


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      =   {
    """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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True )->  List[str]:




      """simple docstring"""

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

      snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				MODEL_CLASSES[model_type]

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

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

      if compare_with_pt_model:
            snake_case_	=				tf_model(tf_model.dummy_inputs , training=SCREAMING_SNAKE_CASE )  # build the network

            snake_case_	=				torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
            snake_case_	=				pt_model_class.from_pretrained(
                pretrained_model_name_or_path=SCREAMING_SNAKE_CASE , config=SCREAMING_SNAKE_CASE , state_dict=SCREAMING_SNAKE_CASE )

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

            snake_case_	=				pto[0].numpy()
            snake_case_	=				tfo[0].numpy()
            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(SCREAMING_SNAKE_CASE , save_format='''h5''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , )->  str:




      """simple docstring"""

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

      for j, model_type in enumerate(SCREAMING_SNAKE_CASE , start=1 ):
            print('''=''' * 100 )
            print(f''' Converting model type {j}/{len(SCREAMING_SNAKE_CASE )}: {model_type}''' )
            print('''=''' * 100 )
            if model_type not in MODEL_CLASSES:
                  raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' )

            snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				MODEL_CLASSES[model_type]

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

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , start=1 ):
                  print('''-''' * 100 )
                  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
                        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(SCREAMING_SNAKE_CASE )}: {model_shortcut_name} - model_type {model_type}''' )
                  print('''-''' * 100 )

                  if config_shortcut_name in aws_config_map:
                        snake_case_	=				cached_file(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , force_download=not use_cached_models )
                  else:
                        snake_case_	=				config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        snake_case_	=				cached_file(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , force_download=not use_cached_models )
                  else:
                        snake_case_	=				model_shortcut_name

                  if os.path.isfile(SCREAMING_SNAKE_CASE ):
                        snake_case_	=				'''converted_model'''

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


if __name__ == "__main__":
   UpperCAmelCase      =   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      =   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,
   )



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''a''', '''b''', '''c''']

       # Defaults to last layer if both are None
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[2] )

       # Out indices set to match out features
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(['''a''', '''c''']		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features set to match out indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[0, 2]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features selected from negative indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[-3, -1]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[-3, -1] )



 def 						UpperCamelCase__					(     self ):
       # Stage names must be set
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				_UpperCAmelCase )

       # Out features must be a list
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(('''a''', '''b''')		,				(0, 1)		,				['''a''', '''b'''] )

       # Out features must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				['''a'''] )

       # Out indices must be a list or tuple
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				0		,				['''a''', '''b'''] )

       # Out indices must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				(0, 1)		,				['''a'''] )

       # Out features and out indices must be the same length
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0,)		,				['''a''', '''b''', '''c'''] )

       # Out features should match out indices
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 2)		,				['''a''', '''b''', '''c'''] )

       # Out features and out indices should be in order
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''b''', '''a''']		,				(0, 1)		,				['''a''', '''b'''] )

       # Check passes with valid inputs
       verify_out_features_out_indices(['''a''', '''b''', '''d''']		,				(0, 1, -1)		,				['''a''', '''b''', '''c''', '''d'''] )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BackboneMixin()

       snake_case_	=				['''a''', '''b''', '''c''']
       snake_case_	=				['''a''', '''c''']
       snake_case_	=				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[0, 2] )

       # Check out features and indices are updated correctly
       snake_case_	=				['''a''', '''b''']
       self.assertEqual(backbone.out_features		,				['''a''', '''b'''] )
       self.assertEqual(backbone.out_indices		,				[0, 1] )

       snake_case_	=				[-3, -1]
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[-3, -1] )