


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()

_A		       =						{
    '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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					str=False			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=True				):
     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}.'				)

     __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          __UpperCamelCase    							=tf_model(tf_model.dummy_inputs			,     training=SCREAMING_SNAKE_CASE__				)  # build the network

          __UpperCamelCase    							=torch.load(SCREAMING_SNAKE_CASE__			,     map_location='cpu'				)
          __UpperCamelCase    							=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():
               __UpperCamelCase    							=pt_model(**pt_model.dummy_inputs				)

          __UpperCamelCase    							=pto[0].numpy()
          __UpperCamelCase    							=tfo[0].numpy()
          __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]=None			,     SCREAMING_SNAKE_CASE__			:					Dict=None			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					Any=False			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					str=False			,     ):
     if args_model_type is None:
          __UpperCamelCase    							=list(MODEL_CLASSES.keys()				)
     else:
          __UpperCamelCase    							=[args_model_type]

     for j, model_type in enumerate(SCREAMING_SNAKE_CASE__			,     start=1				):
          print('=' * 1_00				)
          print(F' Converting model type {j}/{len(SCREAMING_SNAKE_CASE__				)}: {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()				)}.'				)

          __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=MODEL_CLASSES[model_type]

          if model_shortcut_names_or_path is None:
               __UpperCamelCase    							=list(aws_model_maps.keys()				)
          if config_shortcut_names_or_path is None:
               __UpperCamelCase    							=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('-' * 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
                    __UpperCamelCase    							=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('-' * 1_00				)

               if config_shortcut_name in aws_config_map:
                    __UpperCamelCase    							=cached_file(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     force_download=not use_cached_models				)
               else:
                    __UpperCamelCase    							=config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    __UpperCamelCase    							=cached_file(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     force_download=not use_cached_models				)
               else:
                    __UpperCamelCase    							=model_shortcut_name

               if os.path.isfile(SCREAMING_SNAKE_CASE__				):
                    __UpperCamelCase    							='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__":
   _A		       =						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.')
   _A		       =						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,
   )









_A		       =						{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_A		       =						{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[]

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     order.append(SCREAMING_SNAKE_CASE__				)

     return order

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[vert]

     for neighbour in reversed_graph[vert]:
          if not visited[neighbour]:
               component += find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     return component





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]				):
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]
     __UpperCamelCase    							={vert: [] for vert in range(len(SCREAMING_SNAKE_CASE__				)				)}

     for vert, neighbours in graph.items():
          for neighbour in neighbours:
               reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     for i, was_visited in enumerate(SCREAMING_SNAKE_CASE__				):
          if not was_visited:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]

     for i in range(len(SCREAMING_SNAKE_CASE__				)				):
          __UpperCamelCase    							=order[len(SCREAMING_SNAKE_CASE__				) - i - 1]
          if not visited[vert]:
               __UpperCamelCase    							=find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
               components_list.append(SCREAMING_SNAKE_CASE__				)

     return components_list





