






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()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Any     						=						{
    """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""",
}
__UpperCamelCase      :      Optional[int]     						=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]





def 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  {}
 with open(lowerCamelCase				,  """r"""					) as file:
  for line_number, line in enumerate(lowerCamelCase					):
   __lowercase             =  line.strip()
   if line:
    __lowercase             =  line.split()
    __lowercase             =  line_number
    __lowercase             =  words[0]
    __lowercase             =  value
 return result





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 for attribute in key.split("""."""					):
  __lowercase             =  getattr(lowerCamelCase				,  lowerCamelCase					)

 __lowercase             =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(lowerCamelCase					):
   __lowercase             =  PARAM_MAPPING[full_name.split("""."""					)[-1]]
   __lowercase             =  "param"

 if weight_type is not None and weight_type != "param":
  __lowercase             =  getattr(lowerCamelCase				,  lowerCamelCase					).shape
 elif weight_type is not None and weight_type == "param":
  __lowercase             =  hf_pointer
  for attribute in hf_param_name.split("""."""					):
   __lowercase             =  getattr(lowerCamelCase				,  lowerCamelCase					)
  __lowercase             =  shape_pointer.shape

  # let's reduce dimension
  __lowercase             =  value[0]
 else:
  __lowercase             =  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":
  __lowercase             =  value
 elif weight_type == "weight_g":
  __lowercase             =  value
 elif weight_type == "weight_v":
  __lowercase             =  value
 elif weight_type == "bias":
  __lowercase             =  value
 elif weight_type == "param":
  for attribute in hf_param_name.split("""."""					):
   __lowercase             =  getattr(lowerCamelCase				,  lowerCamelCase					)
  __lowercase             =  value
 else:
  __lowercase             =  value

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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(lowerCamelCase					):
   __lowercase             =  PARAM_MAPPING[full_name.split("""."""					)[-1]]
   __lowercase             =  "param"

 if weight_type is not None and weight_type != "param":
  __lowercase             =  ".".join([key, weight_type]					)
 elif weight_type is not None and weight_type == "param":
  __lowercase             =  ".".join([key, hf_param_name]					)
 else:
  __lowercase             =  key

 __lowercase             =  value if "lm_head" in full_key else value[0]


__UpperCamelCase      :      int     						=						{
    """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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=None				,  lowerCamelCase=None					):




 '''simple docstring'''







 __lowercase             =  False
 for key, mapped_key in MAPPING.items():
  __lowercase             =  "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]:
   __lowercase             =  True
   if "*" in mapped_key:
    __lowercase             =  name.split(lowerCamelCase					)[0].split("""."""					)[-2]
    __lowercase             =  mapped_key.replace("""*"""				,  lowerCamelCase					)
   if "weight_g" in name:
    __lowercase             =  "weight_g"
   elif "weight_v" in name:
    __lowercase             =  "weight_v"
   elif "bias" in name:
    __lowercase             =  "bias"
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    __lowercase             =  "weight"
   else:
    __lowercase             =  None
   if hf_dict is not None:
    rename_dict(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
   else:
    set_recursively(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
   return is_used
 return is_used





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  []
 __lowercase             =  fairseq_model.state_dict()

 __lowercase             =  hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  __lowercase             =  False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  hf_model.config.feat_extract_norm == """group"""				,  )
   __lowercase             =  True
  else:
   __lowercase             =  load_wavaveca_layer(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
  if not is_used:
   unused_weights.append(lowerCamelCase					)

 logger.warning(F'Unused weights: {unused_weights}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __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:
   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.'					)
   __lowercase             =  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.'					)
   __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:
   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.'					)
   __lowercase             =  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.'					)
   __lowercase             =  value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
 else:
  unused_weights.append(lowerCamelCase					)





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=None				,  lowerCamelCase=None				,  lowerCamelCase=True				,  lowerCamelCase=False					):




 '''simple docstring'''







 if config_path is not None:
  __lowercase             =  WavaVecaConfig.from_pretrained(lowerCamelCase					)
 else:
  __lowercase             =  WavaVecaConfig()

 if is_seq_class:
  __lowercase             =  read_txt_into_dict(lowerCamelCase					)
  __lowercase             =  idalabel
  __lowercase             =  WavaVecaForSequenceClassification(lowerCamelCase					)
  __lowercase             =  WavaVecaFeatureExtractor(
      feature_size=1				,  sampling_rate=16_000				,  padding_value=0				,  do_normalize=lowerCamelCase				,  return_attention_mask=lowerCamelCase				,  )
  feature_extractor.save_pretrained(lowerCamelCase					)

 elif is_finetuned:
  if dict_path:
   __lowercase             =  Dictionary.load(lowerCamelCase					)

   # 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.eos_index
   __lowercase             =  len(target_dict.symbols					)
   __lowercase             =  os.path.join(lowerCamelCase				,  """vocab.json"""					)
   if not os.path.isdir(lowerCamelCase					):
    logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase					)					)
    return
   os.makedirs(lowerCamelCase				,  exist_ok=lowerCamelCase					)
   __lowercase             =  target_dict.indices

   # fairseq has the <pad> and <s> switched
   __lowercase             =  0
   __lowercase             =  1
   with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as vocab_handle:
    json.dump(lowerCamelCase				,  lowerCamelCase					)
   __lowercase             =  WavaVecaCTCTokenizer(
       lowerCamelCase				,  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=lowerCamelCase				,  )
   __lowercase             =  True if config.feat_extract_norm == "layer" else False
   __lowercase             =  WavaVecaFeatureExtractor(
       feature_size=1				,  sampling_rate=16_000				,  padding_value=0				,  do_normalize=lowerCamelCase				,  return_attention_mask=lowerCamelCase				,  )
   __lowercase             =  WavaVecaProcessor(feature_extractor=lowerCamelCase				,  tokenizer=lowerCamelCase					)
   processor.save_pretrained(lowerCamelCase					)

  __lowercase             =  WavaVecaForCTC(lowerCamelCase					)
 else:
  __lowercase             =  WavaVecaForPreTraining(lowerCamelCase					)

 if is_finetuned or is_seq_class:
  __lowercase             =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]				,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					)}					)
 else:
  __lowercase             =  argparse.Namespace(task="""audio_pretraining"""					)
  __lowercase             =  fairseq.tasks.setup_task(lowerCamelCase					)

  __lowercase             =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,  task=lowerCamelCase					)

 __lowercase             =  model[0].eval()

 recursively_load_weights(lowerCamelCase				,  lowerCamelCase				,  not is_finetuned					)

 hf_wavavec.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
      __UpperCamelCase      :      List[Any]     						=						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""",
      )
      __UpperCamelCase      :      List[str]     						=						parser.parse_args()

      __UpperCamelCase      :      List[str]     						=						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,
      )









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

