
"""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()

_a      :			Optional[Any]					=							{
    """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   :				List[str]       ,     a   :				Any       ,     a   :				Optional[Any]       ,     a   :				Tuple       ,     a   :				Union[str, Any]=False       ,     a   :				List[str]=True		):

  """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     :						Tuple						=   MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    _snake_case     :						List[str]						=   cached_file(a       ,     a       ,     force_download=not use_cached_models		)
  _snake_case     :						str						=   config_class.from_json_file(a		)
  _snake_case     :						Optional[int]						=   True
  _snake_case     :						Any						=   True
  print(f'Building TensorFlow model from configuration: {config}'		)
  _snake_case     :						Union[str, Any]						=   model_class(a		)

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    _snake_case     :						List[Any]						=   cached_file(
        a       ,     a       ,     force_download=not use_cached_models		)
  # Load PyTorch checkpoint in tf2 model:
  _snake_case     :						str						=   load_pytorch_checkpoint_in_tfa_model(a       ,     a		)

  if compare_with_pt_model:
    _snake_case     :						List[str]						=   tf_model(tf_model.dummy_inputs       ,     training=a		)  # build the network

    _snake_case     :						Dict						=   torch.load(a       ,     map_location="cpu"		)
    _snake_case     :						str						=   pt_model_class.from_pretrained(
        pretrained_model_name_or_path=a       ,     config=a       ,     state_dict=a		)

    with torch.no_grad():
      _snake_case     :						Optional[int]						=   pt_model(**pt_model.dummy_inputs		)

    _snake_case     :						Tuple						=   pto[0].numpy()
    _snake_case     :						Optional[int]						=   tfo[0].numpy()
    _snake_case     :						List[Any]						=   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   :				Dict       ,     a   :				Optional[Any]       ,     a   :				str=None       ,     a   :				Any=None       ,     a   :				Optional[Any]=False       ,     a   :				List[str]=False       ,     a   :				Union[str, Any]=False       ,     a   :				List[Any]=False       ,     ):

  """simple docstring"""







  if args_model_type is None:
    _snake_case     :						Union[str, Any]						=   list(MODEL_CLASSES.keys()		)
  else:
    _snake_case     :						Tuple						=   [args_model_type]

  for j, model_type in enumerate(a       ,     start=1		):
    print("=" * 100		)
    print(f' Converting model type {j}/{len(a		)}: {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     :						Union[str, Any]						=   MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      _snake_case     :						Union[str, Any]						=   list(aws_model_maps.keys()		)
    if config_shortcut_names_or_path is None:
      _snake_case     :						Dict						=   model_shortcut_names_or_path

    for i, (model_shortcut_name, config_shortcut_name) in enumerate(
        zip(a       ,     a		)       ,     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     :						str						=   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("-" * 100		)

      if config_shortcut_name in aws_config_map:
        _snake_case     :						int						=   cached_file(a       ,     a       ,     force_download=not use_cached_models		)
      else:
        _snake_case     :						Dict						=   config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        _snake_case     :						List[Any]						=   cached_file(a       ,     a       ,     force_download=not use_cached_models		)
      else:
        _snake_case     :						Any						=   model_shortcut_name

      if os.path.isfile(a		):
        _snake_case     :						Optional[Any]						=   "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__":
    _a      :			int					=							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      :			Dict					=							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,
    )





"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""EncodecFeatureExtractor"""
    __lowercase							: str  =					("""T5Tokenizer""", """T5TokenizerFast""")
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      super().__init__(snake_case_						,    snake_case_     )
      _snake_case     :						Dict						=   self.feature_extractor
      _snake_case     :						Any						=   False
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True     ):
      return self.tokenizer.get_decoder_prompt_ids(task=snake_case_						,    language=snake_case_						,    no_timestamps=snake_case_     )
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*snake_case_						,    **snake_case_     )

      _snake_case     :						str						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						Optional[int]						=   kwargs.pop("sampling_rate"						,    snake_case_     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("text"						,    snake_case_     )
      if len(snake_case_     ) > 0:
        _snake_case     :						Any						=   args[0]
        _snake_case     :						Union[str, Any]						=   args[1:]

      if audio is None and text is None:
        raise ValueError("You need to specify either an `audio` or `text` input to process."     )

      if text is not None:
        _snake_case     :						Any						=   self.tokenizer(snake_case_						,    **snake_case_     )

      if audio is not None:
        _snake_case     :						Any						=   self.feature_extractor(snake_case_						,    *snake_case_						,    sampling_rate=snake_case_						,    **snake_case_     )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        _snake_case     :						str						=   audio_inputs["input_values"]
        if "padding_mask" in audio_inputs:
          _snake_case     :						List[str]						=   audio_inputs["padding_mask"]
        return inputs
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						Tuple						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						List[str]						=   kwargs.pop("padding_mask"						,    snake_case_     )

      if len(snake_case_     ) > 0:
        _snake_case     :						Tuple						=   args[0]
        _snake_case     :						Dict						=   args[1:]

      if audio_values is not None:
        return self._decode_audio(snake_case_						,    padding_mask=snake_case_     )
      else:
        return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )
      _snake_case			,					_snake_case			,					_snake_case     :						Tuple						=   audio_values.shape

      if padding_mask is None:
        return list(snake_case_     )

      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _snake_case     :						Any						=   seq_len - padding_mask.shape[-1]
      _snake_case     :						Optional[Any]						=   1 - self.feature_extractor.padding_value
      _snake_case     :						Optional[int]						=   np.pad(snake_case_						,    ((0, 0), (0, difference))						,    "constant"						,    constant_values=snake_case_     )

      _snake_case     :						Any						=   audio_values.tolist()
      for i in range(snake_case_     ):
        _snake_case     :						Tuple						=   np.asarray(audio_values[i]     )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        _snake_case     :						Tuple						=   sliced_audio.reshape(snake_case_						,    -1     )

      return audio_values

