'''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												={
    "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			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    ,  UpperCamelCase__=True    )     ->   List[str]:
    if model_type not in MODEL_CLASSES:
        raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}."""    )

    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  MODEL_CLASSES[model_type]

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

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

    if compare_with_pt_model:
        __lowerCamelCase          =  tf_model(tf_model.dummy_inputs    ,  training=UpperCamelCase__    )  # build the network

        __lowerCamelCase          =  torch.load(UpperCamelCase__    ,  map_location='''cpu'''    )
        __lowerCamelCase          =  pt_model_class.from_pretrained(
            pretrained_model_name_or_path=UpperCamelCase__    ,  config=UpperCamelCase__    ,  state_dict=UpperCamelCase__    )

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

        __lowerCamelCase          =  pto[0].numpy()
        __lowerCamelCase          =  tfo[0].numpy()
        __lowerCamelCase          =  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(UpperCamelCase__    ,  save_format='''h5'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=False    ,  UpperCamelCase__=False    ,  UpperCamelCase__=False    ,  UpperCamelCase__=False    ,  )     ->   Union[str, Any]:
    if args_model_type is None:
        __lowerCamelCase          =  list(MODEL_CLASSES.keys()    )
    else:
        __lowerCamelCase          =  [args_model_type]

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

        __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  MODEL_CLASSES[model_type]

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

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(UpperCamelCase__    ,  UpperCamelCase__    )    ,  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
                __lowerCamelCase          =  model_shortcut_name
            elif only_convert_finetuned_models:
                print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""    )
                continue
            print(
                f"""    Converting checkpoint {i}/{len(UpperCamelCase__    )}: {model_shortcut_name} - model_type {model_type}"""    )
            print('''-''' * 1_00    )

            if config_shortcut_name in aws_config_map:
                __lowerCamelCase          =  cached_file(UpperCamelCase__    ,  UpperCamelCase__    ,  force_download=not use_cached_models    )
            else:
                __lowerCamelCase          =  config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                __lowerCamelCase          =  cached_file(UpperCamelCase__    ,  UpperCamelCase__    ,  force_download=not use_cached_models    )
            else:
                __lowerCamelCase          =  model_shortcut_name

            if os.path.isfile(UpperCamelCase__    ):
                __lowerCamelCase          =  '''converted_model'''

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


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,
  )

'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__UpperCAmelCase												=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    for attribute in key.split('''.'''    ):
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    )

    if weight_type is not None:
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    ).shape
    else:
        __lowerCamelCase          =  hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
            f""" {value.shape} for {full_name}"""    )

    if weight_type == "weight":
        __lowerCamelCase          =  value
    elif weight_type == "weight_g":
        __lowerCamelCase          =  value
    elif weight_type == "weight_v":
        __lowerCamelCase          =  value
    elif weight_type == "bias":
        __lowerCamelCase          =  value
    else:
        __lowerCamelCase          =  value

    logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  fairseq_model.state_dict()

    __lowerCamelCase          =  hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        __lowerCamelCase          =  False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  hf_model.config.feat_extract_norm == '''group'''    ,  )
            __lowerCamelCase          =  True
        else:
            for key, mapped_key in MAPPING.items():
                __lowerCamelCase          =  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''    )[:-1]    ) != key):
                        # special case since naming is very similar
                        continue
                    __lowerCamelCase          =  True
                    if "*" in mapped_key:
                        __lowerCamelCase          =  name.split(UpperCamelCase__    )[0].split('''.'''    )[-2]
                        __lowerCamelCase          =  mapped_key.replace('''*'''    ,  UpperCamelCase__    )
                    if "weight_g" in name:
                        __lowerCamelCase          =  '''weight_g'''
                    elif "weight_v" in name:
                        __lowerCamelCase          =  '''weight_v'''
                    elif "bias" in name:
                        __lowerCamelCase          =  '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __lowerCamelCase          =  '''weight'''
                    else:
                        __lowerCamelCase          =  None
                    set_recursively(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase__    )

    logger.warning(f"""Unused weights: {unused_weights}"""    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  full_name.split('''conv_layers.'''    )[-1]
    __lowerCamelCase          =  name.split('''.'''    )
    __lowerCamelCase          =  int(items[0]    )
    __lowerCamelCase          =  int(items[1]    )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
    else:
        unused_weights.append(UpperCamelCase__    )






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=True    )     ->   Optional[Any]:
    if config_path is not None:
        __lowerCamelCase          =  UniSpeechSatConfig.from_pretrained(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatConfig()

    __lowerCamelCase          =  ''''''

    if is_finetuned:
        __lowerCamelCase          =  UniSpeechSatForCTC(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatForPreTraining(UpperCamelCase__    )

    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
    __lowerCamelCase          =  model[0].eval()

    recursively_load_weights(UpperCamelCase__    ,  UpperCamelCase__    )

    hf_wavavec.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __UpperCAmelCase												=parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )
