
'''simple docstring'''




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

import black


lowercase			:				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.
lowercase			:				Optional[Any]    		=			'    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  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Any					=						tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir						,			'''models/bert/'''	)	)
         A       :   Optional[Any]					=						self.transformer_dir
         shutil.copy(
             os.path.join(__SCREAMING_SNAKE_CASE						,			'''src/transformers/models/bert/modeling_bert.py'''	)						,			os.path.join(self.transformer_dir						,			'''models/bert/modeling_bert.py'''	)						,			)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   List[Any]					=						'''src/transformers'''
         shutil.rmtree(self.transformer_dir	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				Dict:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''	)
         self.assertEqual(__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """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'''						,			__SCREAMING_SNAKE_CASE						,			)

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

         # Copy consistency with a really long name
         A       :   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'''						,			__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE	)						,			)

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





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






         """simple docstring"""






         A       :   Union[str, Any]					=						check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

         A       :   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.\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.'''
         )
         A       :   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'''
         )
         A       :   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'''
         )

         A,  A       :   Any					=						check_copies.convert_to_localized_md(
             __SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

         self.assertFalse(__SCREAMING_SNAKE_CASE	)
         self.assertEqual(__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE	)

         A,  A       :   List[Any]					=						check_copies.convert_to_localized_md(
             __SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

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

         A       :   Union[str, 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.'''
         )
         A       :   Dict					=						(
             '''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'''
         )
         A       :   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'''
         )

         A,  A       :   Any					=						check_copies.convert_to_localized_md(
             __SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

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







'''simple docstring'''



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

UpperCAmelCase :       Tuple			   =     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__=False      ,    a__=True						):




     """simple docstring"""


     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      MODEL_CLASSES[model_type]

     # Initialise TF model
     if config_file in aws_config_map:
          __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
     __SCREAMING_SNAKE_CASE     =      config_class.from_json_file(a__						)
     __SCREAMING_SNAKE_CASE     =      True
     __SCREAMING_SNAKE_CASE     =      True
     print(F'Building TensorFlow model from configuration: {config}'						)
     __SCREAMING_SNAKE_CASE     =      model_class(a__						)

     # Load weights from tf checkpoint
     if pytorch_checkpoint_path in aws_config_map.keys():
          __SCREAMING_SNAKE_CASE     =      cached_file(
              a__      ,    a__      ,    force_download=not use_cached_models						)
     # Load PyTorch checkpoint in tf2 model:
     __SCREAMING_SNAKE_CASE     =      load_pytorch_checkpoint_in_tfa_model(a__      ,    a__						)

     if compare_with_pt_model:
          __SCREAMING_SNAKE_CASE     =      tf_model(tf_model.dummy_inputs      ,    training=a__						)  # build the network

          __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location="""cpu"""						)
          __SCREAMING_SNAKE_CASE     =      pt_model_class.from_pretrained(
              pretrained_model_name_or_path=a__      ,    config=a__      ,    state_dict=a__						)

          with torch.no_grad():
               __SCREAMING_SNAKE_CASE     =      pt_model(**pt_model.dummy_inputs						)

          __SCREAMING_SNAKE_CASE     =      pto[0].numpy()
          __SCREAMING_SNAKE_CASE     =      tfo[0].numpy()
          __SCREAMING_SNAKE_CASE     =      np.amax(np.abs(np_pt - np_tf						)						)
          print(F'Max absolute difference between models outputs {diff}'						)
          assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

     # Save pytorch-model
     print(F'Save TensorFlow model to {tf_dump_path}'						)
     tf_model.save_weights(a__      ,    save_format="""h5"""						)







def    a__       (  a__      ,    a__      ,    a__=None      ,    a__=None      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=False      ,    ):




     """simple docstring"""


     if args_model_type is None:
          __SCREAMING_SNAKE_CASE     =      list(MODEL_CLASSES.keys()						)
     else:
          __SCREAMING_SNAKE_CASE     =      [args_model_type]

     for j, model_type in enumerate(a__      ,    start=1						):
          print("""=""" * 1_00						)
          print(F' Converting model type {j}/{len(a__						)}: {model_type}'						)
          print("""=""" * 1_00						)
          if model_type not in MODEL_CLASSES:
               raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()						)}.'						)

          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      MODEL_CLASSES[model_type]

          if model_shortcut_names_or_path is None:
               __SCREAMING_SNAKE_CASE     =      list(aws_model_maps.keys()						)
          if config_shortcut_names_or_path is None:
               __SCREAMING_SNAKE_CASE     =      model_shortcut_names_or_path

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(a__      ,    a__						)      ,    start=1						):
               print("""-""" * 1_00						)
               if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                    if not only_convert_finetuned_models:
                         print(F'    Skipping finetuned checkpoint {model_shortcut_name}'						)
                         continue
                    __SCREAMING_SNAKE_CASE     =      model_shortcut_name
               elif only_convert_finetuned_models:
                    print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'						)
                    continue
               print(
                   F'    Converting checkpoint {i}/{len(a__						)}: {model_shortcut_name} - model_type {model_type}'						)
               print("""-""" * 1_00						)

               if config_shortcut_name in aws_config_map:
                    __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
               else:
                    __SCREAMING_SNAKE_CASE     =      config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    __SCREAMING_SNAKE_CASE     =      cached_file(a__      ,    a__      ,    force_download=not use_cached_models						)
               else:
                    __SCREAMING_SNAKE_CASE     =      model_shortcut_name

               if os.path.isfile(a__						):
                    __SCREAMING_SNAKE_CASE     =      """converted_model"""

               convert_pt_checkpoint_to_tf(
                   model_type=a__      ,    pytorch_checkpoint_path=a__      ,    config_file=a__      ,    tf_dump_path=os.path.join(a__      ,    model_shortcut_name + """-tf_model.h5"""						)      ,    compare_with_pt_model=a__      ,    )
               if remove_cached_files:
                    os.remove(a__						)
                    os.remove(a__						)


if __name__ == "__main__":
  UpperCAmelCase :       List[str]			   =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
  )
  parser.add_argument(
      '--model_type',
      default=None,
      type=str,
      help=(
          f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
          'convert all the models from AWS.'
      ),
  )
  parser.add_argument(
      '--pytorch_checkpoint_path',
      default=None,
      type=str,
      help=(
          'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
          'If not given, will download and convert all the checkpoints from AWS.'
      ),
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      help=(
          'The config json file corresponding to the pre-trained model. \n'
          'This specifies the model architecture. If not given and '
          '--pytorch_checkpoint_path is not given or is a shortcut name '
          'use the configuration associated to the shortcut name on the AWS'
      ),
  )
  parser.add_argument(
      '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
  )
  parser.add_argument(
      '--use_cached_models',
      action='store_true',
      help='Use cached models if possible instead of updating to latest checkpoint versions.',
  )
  parser.add_argument(
      '--remove_cached_files',
      action='store_true',
      help='Remove pytorch models after conversion (save memory when converting in batches).',
  )
  parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
  UpperCAmelCase :       List[Any]			   =     parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )






