



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase    :		List[Any]		      =    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_lowerCamelCase    :		Tuple		      =    """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir       ,	'models/bert/'	)	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.transformer_dir
											shutil.copy(
											    os.path.join(snake_case       ,	'src/transformers/models/bert/modeling_bert.py'	)       ,	os.path.join(self.transformer_dir       ,	'models/bert/modeling_bert.py'	)       ,	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				'src/transformers'
											shutil.rmtree(self.transformer_dir	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Any       ,	snake_case    :				Tuple       ,	snake_case    :				str       ,	snake_case    :				Optional[Any]=None	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
											if overwrite_result is not None:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
											SCREAMING_SNAKE_CASE							:     Any						=				black.Mode(target_versions={black.TargetVersion.PYaa}       ,	line_length=119	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				black.format_str(snake_case       ,	mode=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				os.path.join(self.transformer_dir       ,	'new_code.py'	)
											with open(snake_case       ,	'w'       ,	newline='\n'	) as f:
																f.write(snake_case	)
											if overwrite_result is None:
																self.assertTrue(len(check_copies.is_copy_consistent(snake_case	)	) == 0	)
											else:
																check_copies.is_copy_consistent(f.name       ,	overwrite=snake_case	)
																with open(snake_case       ,	'r'	) as f:
																					self.assertTrue(f.read()       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'	)
											self.assertEqual(snake_case       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,	'BertLMPredictionHead'       ,	REFERENCE_CODE + '\n'       ,	)

											# With no empty line at the end
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,	'BertLMPredictionHead'       ,	snake_case       ,	)

											# Copy consistency with rename
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,	'TestModelLMPredictionHead'       ,	re.sub('Bert'       ,	'TestModel'       ,	snake_case	)       ,	)

											# Copy consistency with a really long name
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
											self.check_copy_consistency(
											    f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''       ,	f'''{long_class_name}LMPredictionHead'''       ,	re.sub('Bert'       ,	snake_case       ,	snake_case	)       ,	)

											# Copy consistency with overwrite
											self.check_copy_consistency(
											    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,	'TestModelLMPredictionHead'       ,	snake_case       ,	overwrite_result=re.sub('Bert'       ,	'TestModel'       ,	snake_case	)       ,	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
											    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
											    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
											    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
											    ' Luong, Quoc V. Le, Christopher D. Manning.'
											)
											SCREAMING_SNAKE_CASE							:     List[str]						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
											    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
											    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
											    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
											    ' method has been applied to compress GPT2 into'
											    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
											    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
											    ' Multilingual BERT into'
											    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
											    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
											    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
											    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
											    ' Christopher D. Manning 发布。\n'
											)

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				check_copies.convert_to_localized_md(
											    snake_case       ,	snake_case       ,	localized_readme['format_model_list']	)

											self.assertFalse(snake_case	)
											self.assertEqual(snake_case       ,	snake_case	)

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				check_copies.convert_to_localized_md(
											    snake_case       ,	snake_case       ,	localized_readme['format_model_list']	)

											# Check whether the number of models is equal to README.md after conversion.
											self.assertTrue(snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
											    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
											    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
											    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
											)
											SCREAMING_SNAKE_CASE							:     int						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
											    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				(
											    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
											    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
											    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
											    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
											)

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				check_copies.convert_to_localized_md(
											    snake_case       ,	snake_case       ,	localized_readme['format_model_list']	)

											# Check if the model link is synchronized.
											self.assertEqual(snake_case       ,	snake_case	)




import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:


			class 						lowercase    :
									'''simple docstring'''


									@staticmethod
									def       lowerCamelCase_				(  *snake_case    :				Dict       ,	**snake_case    :				List[Any]	):



														'''simple docstring'''


														pass


@is_pipeline_test
@require_torch
@require_vision
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipeline('visual-question-answering'       ,	model='hf-internal-testing/tiny-vilt-random-vqa'	)
											SCREAMING_SNAKE_CASE							:     Any						=				[
											    {
											        'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
											        'question': 'How many cats are there?',
											    },
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'question': 'How many cats are there?',
											    },
											]
											return vqa_pipeline, examples







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				int       ,	snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vqa_pipeline(snake_case       ,	top_k=1	)
											self.assertEqual(
											    snake_case       ,	[
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											    ]       ,	)







						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline('visual-question-answering'       ,	model='hf-internal-testing/tiny-vilt-random-vqa'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
											SCREAMING_SNAKE_CASE							:     Any						=				'How many cats are there?'

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				vqa_pipeline(image=snake_case       ,	question='How many cats are there?'       ,	top_k=2	)
											self.assertEqual(
											    snake_case       ,	[{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}, {'score': ANY(snake_case	), 'answer': ANY(snake_case	)}]	)

											SCREAMING_SNAKE_CASE							:     int						=				vqa_pipeline({'image': image, 'question': question}       ,	top_k=2	)
											self.assertEqual(
											    snake_case       ,	[{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}, {'score': ANY(snake_case	), 'answer': ANY(snake_case	)}]	)







						@slow
						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				pipeline('visual-question-answering'       ,	model='dandelin/vilt-b32-finetuned-vqa'	)
											SCREAMING_SNAKE_CASE							:     int						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
											SCREAMING_SNAKE_CASE							:     Dict						=				'How many cats are there?'

											SCREAMING_SNAKE_CASE							:     int						=				vqa_pipeline(image=snake_case       ,	question=snake_case       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				vqa_pipeline({'image': image, 'question': question}       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     Any						=				vqa_pipeline(
											    [{'image': image, 'question': question}, {'image': image, 'question': question}]       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2       ,	)







						@require_tf
						@unittest.skip('Visual question answering not implemented in TF'	)
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											pass