




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()

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



       """simple docstring"""

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

       A__					,				A__					,				A__					,				A__ 					=       MODEL_CLASSES[model_type]

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

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

       if compare_with_pt_model:
              A__ 					=       tf_model(tf_model.dummy_inputs					,							training=lowercase_		)  # build the network

              A__ 					=       torch.load(lowercase_					,							map_location='''cpu'''		)
              A__ 					=       pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowercase_					,							config=lowercase_					,							state_dict=lowercase_		)

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

              A__ 					=       pto[0].numpy()
              A__ 					=       tfo[0].numpy()
              A__ 					=       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(lowercase_					,							save_format='''h5'''		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							lowercase_=False					,							lowercase_=False					,							lowercase_=False					,							lowercase_=False					,							)  ->			int:



       """simple docstring"""

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

       for j, model_type in enumerate(lowercase_					,							start=1		):
              print('''=''' * 100		)
              print(f""" Converting model type {j}/{len(lowercase_		)}: {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()		)}."""		)

              A__					,				A__					,				A__					,				A__					,				A__ 					=       MODEL_CLASSES[model_type]

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

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowercase_					,							lowercase_		)					,							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
                            A__ 					=       model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""		)
                            continue
                     print(
                         f"""    Converting checkpoint {i}/{len(lowercase_		)}: {model_shortcut_name} - model_type {model_type}"""		)
                     print('''-''' * 100		)

                     if config_shortcut_name in aws_config_map:
                            A__ 					=       cached_file(lowercase_					,							lowercase_					,							force_download=not use_cached_models		)
                     else:
                            A__ 					=       config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            A__ 					=       cached_file(lowercase_					,							lowercase_					,							force_download=not use_cached_models		)
                     else:
                            A__ 					=       model_shortcut_name

                     if os.path.isfile(lowercase_		):
                            A__ 					=       '''converted_model'''

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


if __name__ == "__main__":
  _lowerCamelCase      :						Union[str, Any]							=  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.""")
  _lowerCamelCase      :						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,
  )








def 						__A						(      ) ->			list[list[int]]:
	return [list(range(1000 - i				,     -1000 - i				,     -1       )       ) for i in range(1000       )]


__UpperCamelCase   :			str          = generate_large_matrix()
__UpperCamelCase   :			Tuple          = (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 						__A						(      __lowerCamelCase       ) ->			None:
	assert all(row == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for row in grid       )
	assert all(list(__lowerCamelCase       ) == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for col in zip(*__lowerCamelCase       )       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(__lowerCamelCase       ) - 1

	# Edge cases such as no values or all numbers are negative.
	if not array or array[0] < 0:
		return 0

	while right + 1 > left:
		a									=				(left + right) // 2
		a									=				array[mid]

		# Num must be negative and the index must be greater than or equal to 0.
		if num < 0 and array[mid - 1] >= 0:
			return mid

		if num >= 0:
			a									=				mid + 1
		else:
			a									=				mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
	return len(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(grid[0]       )

	for i in range(len(__lowerCamelCase       )       ):
		a									=				find_negative_index(grid[i][:bound]       )
		total += bound
	return (len(__lowerCamelCase       ) * len(grid[0]       )) - total





def 						__A						(      __lowerCamelCase       ) ->			int:
	return len([number for row in grid for number in row if number < 0]       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	for row in grid:
		for i, number in enumerate(__lowerCamelCase       ):
			if number < 0:
				total += len(__lowerCamelCase       ) - i
				break
	return total





def 						__A						(      ) ->			None:
	from timeit import timeit

	print("""Running benchmarks"""       )
	a									=				(
	    """from __main__ import count_negatives_binary_search, """
	    """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
	)
	for func in (
	    "count_negatives_binary_search",  # took 0.7727 seconds
	    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
	    "count_negatives_brute_force",  # took 12.8160 seconds
	):
		a									=				timeit(f'{func}(grid=grid)'				,     setup=__lowerCamelCase				,     number=500       )
		print(f'{func}() took {time:0.4f} seconds'       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()

