'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__snake_case						  =		{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  for attribute in key.split("""."""						):
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  if weight_type is not None:
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						).shape
  else:
    lowercase_     =       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":
    lowercase_     =       value
  elif weight_type == "weight_g":
    lowercase_     =       value
  elif weight_type == "weight_v":
    lowercase_     =       value
  elif weight_type == "bias":
    lowercase_     =       value
  else:
    lowercase_     =       value

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       []
  lowercase_     =       fairseq_model.state_dict()

  lowercase_     =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowercase_     =       False
    if "conv_layers" in name:
      load_conv_layer(
          SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	hf_model.config.feat_extract_norm == """group""" ,	)
      lowercase_     =       True
    else:
      for key, mapped_key in MAPPING.items():
        lowercase_     =       """sew.""" + 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]:
          lowercase_     =       True
          if "*" in mapped_key:
            lowercase_     =       name.split(SCREAMING_SNAKE_CASE_						)[0].split("""."""						)[-2]
            lowercase_     =       mapped_key.replace("""*""" ,	SCREAMING_SNAKE_CASE_						)
          if "weight_g" in name:
            lowercase_     =       """weight_g"""
          elif "weight_v" in name:
            lowercase_     =       """weight_v"""
          elif "weight" in name:
            lowercase_     =       """weight"""
          elif "bias" in name:
            lowercase_     =       """bias"""
          else:
            lowercase_     =       None
          set_recursively(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
        continue
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE_						)

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       full_name.split("""conv_layers."""						)[-1]
  lowercase_     =       name.split("""."""						)
  lowercase_     =       int(items[0]						)
  lowercase_     =       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."""
      )
      lowercase_     =       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."""
      )
      lowercase_     =       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."
      )
      lowercase_     =       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."""
      )
      lowercase_     =       value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
  else:
    unused_weights.append(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       SEWConfig()
  if is_finetuned:
    lowercase_     =       model.wav_encoder.wav_model.cfg
  else:
    lowercase_     =       model.cfg

  lowercase_     =       fs_config.conv_bias
  lowercase_     =       eval(fs_config.conv_feature_layers						)
  lowercase_     =       [x[0] for x in conv_layers]
  lowercase_     =       [x[1] for x in conv_layers]
  lowercase_     =       [x[2] for x in conv_layers]
  lowercase_     =       """gelu"""
  lowercase_     =       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  lowercase_     =       0.0
  lowercase_     =       fs_config.activation_fn.name
  lowercase_     =       fs_config.encoder_embed_dim
  lowercase_     =       0.02
  lowercase_     =       fs_config.encoder_ffn_embed_dim
  lowercase_     =       1e-5
  lowercase_     =       fs_config.encoder_layerdrop
  lowercase_     =       fs_config.encoder_attention_heads
  lowercase_     =       fs_config.conv_pos_groups
  lowercase_     =       fs_config.conv_pos
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       fs_config.encoder_layers
  lowercase_     =       fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    lowercase_     =       model.cfg
    lowercase_     =       fs_config.final_dropout
    lowercase_     =       fs_config.layerdrop
  lowercase_     =       fs_config.activation_dropout
  lowercase_     =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  lowercase_     =       fs_config.attention_dropout
  lowercase_     =       fs_config.dropout_input
  lowercase_     =       fs_config.dropout
  lowercase_     =       fs_config.mask_channel_length
  lowercase_     =       fs_config.mask_channel_prob
  lowercase_     =       fs_config.mask_length
  lowercase_     =       fs_config.mask_prob

  lowercase_     =       """Wav2Vec2FeatureExtractor"""
  lowercase_     =       """Wav2Vec2CTCTokenizer"""

  return config

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=True						)			->Optional[Any]:

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

  if config_path is not None:
    lowercase_     =       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       convert_config(model[0] ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       model[0].eval()

  lowercase_     =       True if config.feat_extract_norm == """layer""" else False
  lowercase_     =       WavaVecaFeatureExtractor(
      feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	)

  if is_finetuned:
    if dict_path:
      lowercase_     =       Dictionary.load(SCREAMING_SNAKE_CASE_						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowercase_     =       target_dict.pad_index
      lowercase_     =       target_dict.bos_index
      lowercase_     =       target_dict.pad_index
      lowercase_     =       target_dict.bos_index
      lowercase_     =       target_dict.eos_index
      lowercase_     =       len(target_dict.symbols						)
      lowercase_     =       os.path.join(SCREAMING_SNAKE_CASE_ ,	"""vocab.json"""						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE_						):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE_						)						)
        return
      os.makedirs(SCREAMING_SNAKE_CASE_ ,	exist_ok=SCREAMING_SNAKE_CASE_						)
      with open(SCREAMING_SNAKE_CASE_ ,	"""w""" ,	encoding="""utf-8"""						) as vocab_handle:
        json.dump(target_dict.indices ,	SCREAMING_SNAKE_CASE_						)
      lowercase_     =       WavaVecaCTCTokenizer(
          SCREAMING_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=SCREAMING_SNAKE_CASE_ ,	)
      lowercase_     =       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,	tokenizer=SCREAMING_SNAKE_CASE_						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)

    lowercase_     =       SEWForCTC(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       SEWModel(SCREAMING_SNAKE_CASE_						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)

  recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  hf_model.save_pretrained(SCREAMING_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(
           """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
       )
       __snake_case						  =		parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )


'''simple docstring'''


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__)

