





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

snake_case_           =      {
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def lowerCamelCase__   (							snake_case_     :  Optional[Any]	,				snake_case_     :  Optional[Any]	,				snake_case_     :  Dict	,				snake_case_     :  Optional[int]	,				snake_case_     :  Dict	)				->    Union[str, Any]:
   for attribute in key.split('''.'''	):
      __snake_case								=       getattr(snake_case_	,				snake_case_	)

   if weight_type is not None:
      __snake_case								=       getattr(snake_case_	,				snake_case_	).shape
   else:
      __snake_case								=       hf_pointer.shape

   assert hf_shape == value.shape, (
       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":
      __snake_case								=       value
   elif weight_type == "weight_g":
      __snake_case								=       value
   elif weight_type == "weight_v":
      __snake_case								=       value
   elif weight_type == "bias":
      __snake_case								=       value
   else:
      __snake_case								=       value

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






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  List[Any]	,				snake_case_     :  Optional[Any]	)				->    List[str]:
   __snake_case								=       []
   __snake_case								=       fairseq_model.state_dict()

   __snake_case								=       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case								=       False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	,				hf_model.config.feat_extract_norm == '''group'''	,				)
         __snake_case								=       True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case								=       '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

            if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
               __snake_case								=       True
               if "*" in mapped_key:
                  __snake_case								=       name.split(snake_case_	)[0].split('''.'''	)[-2]
                  __snake_case								=       mapped_key.replace('''*'''	,				snake_case_	)
               if "weight_g" in name:
                  __snake_case								=       '''weight_g'''
               elif "weight_v" in name:
                  __snake_case								=       '''weight_v'''
               elif "weight" in name:
                  __snake_case								=       '''weight'''
               elif "bias" in name:
                  __snake_case								=       '''bias'''
               else:
                  __snake_case								=       None
               set_recursively(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	)
            continue
      if not is_used:
         unused_weights.append(snake_case_	)

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






def lowerCamelCase__   (							snake_case_     :  Dict	,				snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  int	,				snake_case_     :  str	)				->    Optional[int]:
   __snake_case								=       full_name.split('''conv_layers.'''	)[-1]
   __snake_case								=       name.split('''.'''	)
   __snake_case								=       int(items[0]	)
   __snake_case								=       int(items[1]	)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         __snake_case								=       value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         __snake_case								=       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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         __snake_case								=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         __snake_case								=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
   else:
      unused_weights.append(snake_case_	)






@torch.no_grad()
def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  Any=None	,				snake_case_     :  Tuple=None	,				snake_case_     :  List[str]=True	)				->    Union[str, Any]:
   if config_path is not None:
      __snake_case								=       HubertConfig.from_pretrained(snake_case_	)
   else:
      __snake_case								=       HubertConfig()

   if is_finetuned:
      if dict_path:
         __snake_case								=       Dictionary.load(snake_case_	)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case								=       target_dict.pad_index
         __snake_case								=       target_dict.bos_index
         __snake_case								=       target_dict.eos_index
         __snake_case								=       len(target_dict.symbols	)
         __snake_case								=       os.path.join(snake_case_	,				'''vocab.json'''	)
         if not os.path.isdir(snake_case_	):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case_	)	)
            return
         os.makedirs(snake_case_	,				exist_ok=snake_case_	)
         with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as vocab_handle:
            json.dump(target_dict.indices	,				snake_case_	)
         __snake_case								=       WavaVecaCTCTokenizer(
             snake_case_	,				unk_token=target_dict.unk_word	,				pad_token=target_dict.pad_word	,				bos_token=target_dict.bos_word	,				eos_token=target_dict.eos_word	,				word_delimiter_token='''|'''	,				do_lower_case=snake_case_	,				)
         __snake_case								=       True if config.feat_extract_norm == '''layer''' else False
         __snake_case								=       WavaVecaFeatureExtractor(
             feature_size=1	,				sampling_rate=1_6000	,				padding_value=0	,				do_normalize=snake_case_	,				return_attention_mask=snake_case_	,				)
         __snake_case								=       WavaVecaProcessor(feature_extractor=snake_case_	,				tokenizer=snake_case_	)
         processor.save_pretrained(snake_case_	)

      __snake_case								=       HubertForCTC(snake_case_	)
   else:
      __snake_case								=       HubertModel(snake_case_	)

   if is_finetuned:
      __snake_case	,     __snake_case	,     __snake_case								=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
   else:
      __snake_case	,     __snake_case	,     __snake_case								=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

   __snake_case								=       model[0].eval()

   recursively_load_weights(snake_case_	,				snake_case_	,				snake_case_	)

   hf_wavavec.save_pretrained(snake_case_	)


if __name__ == "__main__":
    snake_case_           =      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'
    )
    snake_case_           =      parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_           =      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

