





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase__		:     Optional[int]			=							'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class 							__lowerCAmelCase	(       unittest.TestCase		,		__magic_name__					):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase							       =			load_tool('''text-question-answering'''	)
            self.tool.setup()
            _UpperCamelCase							       =			load_tool('''text-question-answering'''			,					remote=lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.tool(lowerCAmelCase__			,					'''What did Hugging Face do in April 2021?'''	)
            self.assertEqual(lowerCAmelCase__			,					'''launched the BigScience Research Workshop'''	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.remote_tool(lowerCAmelCase__			,					'''What did Hugging Face do in April 2021?'''	)
            self.assertEqual(lowerCAmelCase__			,					'''launched the BigScience Research Workshop'''	)





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tool(text=lowerCAmelCase__			,					question='''What did Hugging Face do in April 2021?'''	)
            self.assertEqual(lowerCAmelCase__			,					'''launched the BigScience Research Workshop'''	)







       def snake_case__		(				self  :						List[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.remote_tool(text=lowerCAmelCase__			,					question='''What did Hugging Face do in April 2021?'''	)
            self.assertEqual(lowerCAmelCase__			,					'''launched the BigScience Research Workshop'''	)









'''simple docstring'''


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

import black


_lowercase  =			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.
_lowercase  =			"""    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 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,		'models/bert/'      )      )
							__snake_case						 =   self.transformer_dir
							shutil.copy(
							    os.path.join(__lowerCAmelCase       ,		'src/transformers/models/bert/modeling_bert.py'      )       ,		os.path.join(self.transformer_dir       ,		'models/bert/modeling_bert.py'      )       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'src/transformers'
							shutil.rmtree(self.transformer_dir      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]=None      ):
							__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + class_code
							if overwrite_result is not None:
													__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
							__snake_case						 =   black.Mode(target_versions={black.TargetVersion.PYaa}       ,		line_length=1_1_9      )
							__snake_case						 =   black.format_str(__lowerCAmelCase       ,		mode=__lowerCAmelCase      )
							__snake_case						 =   os.path.join(self.transformer_dir       ,		'new_code.py'      )
							with open(__lowerCAmelCase       ,		'w'       ,		newline='\n'      ) as f:
													f.write(__lowerCAmelCase      )
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase      )      ) == 0      )
							else:
													check_copies.is_copy_consistent(f.name       ,		overwrite=__lowerCAmelCase      )
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			self.assertTrue(f.read()       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							# Base copy consistency
							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'       ,		__lowerCAmelCase       ,		)

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

							# Copy consistency with a really long name
							__snake_case						 =   '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'       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )       ,		)

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




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   check_copies.LOCALIZED_READMES['README_zh-hans.md']

							__snake_case						 =   (
							    '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.'
							)
							__snake_case						 =   (
							    '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'
							)
							__snake_case						 =   (
							    '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'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							self.assertFalse(__lowerCAmelCase      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

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

							__snake_case						 =   (
							    '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.'
							)
							__snake_case						 =   (
							    '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'
							)
							__snake_case						 =   (
							    '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'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

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

