


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

_UpperCamelCase          =     {
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "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",
}
_UpperCamelCase          =     [
    "lm_head",
]


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     for attribute in key.split("."       ):
          __A  :					Union[str, Any]						     =       getattr(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )

     if weight_type is not None:
          __A  :					int						     =       getattr(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ).shape
     else:
          __A  :					List[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":
          __A  :					Tuple						     =       value
     elif weight_type == "weight_g":
          __A  :					List[str]						     =       value
     elif weight_type == "weight_v":
          __A  :					List[str]						     =       value
     elif weight_type == "bias":
          __A  :					int						     =       value
     else:
          __A  :					Union[str, Any]						     =       value

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


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       []
     __A  :					Tuple						     =       fairseq_model.state_dict()

     if not is_headless:
          __A  :					str						     =       hf_model.dataavec_audio.feature_extractor
          __A  :					Dict						     =       hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          __A  :					Union[str, Any]						     =       hf_model.feature_extractor
          __A  :					List[str]						     =       hf_model.encoder.pos_conv_embed

     for name, value in fairseq_dict.items():
          __A  :					Optional[int]						     =       False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				)
               __A  :					Union[str, Any]						     =       True
          elif "pos_conv" in name:
               load_pos_conv_layer(
                   SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				)
               __A  :					List[Any]						     =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if not is_headless:
                         __A  :					int						     =       "data2vec_audio." + 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]:
                         __A  :					str						     =       True
                         if "*" in mapped_key:
                              __A  :					Tuple						     =       name.split(SCREAMING_SNAKE_CASE       )[0].split("."       )[-2]
                              __A  :					str						     =       mapped_key.replace("*"					,				SCREAMING_SNAKE_CASE       )
                         if "weight_g" in name:
                              __A  :					Any						     =       "weight_g"
                         elif "weight_v" in name:
                              __A  :					Any						     =       "weight_v"
                         elif "bias" in name:
                              __A  :					int						     =       "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __A  :					str						     =       "weight"
                         else:
                              __A  :					Tuple						     =       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 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       path.split("."       )
     return reduce(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Union[str, Any]						     =       access_by_string(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )
     __A  :					List[str]						     =       hf_weight.data

     if fsq_value.shape != hf_value.shape:
          raise ValueError(f"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."       )
     __A  :					List[str]						     =       fsq_value
     logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Tuple						     =       full_name.split("conv_layers."       )[-1]
     __A  :					List[str]						     =       name.split("."       )
     __A  :					Any						     =       int(items[0]       )
     __A  :					Optional[int]						     =       int(items[1]       )

     __A  :					Any						     =       name.split("."       )[-1]
     if type_id == 0:
          __A  :					List[str]						     =       "conv"
     elif type_id == 2:
          __A  :					Optional[int]						     =       "layer_norm"
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE       )
          return

     set_weights(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				f"conv_layers.{layer_id}.{layer_type}.{weight_type}"       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       full_name.split("pos_conv."       )[-1]
     __A  :					Optional[int]						     =       name.split("."       )
     __A  :					Dict						     =       int(items[0]       )
     __A  :					List[Any]						     =       int(items[1]       )

     __A  :					Optional[int]						     =       name.split("."       )[-1]
     if type_id != 0:
          unused_weights.append(SCREAMING_SNAKE_CASE       )
          return
     else:
          __A  :					List[Any]						     =       "conv"

     set_weights(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				f"layers.{layer_id}.{layer_type}.{weight_type}"       )


@torch.no_grad()
def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=None					,				SCREAMING_SNAKE_CASE=True       ):




     '''simple docstring'''





     if config_path is not None:
          __A  :					List[str]						     =       DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE       )
     else:
          __A  :					List[str]						     =       DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          __A  :					Optional[int]						     =       DataaVecAudioModel(SCREAMING_SNAKE_CASE       )
          __A  :					Optional[Any]						     =       os.path.dirname(SCREAMING_SNAKE_CASE       )

          __A  :					List[Any]						     =       torch.load(SCREAMING_SNAKE_CASE       )
          __A  :					List[str]						     =       state_dict["model"].pop("final_proj.0.weight"       )
          __A  :					Optional[int]						     =       state_dict["model"].pop("final_proj.0.bias"       )
          __A  :					Union[str, Any]						     =       os.path.join(SCREAMING_SNAKE_CASE					,				"converted.pt"       )
          torch.save(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )
     else:
          __A  :					Optional[int]						     =       DataaVecAudioForCTC(SCREAMING_SNAKE_CASE       )
          __A  :					int						     =       checkpoint_path

     def load_dataavec(SCREAMING_SNAKE_CASE       ):
          __A     ,__A     ,__A  :					Any						     =       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
          return model[0].eval()

     __A  :					List[str]						     =       load_dataavec(SCREAMING_SNAKE_CASE       )

     recursively_load_weights(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				not is_finetuned       )

     __A  :					List[Any]						     =       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"       )

     __A  :					Dict						     =       load_dataset("patrickvonplaten/librispeech_asr_dummy"					,				"clean"					,				split="validation"       )
     __A  :					Tuple						     =       [x["array"] for x in ds[:4]["audio"]]

     __A  :					str						     =       processor(SCREAMING_SNAKE_CASE					,				return_tensors="pt"					,				padding=SCREAMING_SNAKE_CASE       )

     __A  :					List[str]						     =       inputs.input_values
     __A  :					List[str]						     =       inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          __A  :					int						     =       model(source=SCREAMING_SNAKE_CASE					,				padding_mask=(1 - attention_mask)					,				mask=SCREAMING_SNAKE_CASE					,				features_only=SCREAMING_SNAKE_CASE       )[
              "encoder_out"
          ].transpose(0					,				1       )
          __A  :					Union[str, Any]						     =       hf_wavavec(SCREAMING_SNAKE_CASE					,				attention_mask=SCREAMING_SNAKE_CASE       )["logits"]

          __A  :					Optional[Any]						     =       torch.argmax(SCREAMING_SNAKE_CASE					,				dim=-1       )
          __A  :					Any						     =       processor.batch_decode(SCREAMING_SNAKE_CASE       )

          print(f"Expected Output: {ds[:4]['text']}, Pred: {output_string}"       )
     else:
          __A  :					int						     =       model(source=SCREAMING_SNAKE_CASE					,				padding_mask=(1 - attention_mask)					,				mask=SCREAMING_SNAKE_CASE					,				features_only=SCREAMING_SNAKE_CASE       )[
              "layer_results"
          ][-1][0].transpose(0					,				1       )
          __A  :					str						     =       hf_wavavec(SCREAMING_SNAKE_CASE					,				attention_mask=SCREAMING_SNAKE_CASE       )["last_hidden_state"]

     print(our_output.shape					,				their_output.shape       )
     __A  :					Optional[Any]						     =       torch.max(torch.abs(our_output - their_output       )       ).item()
     print(f"max_absolute_diff = {max_absolute_diff}"       )  # ~ 1e-7
     __A  :					List[str]						     =       torch.allclose(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				atol=1E-3       )
     print("Do both models output the same tensors?"					,				"🔥" if success else "💩"       )
     if not success:
          raise Exception("Something went wRoNg"       )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE       )

     if is_finetuned:
          processor.save_pretrained(SCREAMING_SNAKE_CASE       )
     else:
          processor.feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE       )


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_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

