





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

import black


lowerCAmelCase_    =    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_    =    '''    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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir ,							'''models/bert/'''   )   )
							snake_case_						:   Union[str, Any]           =  self.transformer_dir
							shutil.copy(
							    os.path.join(__magic_name__ ,							'''src/transformers/models/bert/modeling_bert.py'''   ) ,							os.path.join(self.transformer_dir ,							'''models/bert/modeling_bert.py'''   ) ,							)






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  '''src/transformers'''
							shutil.rmtree(self.transformer_dir   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None   )					->       Union[str, Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   str           =  check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''   )
							self.assertEqual(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       int:


							'''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''' ,							__magic_name__ ,							)

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

							# Copy consistency with a really long name
							snake_case_						:   Optional[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''' ,							__magic_name__ ,							__magic_name__   ) ,							)

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






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   str           =  check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

							snake_case_						:   List[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.'''
							)
							snake_case_						:   Optional[Any]           =  (
							    '''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_						:   Union[str, Any]           =  (
							    '''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_						:   Optional[int]           =  check_copies.convert_to_localized_md(
							    __magic_name__ ,							__magic_name__ ,							localized_readme['''format_model_list''']   )

							self.assertFalse(__magic_name__   )
							self.assertEqual(__magic_name__ ,							__magic_name__   )

							snake_case_						:   List[str]           =  check_copies.convert_to_localized_md(
							    __magic_name__ ,							__magic_name__ ,							localized_readme['''format_model_list''']   )

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

							snake_case_						:   List[str]           =  (
							    '''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_						:   Union[str, Any]           =  (
							    '''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_						:   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'''
							)

							snake_case_						:   Optional[int]           =  check_copies.convert_to_localized_md(
							    __magic_name__ ,							__magic_name__ ,							localized_readme['''format_model_list''']   )

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







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class  __lowerCAmelCase	(      unittest.TestCase,					_a					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  load_tool('''text-to-speech'''   )
							self.tool.setup()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  self.tool('''hey'''   )
							snake_case_						:   Union[str, Any]           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Any           =  self.tool('''hey'''   )
							snake_case_						:   str           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )
