




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()
_lowerCamelCase							=      logging.get_logger(__name__)

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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Any   ,       UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      Optional[int]      )->			Optional[int]:

       """simple docstring"""




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

       if weight_type is not None:
              a			   =getattr(_snake_case   ,       _snake_case      ).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 							lowerCamelCase    (							UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      Union[str, Any]      )->			Optional[Any]:

       """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(
                         _snake_case   ,       _snake_case   ,       _snake_case   ,       _snake_case   ,       )
                     a			   =True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         _snake_case   ,       _snake_case   ,       _snake_case   ,       _snake_case   ,       )
                     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(_snake_case      )[0].split("""."""      )[-2]
                                          a			   =mapped_key.replace("""*"""   ,       _snake_case      )
                                   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(_snake_case   ,       _snake_case   ,       _snake_case   ,       _snake_case   ,       _snake_case      )
                            continue
              if not is_used:
                     unused_weights.append(_snake_case      )

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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      str      )->			Optional[Any]:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      Any      )->			List[str]:

       """simple docstring"""




       a			   =access_by_string(_snake_case   ,       _snake_case      )
       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 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      List[str]      )->			List[Any]:

       """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(_snake_case      )
              return

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





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      Optional[Any]   ,       UpperCAmelCase_	:      List[Any]      )->			Any:

       """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(_snake_case      )
              return
       else:
              a			   ="""conv"""

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





@torch.no_grad()
def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      List[str]=None   ,       UpperCAmelCase_	:      Tuple=None   ,       UpperCAmelCase_	:      Dict=True      )->			Dict:

       """simple docstring"""




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

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

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

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

       a			   =load_dataavec(_snake_case      )

       recursively_load_weights(_snake_case   ,       _snake_case   ,       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(_snake_case   ,       return_tensors="""pt"""   ,       padding=_snake_case      )

       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=_snake_case   ,       padding_mask=(1 - attention_mask)   ,       mask=_snake_case   ,       features_only=_snake_case      )[
                  """encoder_out"""
              ].transpose(0   ,       1      )
              a			   =hf_wavavec(_snake_case   ,       attention_mask=_snake_case      )["""logits"""]

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

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

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


if __name__ == "__main__":
    _lowerCamelCase							=      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'''
    )
    _lowerCamelCase							=      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 ..utils import DummyObject, requires_backends
class   UpperCAmelCase_	(				metaclass=lowercase__   ):
    snake_case_											=      ["""torch""", """transformers""", """k_diffusion"""]
    def __init__(	self			:       Optional[Any]   ,						*_lowercase			:       Optional[int]   ,						**_lowercase			:       List[str]		)						->    Dict:
       requires_backends(self   ,						["torch", "transformers", "k_diffusion"]		)
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Union[str, Any]   ,						*_lowercase			:       Any   ,						**_lowercase			:       List[Any]		)						->    str:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)




    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						*_lowercase			:       List[Any]   ,						**_lowercase			:       Any		)						->    Any:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)