"""simple docstring"""





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  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir      ,    """models/bert/"""))
											_SCREAMING_SNAKE_CASE		:						Any			  =    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 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    """src/transformers"""
											shutil.rmtree(self.transformer_dir)





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int      ,    _A							:							Optional[int]      ,    _A							:							Any      ,    _A							:							Tuple=None):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
											if overwrite_result is not None:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    black.Mode(target_versions={black.TargetVersion.PYaa}      ,    line_length=1_1_9)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    black.format_str(_A      ,    mode=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead""")
											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Optional[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"""      ,    _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
											_SCREAMING_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"""      ,    _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 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

											_SCREAMING_SNAKE_CASE		:						Dict			  =    (
											    """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[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"""
											)
											_SCREAMING_SNAKE_CASE		:						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"""
											)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    check_copies.convert_to_localized_md(
											    _A      ,    _A      ,    localized_readme["""format_model_list"""])

											self.assertFalse(_A)
											self.assertEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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)

											_SCREAMING_SNAKE_CASE		:						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		:						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"""
											)
											_SCREAMING_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。\n"""
											)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    check_copies.convert_to_localized_md(
											    _A      ,    _A      ,    localized_readme["""format_model_list"""])

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



"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs


