


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()
A__:   Tuple    =	logging.get_logger(__name__)

A__:   Tuple    =	{
    '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',
}
A__:   str    =	[
    'lm_head',
]


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     str   ,_UpperCAmelCase     :     Optional[Any]   ,_UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     Any  )  ->      str:







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

       if weight_type is not None:
              _a   :			List[str]					 =getattr(lowercase_   ,lowercase_  ).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   :			int					 =value
       elif weight_type == "weight_g":
              _a   :			Optional[int]					 =value
       elif weight_type == "weight_v":
              _a   :			Any					 =value
       elif weight_type == "bias":
              _a   :			int					 =value
       else:
              _a   :			List[str]					 =value

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


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[Any]   ,_UpperCAmelCase     :     str   ,_UpperCAmelCase     :     Optional[Any]  )  ->      List[str]:
       _a   :			Tuple					 =[]
       _a   :			List[str]					 =fairseq_model.state_dict()

       if not is_headless:
              _a   :			List[Any]					 =hf_model.dataavec_audio.feature_extractor
              _a   :			List[Any]					 =hf_model.dataavec_audio.encoder.pos_conv_embed

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

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

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


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     List[str]  )  ->      int:
       _a   :			Optional[int]					 =path.split("""."""  )
       return reduce(lowercase_   ,lowercase_   ,lowercase_  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     int   ,_UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     Any  )  ->      List[str]:
       _a   :			int					 =access_by_string(lowercase_   ,lowercase_  )
       _a   :			List[Any]					 =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   :			Optional[int]					 =fsq_value
       logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[Any]   ,_UpperCAmelCase     :     List[Any]   ,_UpperCAmelCase     :     List[Any]   ,_UpperCAmelCase     :     Union[str, Any]  )  ->      str:
       _a   :			str					 =full_name.split("""conv_layers."""  )[-1]
       _a   :			Optional[int]					 =name.split("""."""  )
       _a   :			List[str]					 =int(items[0]  )
       _a   :			Optional[Any]					 =int(items[1]  )

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

       set_weights(lowercase_   ,lowercase_   ,lowercase_   ,F"conv_layers.{layer_id}.{layer_type}.{weight_type}"  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     List[str]   ,_UpperCAmelCase     :     Optional[Any]   ,_UpperCAmelCase     :     str   ,_UpperCAmelCase     :     Optional[Any]  )  ->      int:
       _a   :			int					 =full_name.split("""pos_conv."""  )[-1]
       _a   :			Any					 =name.split("""."""  )
       _a   :			List[str]					 =int(items[0]  )
       _a   :			Optional[Any]					 =int(items[1]  )

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

       set_weights(lowercase_   ,lowercase_   ,lowercase_   ,F"layers.{layer_id}.{layer_type}.{weight_type}"  )


@torch.no_grad()
def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     List[str]   ,_UpperCAmelCase     :     int   ,_UpperCAmelCase     :     Optional[int]=None   ,_UpperCAmelCase     :     Union[str, Any]=None   ,_UpperCAmelCase     :     int=True  )  ->      List[str]:







       if config_path is not None:
              _a   :			Optional[int]					 =DataaVecAudioConfig.from_pretrained(lowercase_  )
       else:
              _a   :			int					 =DataaVecAudioConfig()

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

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

       def load_dataavec(_UpperCAmelCase     :     str  ):
              _a ,   _a ,   _a   :			List[Any]					 =fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       _a   :			Optional[int]					 =load_dataavec(lowercase_  )

       recursively_load_weights(lowercase_   ,lowercase_   ,not is_finetuned  )

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

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

       _a   :			List[str]					 =processor(lowercase_   ,return_tensors="""pt"""   ,padding=lowercase_  )

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

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

              _a   :			str					 =torch.argmax(lowercase_   ,dim=-1  )
              _a   :			Dict					 =processor.batch_decode(lowercase_  )

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

       print(our_output.shape   ,their_output.shape  )
       _a   :			List[str]					 =torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-7
       _a   :			int					 =torch.allclose(lowercase_   ,lowercase_   ,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(lowercase_  )

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


if __name__ == "__main__":
   A__:   Optional[int]    =	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'''
   )
   A__:   Tuple    =	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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             if text is None and images is None:
                    raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

             if text is not None:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if images is not None:
                    _a   :			Any					 =self.image_processor(SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if text is not None and images is not None:
                    _a   :			Dict					 =image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE     )					,						tensor_type=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


