


'''simple docstring'''






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

import black


__A						:						List[str]  =  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.
__A						:						Tuple  =  '    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     __UpperCamelCase						(    unittest.TestCase				):





							def 		a__ (	self		:Optional[int]      ):
									snake_case_		:				Optional[Any]      							=    tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir	,"""models/bert/"""      )      )
									snake_case_		:				int      							=    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		:Optional[Any]      ):
									snake_case_		:				Tuple      							=    "src/transformers"
									shutil.rmtree(self.transformer_dir      )





							def 		a__ (	self		:Any	,_UpperCamelCase		:Tuple	,_UpperCamelCase		:Optional[int]	,_UpperCamelCase		:Union[str, Any]	,_UpperCamelCase		:str=None      ):
									snake_case_		:				Optional[int]      							=    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
									if overwrite_result is not None:
											snake_case_		:				int      							=    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
									snake_case_		:				Optional[int]      							=    black.Mode(target_versions={black.TargetVersion.PYaa}	,line_length=1_1_9      )
									snake_case_		:				Tuple      							=    black.format_str(__a	,mode=__a      )
									snake_case_		:				List[str]      							=    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		:Optional[int]      ):
									snake_case_		:				str      							=    check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""      )
									self.assertEqual(__a	,__a      )





							def 		a__ (	self		:List[Any]      ):
									# 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"""	,__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
									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 		a__ (	self		:Dict      ):
									snake_case_		:				Tuple      							=    check_copies.LOCALIZED_READMES["README_zh-hans.md"]

									snake_case_		:				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.\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_		:				int      							=    (
									    "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_		:				List[Any]      							=    check_copies.convert_to_localized_md(
									    __a	,__a	,localized_readme["""format_model_list"""]      )

									self.assertFalse(__a      )
									self.assertEqual(__a	,__a      )

									snake_case_		:				Optional[int]      							=    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      )

									snake_case_		:				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_		:				List[str]      							=    (
									    "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_		:				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_		:				Dict      							=    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 argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
