





"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

__A											=						{
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__A											=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[str]:





  """simple docstring"""


  lowerCAmelCase__     :Tuple											=				{}
  with open(_SCREAMING_SNAKE_CASE	,  'r'     ) as file:
    for line_number, line in enumerate(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :List[str]											=				line.strip()
      if line:
        lowerCAmelCase__     :int											=				line.split()
        lowerCAmelCase__     :Optional[int]											=				line_number
        lowerCAmelCase__     :Optional[Any]											=				words[0]
        lowerCAmelCase__     :Any											=				value
  return result
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  for attribute in key.split('.'     ):
    lowerCAmelCase__     :List[str]											=				getattr(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Optional[Any]											=				None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :str											=				PARAM_MAPPING[full_name.split('.'     )[-1]]
      lowerCAmelCase__     :Union[str, Any]											=				'param'

  if weight_type is not None and weight_type != "param":
    lowerCAmelCase__     :List[Any]											=				getattr(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).shape
  elif weight_type is not None and weight_type == "param":
    lowerCAmelCase__     :Any											=				hf_pointer
    for attribute in hf_param_name.split('.'     ):
      lowerCAmelCase__     :Dict											=				getattr(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[Any]											=				shape_pointer.shape

    # let's reduce dimension
    lowerCAmelCase__     :Dict											=				value[0]
  else:
    lowerCAmelCase__     :str											=				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__     :Optional[int]											=				value
  elif weight_type == "weight_g":
    lowerCAmelCase__     :List[str]											=				value
  elif weight_type == "weight_v":
    lowerCAmelCase__     :List[Any]											=				value
  elif weight_type == "bias":
    lowerCAmelCase__     :Optional[Any]											=				value
  elif weight_type == "param":
    for attribute in hf_param_name.split('.'     ):
      lowerCAmelCase__     :str											=				getattr(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :str											=				value
  else:
    lowerCAmelCase__     :int											=				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	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :str											=				PARAM_MAPPING[full_name.split('.'     )[-1]]
      lowerCAmelCase__     :List[str]											=				'param'

  if weight_type is not None and weight_type != "param":
    lowerCAmelCase__     :List[Any]											=				'.'.join([key, weight_type]     )
  elif weight_type is not None and weight_type == "param":
    lowerCAmelCase__     :Optional[int]											=				'.'.join([key, hf_param_name]     )
  else:
    lowerCAmelCase__     :Dict											=				key

  lowerCAmelCase__     :int											=				value if 'lm_head' in full_key else value[0]


__A											=						{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				False
  for key, mapped_key in MAPPING.items():
    lowerCAmelCase__     :Any											=				'wav2vec2.' + 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]:
      lowerCAmelCase__     :Dict											=				True
      if "*" in mapped_key:
        lowerCAmelCase__     :Any											=				name.split(_SCREAMING_SNAKE_CASE     )[0].split('.'     )[-2]
        lowerCAmelCase__     :Union[str, Any]											=				mapped_key.replace('*'	,  _SCREAMING_SNAKE_CASE     )
      if "weight_g" in name:
        lowerCAmelCase__     :Tuple											=				'weight_g'
      elif "weight_v" in name:
        lowerCAmelCase__     :Optional[int]											=				'weight_v'
      elif "bias" in name:
        lowerCAmelCase__     :Dict											=				'bias'
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        lowerCAmelCase__     :Optional[int]											=				'weight'
      else:
        lowerCAmelCase__     :List[str]											=				None
      if hf_dict is not None:
        rename_dict(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      else:
        set_recursively(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      return is_used
  return is_used
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				[]
  lowerCAmelCase__     :Any											=				fairseq_model.state_dict()

  lowerCAmelCase__     :Union[str, Any]											=				hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase__     :List[Any]											=				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'	,  )
      lowerCAmelCase__     :Any											=				True
    else:
      lowerCAmelCase__     :Optional[int]											=				load_wavaveca_layer(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    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     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				full_name.split('conv_layers.'     )[-1]
  lowerCAmelCase__     :Dict											=				name.split('.'     )
  lowerCAmelCase__     :List[str]											=				int(items[0]     )
  lowerCAmelCase__     :Any											=				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__     :List[str]											=				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__     :Optional[int]											=				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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."     )
      lowerCAmelCase__     :List[Any]											=				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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."     )
      lowerCAmelCase__     :List[Any]											=				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     )
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=True	,  _SCREAMING_SNAKE_CASE=False     )   ->Any:





  """simple docstring"""


  if config_path is not None:
    lowerCAmelCase__     :str											=				WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  else:
    lowerCAmelCase__     :Optional[int]											=				WavaVecaConfig()

  if is_seq_class:
    lowerCAmelCase__     :int											=				read_txt_into_dict(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Any											=				idalabel
    lowerCAmelCase__     :Optional[Any]											=				WavaVecaForSequenceClassification(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :List[Any]											=				WavaVecaFeatureExtractor(
        feature_size=1	,  sampling_rate=1_6000	,  padding_value=0	,  do_normalize=_SCREAMING_SNAKE_CASE	,  return_attention_mask=_SCREAMING_SNAKE_CASE	,  )
    feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE     )

  elif is_finetuned:
    if dict_path:
      lowerCAmelCase__     :List[Any]											=				Dictionary.load(_SCREAMING_SNAKE_CASE     )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase__     :str											=				target_dict.pad_index
      lowerCAmelCase__     :Union[str, Any]											=				target_dict.bos_index
      lowerCAmelCase__     :Any											=				target_dict.eos_index
      lowerCAmelCase__     :List[str]											=				len(target_dict.symbols     )
      lowerCAmelCase__     :List[Any]											=				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     )
      lowerCAmelCase__     :List[str]											=				target_dict.indices

      # fairseq has the <pad> and <s> switched
      lowerCAmelCase__     :Optional[Any]											=				0
      lowerCAmelCase__     :Tuple											=				1
      with open(_SCREAMING_SNAKE_CASE	,  'w'	,  encoding='utf-8'     ) as vocab_handle:
        json.dump(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :List[str]											=				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	,  )
      lowerCAmelCase__     :List[Any]											=				True if config.feat_extract_norm == 'layer' else False
      lowerCAmelCase__     :List[Any]											=				WavaVecaFeatureExtractor(
          feature_size=1	,  sampling_rate=1_6000	,  padding_value=0	,  do_normalize=_SCREAMING_SNAKE_CASE	,  return_attention_mask=_SCREAMING_SNAKE_CASE	,  )
      lowerCAmelCase__     :Tuple											=				WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE     )
      processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

    lowerCAmelCase__     :str											=				WavaVecaForCTC(_SCREAMING_SNAKE_CASE     )
  else:
    lowerCAmelCase__     :List[str]											=				WavaVecaForPreTraining(_SCREAMING_SNAKE_CASE     )

  if is_finetuned or is_seq_class:
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Optional[Any]											=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
  else:
    lowerCAmelCase__     :Tuple											=				argparse.Namespace(task='audio_pretraining'     )
    lowerCAmelCase__     :Tuple											=				fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE     )

    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Dict											=				model[0].eval()

  recursively_load_weights(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  not is_finetuned     )

  hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  __A											=						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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  __A											=						parser.parse_args()

  __A											=						not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )



'''simple docstring'''



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available



@dataclass
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[List[np.ndarray], torch.FloatTensor]


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipeline_text_to_video_synth import TextToVideoSDPipeline
     from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
     from .pipeline_text_to_video_zero import TextToVideoZeroPipeline





