






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

import black


__snake_case    :    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.
__snake_case    :    Any         =     """    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     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						tempfile.mkdtemp()
												os.makedirs(os.path.join(self.transformer_dir     ,						'models/bert/'   )   )
												lowerCAmelCase__    						=						self.transformer_dir
												shutil.copy(
												    os.path.join(_UpperCamelCase     ,						'src/transformers/models/bert/modeling_bert.py'   )     ,						os.path.join(self.transformer_dir     ,						'models/bert/modeling_bert.py'   )     ,						)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'src/transformers'
												shutil.rmtree(self.transformer_dir   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						comment + F"\nclass {class_name}(nn.Module):\n" + class_code
												if overwrite_result is not None:
																		lowerCAmelCase__    						=						comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
												lowerCAmelCase__    						=						black.Mode(target_versions={black.TargetVersion.PYaa}     ,						line_length=1_19   )
												lowerCAmelCase__    						=						black.format_str(_UpperCamelCase     ,						mode=_UpperCamelCase   )
												lowerCAmelCase__    						=						os.path.join(self.transformer_dir     ,						'new_code.py'   )
												with open(_UpperCamelCase     ,						'w'     ,						newline='\n'   ) as f:
																		f.write(_UpperCamelCase   )
												if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase   )   ) == 0   )
												else:
																		check_copies.is_copy_consistent(f.name     ,						overwrite=_UpperCamelCase   )
																		with open(_UpperCamelCase     ,						'r'   ) as f:
																								self.assertTrue(f.read()     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
												self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# 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'     ,						_UpperCamelCase     ,						)

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

												# Copy consistency with a really long name
												lowerCAmelCase__    						=						'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'     ,						_UpperCamelCase     ,						_UpperCamelCase   )     ,						)

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







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						check_copies.LOCALIZED_READMES['README_zh-hans.md']

												lowerCAmelCase__    						=						(
												    '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.'
												)
												lowerCAmelCase__    						=						(
												    '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'
												)
												lowerCAmelCase__    						=						(
												    '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'
												)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						check_copies.convert_to_localized_md(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						localized_readme['format_model_list']   )

												self.assertFalse(_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						check_copies.convert_to_localized_md(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						localized_readme['format_model_list']   )

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

												lowerCAmelCase__    						=						(
												    '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.'
												)
												lowerCAmelCase__    						=						(
												    '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'
												)
												lowerCAmelCase__    						=						(
												    '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'
												)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						check_copies.convert_to_localized_md(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						localized_readme['format_model_list']   )

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





