


"""simple docstring"""




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()
__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					=						{
    """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""",
}
__SCREAMING_SNAKE_CASE					=						[
    """lm_head""",
]



def 						__a      (  a,		a,		a,		a,		a					):





       """simple docstring"""







       for attribute in key.split("."					):
              _a          =				getattr(a,		a					)

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

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



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       _a          =				[]
       _a          =				fairseq_model.state_dict()

       if not is_headless:
              _a          =				hf_model.dataavec_audio.feature_extractor
              _a          =				hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              _a          =				hf_model.feature_extractor
              _a          =				hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              _a          =				False
              if "conv_layers" in name:
                     load_conv_layer(
                         a,		a,		a,		a,		)
                     _a          =				True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a,		a,		a,		a,		)
                     _a          =				True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   _a          =				"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          =				True
                                   if "*" in mapped_key:
                                          _a          =				name.split(a					)[0].split("."					)[-2]
                                          _a          =				mapped_key.replace("*",		a					)
                                   if "weight_g" in name:
                                          _a          =				"weight_g"
                                   elif "weight_v" in name:
                                          _a          =				"weight_v"
                                   elif "bias" in name:
                                          _a          =				"bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _a          =				"weight"
                                   else:
                                          _a          =				None
                                   set_recursively(a,		a,		a,		a,		a					)
                            continue
              if not is_used:
                     unused_weights.append(a					)

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



def 						__a      (  a,		a					):





       """simple docstring"""







       _a          =				path.split("."					)
       return reduce(a,		a,		a					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       _a          =				access_by_string(a,		a					)
       _a          =				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          =				fsq_value
       logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       _a          =				full_name.split("conv_layers."					)[-1]
       _a          =				name.split("."					)
       _a          =				int(items[0]					)
       _a          =				int(items[1]					)

       _a          =				name.split("."					)[-1]
       if type_id == 0:
              _a          =				"conv"
       elif type_id == 2:
              _a          =				"layer_norm"
       else:
              unused_weights.append(a					)
              return

       set_weights(a,		a,		a,		F'conv_layers.{layer_id}.{layer_type}.{weight_type}'					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       _a          =				full_name.split("pos_conv."					)[-1]
       _a          =				name.split("."					)
       _a          =				int(items[0]					)
       _a          =				int(items[1]					)

       _a          =				name.split("."					)[-1]
       if type_id != 0:
              unused_weights.append(a					)
              return
       else:
              _a          =				"conv"

       set_weights(a,		a,		a,		F'layers.{layer_id}.{layer_type}.{weight_type}'					)



@torch.no_grad()
def 						__a      (  a,		a,		a=None,		a=None,		a=True					):





       """simple docstring"""







       if config_path is not None:
              _a          =				DataaVecAudioConfig.from_pretrained(a					)
       else:
              _a          =				DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              _a          =				DataaVecAudioModel(a					)
              _a          =				os.path.dirname(a					)

              _a          =				torch.load(a					)
              _a          =				state_dict["model"].pop("final_proj.0.weight"					)
              _a          =				state_dict["model"].pop("final_proj.0.bias"					)
              _a          =				os.path.join(a,		"converted.pt"					)
              torch.save(a,		a					)
       else:
              _a          =				DataaVecAudioForCTC(a					)
              _a          =				checkpoint_path

       def load_dataavec(a					):
              _a   , _a   , _a          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]					)
              return model[0].eval()

       _a          =				load_dataavec(a					)

       recursively_load_weights(a,		a,		not is_finetuned					)

       _a          =				WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"					)

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

       _a          =				processor(a,		return_tensors="pt",		padding=a					)

       _a          =				inputs.input_values
       _a          =				inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              _a          =				model(source=a,		padding_mask=(1 - attention_mask),		mask=a,		features_only=a					)[
                  "encoder_out"
              ].transpose(0,		1					)
              _a          =				hf_wavavec(a,		attention_mask=a					)["logits"]

              _a          =				torch.argmax(a,		dim=-1					)
              _a          =				processor.batch_decode(a					)

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

       print(our_output.shape,		their_output.shape					)
       _a          =				torch.max(torch.abs(our_output - their_output					)					).item()
       print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
       _a          =				torch.allclose(a,		a,		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(a					)

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


if __name__ == "__main__":
   __SCREAMING_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"""
   )
   __SCREAMING_SNAKE_CASE					=						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"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				data
          _a          =				None




   def __str__(		self							:Any       ):
          return f'{self.data}'






class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Any       ):
          _a          =				None
          _a          =				None




   def __iter__(		self							:List[str]       ):
          _a          =				self.front
          while node:
                 yield node.data
                 _a          =				node.next




   def __len__(		self							:List[str]       ):
          return len(tuple(iter(self       )       )       )




   def __str__(		self							:Union[str, Any]       ):
          return " <- ".join(str(UpperCamelCase__       ) for item in self       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          return len(self       ) == 0




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
          _a          =				Node(UpperCamelCase__       )
          if self.is_empty():
                 _a          =				_a          =				node
          else:
                 assert isinstance(self.rear				,  UpperCamelCase__       )
                 _a          =				node
                 _a          =				node




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          if self.is_empty():
                 raise IndexError("dequeue from empty queue"       )
          assert isinstance(self.front				,  UpperCamelCase__       )
          _a          =				self.front
          _a          =				self.front.next
          if self.front is None:
                 _a          =				None
          return node.data




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				_a          =				None


if __name__ == "__main__":
   from doctest import testmod

   testmod()
