


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

import black


UpperCAmelCase   :    Dict     =		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.
UpperCAmelCase   :    Optional[int]     =		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

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







					'''simple docstring'''







					lowerCamelCase    		=  tempfile.mkdtemp()
					os.makedirs(os.path.join(self.transformer_dir		,      """models/bert/"""	)	)
					lowerCamelCase    		=  self.transformer_dir
					shutil.copy(
					    os.path.join(A		,      """src/transformers/models/bert/modeling_bert.py"""	)		,      os.path.join(self.transformer_dir		,      """models/bert/modeling_bert.py"""	)		,      )







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  """src/transformers"""
					shutil.rmtree(self.transformer_dir	)







		def 					__A   ( self		,      A		,      A		,      A		,      A=None	)  -> Dict:







					'''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(A		,      mode=A	)
					lowerCamelCase    		=  os.path.join(self.transformer_dir		,      """new_code.py"""	)
					with open(A		,      """w"""		,      newline="""\n"""	) as f:
								f.write(A	)
					if overwrite_result is None:
								self.assertTrue(len(check_copies.is_copy_consistent(A	)	) == 0	)
					else:
								check_copies.is_copy_consistent(f.name		,      overwrite=A	)
								with open(A		,      """r"""	) as f:
											self.assertTrue(f.read()		,      A	)







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







					'''simple docstring'''







					lowerCamelCase    		=  check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""	)
					self.assertEqual(A		,      A	)







		def 					__A   ( 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"""		,      A		,      )

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

					# 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"""		,      A		,      A	)		,      )

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







		def 					__A   ( self	)  -> Any:







					'''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(
					    A		,      A		,      localized_readme["""format_model_list"""]	)

					self.assertFalse(A	)
					self.assertEqual(A		,      A	)

					lowerCamelCase		,		lowerCamelCase    		=  check_copies.convert_to_localized_md(
					    A		,      A		,      localized_readme["""format_model_list"""]	)

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

					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(
					    A		,      A		,      localized_readme["""format_model_list"""]	)

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





import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

			return x * tf.tanh(tf.math.softplus(lowerCamelCase__      )      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)      ))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}'      )

