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()
__snake_case			      =    logging.get_logger(__name__)

__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',
}
__snake_case			      =    [
    'lm_head',
]





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  List[Any]:






      '''simple docstring'''





      for attribute in key.split('''.'''						):
            UpperCAmelCase    : List[Any]      =getattr(UpperCamelCase__		,					UpperCamelCase__						)

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

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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase    : Any      =[]
      UpperCAmelCase    : Any      =fairseq_model.state_dict()

      if not is_headless:
            UpperCAmelCase    : Tuple      =hf_model.dataavec_audio.feature_extractor
            UpperCAmelCase    : Tuple      =hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            UpperCAmelCase    : Dict      =hf_model.feature_extractor
            UpperCAmelCase    : Optional[int]      =hf_model.encoder.pos_conv_embed

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

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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  List[str]:






      '''simple docstring'''





      UpperCAmelCase    : Dict      =path.split('''.'''						)
      return reduce(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__						)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  List[str]:






      '''simple docstring'''





      UpperCAmelCase    : Tuple      =access_by_string(UpperCamelCase__		,					UpperCamelCase__						)
      UpperCAmelCase    : 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.'''						)
      UpperCAmelCase    : Tuple      =fsq_value
      logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''						)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Tuple      =full_name.split('''conv_layers.'''						)[-1]
      UpperCAmelCase    : int      =name.split('''.'''						)
      UpperCAmelCase    : Dict      =int(items[0]						)
      UpperCAmelCase    : Any      =int(items[1]						)

      UpperCAmelCase    : Optional[int]      =name.split('''.'''						)[-1]
      if type_id == 0:
            UpperCAmelCase    : Optional[int]      ='''conv'''
      elif type_id == 2:
            UpperCAmelCase    : Dict      ='''layer_norm'''
      else:
            unused_weights.append(UpperCamelCase__						)
            return

      set_weights(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''						)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





      UpperCAmelCase    : int      =full_name.split('''pos_conv.'''						)[-1]
      UpperCAmelCase    : int      =name.split('''.'''						)
      UpperCAmelCase    : Dict      =int(items[0]						)
      UpperCAmelCase    : Union[str, Any]      =int(items[1]						)

      UpperCAmelCase    : List[str]      =name.split('''.'''						)[-1]
      if type_id != 0:
            unused_weights.append(UpperCamelCase__						)
            return
      else:
            UpperCAmelCase    : Tuple      ='''conv'''

      set_weights(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					f'''layers.{layer_id}.{layer_type}.{weight_type}'''						)





@torch.no_grad()
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					__lowerCAmelCase=True						)->  Any:






      '''simple docstring'''





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

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

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

      def load_dataavec(__lowerCAmelCase						):
            UpperCAmelCase    : List[Any]      =fairseq.checkpoint_utils.load_model_ensemble_and_task([path]						)
            return model[0].eval()

      UpperCAmelCase    : str      =load_dataavec(UpperCamelCase__						)

      recursively_load_weights(UpperCamelCase__		,					UpperCamelCase__		,					not is_finetuned						)

      UpperCAmelCase    : Dict      =WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''						)

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

      UpperCAmelCase    : Dict      =processor(UpperCamelCase__		,					return_tensors='''pt'''		,					padding=UpperCamelCase__						)

      UpperCAmelCase    : Union[str, Any]      =inputs.input_values
      UpperCAmelCase    : Any      =inputs.attention_mask
      #    input_values = inputs.input_values[:, :-1]
      #    attention_mask = inputs.attention_mask[:, :-1]

      hf_wavavec.eval()
      model.eval()
      if is_finetuned:
            UpperCAmelCase    : List[str]      =model(source=UpperCamelCase__		,					padding_mask=(1 - attention_mask)		,					mask=UpperCamelCase__		,					features_only=UpperCamelCase__						)[
                '''encoder_out'''
            ].transpose(0		,					1						)
            UpperCAmelCase    : List[str]      =hf_wavavec(UpperCamelCase__		,					attention_mask=UpperCamelCase__						)['''logits''']

            UpperCAmelCase    : int      =torch.argmax(UpperCamelCase__		,					dim=-1						)
            UpperCAmelCase    : List[Any]      =processor.batch_decode(UpperCamelCase__						)

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

      print(our_output.shape		,					their_output.shape						)
      UpperCAmelCase    : str      =torch.max(torch.abs(our_output - their_output						)						).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
      UpperCAmelCase    : List[Any]      =torch.allclose(UpperCamelCase__		,					UpperCamelCase__		,					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(UpperCamelCase__						)

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


if __name__ == "__main__":
     __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'''
     )
     __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
     )

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
