
					snake_case							=					[
					    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
					    './tests/fixtures/tests_samples/COCO/000000039769.png',
					]
					return pipe, examples

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': ANY(lowercase_ )}],
					        [{'generated_text': ANY(lowercase_ )}],
					    ],						)

				@require_tf
				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-vit-gpt2',						framework='tf' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        {
					            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					        },
					    ],						)

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					    ],						)

					snake_case							=					pipe(lowercase_,						max_new_tokens=1 )
					self.assertEqual(
					    lowercase_,						[{'generated_text': 'growth'}],						)

				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-vit-gpt2' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        {
					            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					        },
					    ],						)

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					    ],						)

				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-BlipForConditionalGeneration' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'
					snake_case							=					'a photo of'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertTrue(outputs[0]['generated_text'].startswith(lowercase_ ) )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model='ydshieh/vit-gpt2-coco-en' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}] )

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					    ],						)

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					pipeline('image-to-text',						model='Salesforce/blip-image-captioning-base' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cartoon of a purple character.'}] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model='Salesforce/blip-image-captioning-base' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'a photography of'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a photography of a volcano'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'a photo of a'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )

				@unittest.skipIf(
				    not is_torch_greater_or_equal_than_1_11,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' )
				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					pipeline('image-to-text',						model='google/pix2struct-ai2d-base' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'ash cloud'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )






				@slow
				@require_tf
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					pipeline('image-to-text',						model='ydshieh/vit-gpt2-coco-en',						framework='tf' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}] )

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					    ],						)













"""simple docstring"""



import unittest

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

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
    )
    from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=1_3				,						_lowerCamelCase=7				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=3_7				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.1				,						_lowerCamelCase=5_1_2				,						_lowerCamelCase=1_6				,						_lowerCamelCase=2				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=3				,						_lowerCamelCase=4				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			seq_length
      lowercase				       =			is_training
      lowercase				       =			use_input_mask
      lowercase				       =			use_token_type_ids
      lowercase				       =			use_labels
      lowercase				       =			vocab_size
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			intermediate_size
      lowercase				       =			hidden_act
      lowercase				       =			hidden_dropout_prob
      lowercase				       =			attention_probs_dropout_prob
      lowercase				       =			max_position_embeddings
      lowercase				       =			type_vocab_size
      lowercase				       =			type_sequence_label_size
      lowercase				       =			initializer_range
      lowercase				       =			num_labels
      lowercase				       =			num_choices
      lowercase				       =			scope







    def      UpperCamelCase_ (     self			):
      lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size			)

      lowercase				       =			None
      if self.use_input_mask:
        lowercase				       =			random_attention_mask([self.batch_size, self.seq_length]			)

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

      lowercase				       =			None
      lowercase				       =			None
      lowercase				       =			None
      if self.use_labels:
        lowercase				       =			ids_tensor([self.batch_size]				,						self.type_sequence_label_size			)
        lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						self.num_labels			)
        lowercase				       =			ids_tensor([self.batch_size]				,						self.num_choices			)
        lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						1			)

      lowercase				       =			self.get_config()

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          fake_token_labels,
      )







    def      UpperCamelCase_ (     self			):
      return ElectraConfig(
          vocab_size=self.vocab_size				,						hidden_size=self.hidden_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				,						is_decoder=_lowerCamelCase				,						initializer_range=self.initializer_range				,						)







    def      UpperCamelCase_ (     self			):
      (
          (
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	
      )				       =			self.prepare_config_and_inputs()

      lowercase				       =			True
      lowercase				       =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      lowercase				       =			ids_tensor([self.batch_size, self.seq_length]				,						vocab_size=2			)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraModel(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      lowercase				       =			model(_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			True
      lowercase				       =			ElectraModel(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						encoder_attention_mask=_lowerCamelCase				,						)
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForMaskedLM(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForCausalLM(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForTokenClassification(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForPreTraining(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_labels
      lowercase				       =			ElectraForSequenceClassification(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			ElectraForQuestionAnswering(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						start_positions=_lowerCamelCase				,						end_positions=_lowerCamelCase				,						)
      self.parent.assertEqual(result.start_logits.shape				,						(self.batch_size, self.seq_length)			)
      self.parent.assertEqual(result.end_logits.shape				,						(self.batch_size, self.seq_length)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						):
      lowercase				       =			self.num_choices
      lowercase				       =			ElectraForMultipleChoice(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			input_ids.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			token_type_ids.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			input_mask.unsqueeze(1			).expand(-1				,						self.num_choices				,						-1			).contiguous()
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						token_type_ids=_lowerCamelCase				,						labels=_lowerCamelCase				,						)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_choices)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      (
          (
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	(
          lowercase
      )       ,	
      )				       =			config_and_inputs
      lowercase				       =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[str]      	=(
        (
            ElectraModel,
            ElectraForPreTraining,
            ElectraForMaskedLM,
            ElectraForCausalLM,
            ElectraForMultipleChoice,
            ElectraForTokenClassification,
            ElectraForSequenceClassification,
            ElectraForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_					:	Optional[Any]      	=(
        {
            "feature-extraction": ElectraModel,
            "fill-mask": ElectraForMaskedLM,
            "question-answering": ElectraForQuestionAnswering,
            "text-classification": ElectraForSequenceClassification,
            "text-generation": ElectraForCausalLM,
            "token-classification": ElectraForTokenClassification,
            "zero-shot": ElectraForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase_					:	Tuple      	=True







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=False			):
      lowercase				       =			super()._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase				,						return_labels=_lowerCamelCase			)

      if return_labels:
        if model_class in get_values(_lowerCamelCase			):
          lowercase				       =			torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=torch.long				,						device=_lowerCamelCase			)
      return inputs_dict







    def      UpperCamelCase_ (     self			):
      lowercase				       =			ElectraModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						hidden_size=3_7			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_model(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_model_as_decoder(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowercase				       =			type
        self.model_tester.create_and_check_electra_model(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_masked_lm(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_token_classification(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_pretraining(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_sequence_classification(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_question_answering(*_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_multiple_choice(*_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase				       =			ElectraModel.from_pretrained(_lowerCamelCase			)
        self.assertIsNotNone(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_for_causal_lm(*_lowerCamelCase			)


@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			ElectraModel.from_pretrained('google/electra-small-discriminator'			)
      lowercase				       =			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]			)
      lowercase				       =			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase			)[0]
      lowercase				       =			torch.Size((1, 1_1, 2_5_6)			)
      self.assertEqual(output.shape				,						_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]			)

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,						_lowerCamelCase				,						atol=1e-4			)			)


