



"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

snake_case   					=				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
snake_case   					=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]




def 			snake_case		(		lowerCAmelCase_	)						->				Optional[Any]:
  _snake_case   	=      {}
  with open(lowerCAmelCase_  ,						'''r'''	) as file:
    for line_number, line in enumerate(lowerCAmelCase_	):
      _snake_case   	=      line.strip()
      if line:
        _snake_case   	=      line.split()
        _snake_case   	=      line_number
        _snake_case   	=      words[0]
        _snake_case   	=      value
  return result




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  for attribute in key.split('''.'''	):
    _snake_case   	=      getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)

  _snake_case   	=      None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase_	):
      _snake_case   	=      PARAM_MAPPING[full_name.split('''.'''	)[-1]]
      _snake_case   	=      '''param'''

  if weight_type is not None and weight_type != "param":
    _snake_case   	=      getattr(lowerCAmelCase_  ,						lowerCAmelCase_	).shape
  elif weight_type is not None and weight_type == "param":
    _snake_case   	=      hf_pointer
    for attribute in hf_param_name.split('''.'''	):
      _snake_case   	=      getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)
    _snake_case   	=      shape_pointer.shape

    # let's reduce dimension
    _snake_case   	=      value[0]
  else:
    _snake_case   	=      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":
    _snake_case   	=      value
  elif weight_type == "weight_g":
    _snake_case   	=      value
  elif weight_type == "weight_v":
    _snake_case   	=      value
  elif weight_type == "bias":
    _snake_case   	=      value
  elif weight_type == "param":
    for attribute in hf_param_name.split('''.'''	):
      _snake_case   	=      getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)
    _snake_case   	=      value
  else:
    _snake_case   	=      value

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				str:
  _snake_case   	=      None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase_	):
      _snake_case   	=      PARAM_MAPPING[full_name.split('''.'''	)[-1]]
      _snake_case   	=      '''param'''

  if weight_type is not None and weight_type != "param":
    _snake_case   	=      '''.'''.join([key, weight_type]	)
  elif weight_type is not None and weight_type == "param":
    _snake_case   	=      '''.'''.join([key, hf_param_name]	)
  else:
    _snake_case   	=      key

  _snake_case   	=      value if '''lm_head''' in full_key else value[0]


snake_case   					=				{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				Optional[Any]:
  _snake_case   	=      False
  for key, mapped_key in MAPPING.items():
    _snake_case   	=      '''wav2vec2.''' + 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]:
      _snake_case   	=      True
      if "*" in mapped_key:
        _snake_case   	=      name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
        _snake_case   	=      mapped_key.replace('''*'''  ,						lowerCAmelCase_	)
      if "weight_g" in name:
        _snake_case   	=      '''weight_g'''
      elif "weight_v" in name:
        _snake_case   	=      '''weight_v'''
      elif "bias" in name:
        _snake_case   	=      '''bias'''
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _snake_case   	=      '''weight'''
      else:
        _snake_case   	=      None
      if hf_dict is not None:
        rename_dict(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      else:
        set_recursively(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      return is_used
  return is_used




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Union[str, Any]:
  _snake_case   	=      []
  _snake_case   	=      fairseq_model.state_dict()

  _snake_case   	=      hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case   	=      False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						hf_model.config.feat_extract_norm == '''group'''  ,						)
      _snake_case   	=      True
    else:
      _snake_case   	=      load_wavaveca_layer(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    if not is_used:
      unused_weights.append(lowerCAmelCase_	)

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  _snake_case   	=      full_name.split('''conv_layers.'''	)[-1]
  _snake_case   	=      name.split('''.'''	)
  _snake_case   	=      int(items[0]	)
  _snake_case   	=      int(items[1]	)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
      _snake_case   	=      value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
      _snake_case   	=      value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
      _snake_case   	=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"""{full_name} has size {value.shape}, but"""
            f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
      _snake_case   	=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
  else:
    unused_weights.append(lowerCAmelCase_	)




@torch.no_grad()
def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=True  ,						lowerCAmelCase_=False	)						->				Dict:
  if config_path is not None:
    _snake_case   	=      WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
  else:
    _snake_case   	=      WavaVecaConfig()

  if is_seq_class:
    _snake_case   	=      read_txt_into_dict(lowerCAmelCase_	)
    _snake_case   	=      idalabel
    _snake_case   	=      WavaVecaForSequenceClassification(lowerCAmelCase_	)
    _snake_case   	=      WavaVecaFeatureExtractor(
        feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						)
    feature_extractor.save_pretrained(lowerCAmelCase_	)

  elif is_finetuned:
    if dict_path:
      _snake_case   	=      Dictionary.load(lowerCAmelCase_	)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case   	=      target_dict.pad_index
      _snake_case   	=      target_dict.bos_index
      _snake_case   	=      target_dict.eos_index
      _snake_case   	=      len(target_dict.symbols	)
      _snake_case   	=      os.path.join(lowerCAmelCase_  ,						'''vocab.json'''	)
      if not os.path.isdir(lowerCAmelCase_	):
        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_	)	)
        return
      os.makedirs(lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_	)
      _snake_case   	=      target_dict.indices

      # fairseq has the <pad> and <s> switched
      _snake_case   	=      0
      _snake_case   	=      1
      with open(lowerCAmelCase_  ,						'''w'''  ,						encoding='''utf-8'''	) as vocab_handle:
        json.dump(lowerCAmelCase_  ,						lowerCAmelCase_	)
      _snake_case   	=      WavaVecaCTCTokenizer(
          lowerCAmelCase_  ,						unk_token=target_dict.unk_word  ,						pad_token=target_dict.pad_word  ,						bos_token=target_dict.bos_word  ,						eos_token=target_dict.eos_word  ,						word_delimiter_token='''|'''  ,						do_lower_case=lowerCAmelCase_  ,						)
      _snake_case   	=      True if config.feat_extract_norm == '''layer''' else False
      _snake_case   	=      WavaVecaFeatureExtractor(
          feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						)
      _snake_case   	=      WavaVecaProcessor(feature_extractor=lowerCAmelCase_  ,						tokenizer=lowerCAmelCase_	)
      processor.save_pretrained(lowerCAmelCase_	)

    _snake_case   	=      WavaVecaForCTC(lowerCAmelCase_	)
  else:
    _snake_case   	=      WavaVecaForPreTraining(lowerCAmelCase_	)

  if is_finetuned or is_seq_class:
    _snake_case	,     _snake_case	,     _snake_case   	=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]  ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
  else:
    _snake_case   	=      argparse.Namespace(task='''audio_pretraining'''	)
    _snake_case   	=      fairseq.tasks.setup_task(lowerCAmelCase_	)

    _snake_case	,     _snake_case	,     _snake_case   	=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,						task=lowerCAmelCase_	)

  _snake_case   	=      model[0].eval()

  recursively_load_weights(lowerCAmelCase_  ,						lowerCAmelCase_  ,						not is_finetuned	)

  hf_wavavec.save_pretrained(lowerCAmelCase_	)


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'''
     )
     parser.add_argument(
         '''--is_seq_class''',
         action='''store_true''',
         help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
     )
     snake_case   					=				parser.parse_args()

     snake_case   					=				not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )








'''simple docstring'''







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_text_model"
    lowercase						       =		["past_key_values"]
    lowercase						       =		{
        "hidden_size": "hidden_size",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }



    def __init__(       self		,					UpperCamelCase__=50244		,					UpperCamelCase__=768		,					UpperCamelCase__=64		,					UpperCamelCase__=2048		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					UpperCamelCase__=0.1		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=1.0		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0		,					UpperCamelCase__=False		,					UpperCamelCase__=0		,					UpperCamelCase__=1		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   d_kv
           A_						       =   d_ff
           A_						       =   num_layers
           A_						       =   num_heads
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   dropout_rate
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_factor
           A_						       =   use_cache

           A_						       =   eos_token_id
           A_						       =   decoder_start_token_id

           # for backwards compatibility
           A_						       =   dense_act_fn

           super().__init__(
               pad_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					decoder_start_token_id=UpperCamelCase__		,					tie_word_embeddings=UpperCamelCase__		,					is_decoder=UpperCamelCase__		,					**UpperCamelCase__		,					)







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   config_dict["""text_config"""]

           if "model_type" in config_dict and hasattr(cls		,					"""model_type"""							) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                      f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''							)

           return cls.from_dict(UpperCamelCase__		,					**UpperCamelCase__							)


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_vision_model"



    def __init__(       self		,					UpperCamelCase__=768		,					UpperCamelCase__=768		,					UpperCamelCase__=2048		,					UpperCamelCase__=64		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-1_0		,					UpperCamelCase__=1.0		,					UpperCamelCase__=4096		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					**UpperCamelCase__		,					)    ->   int:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)

           A_						       =   hidden_size
           A_						       =   patch_embed_hidden_size
           A_						       =   d_ff
           A_						       =   dropout_rate
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   initializer_range
           A_						       =   initializer_factor
           A_						       =   attention_dropout
           A_						       =   layer_norm_eps
           A_						       =   dense_act_fn
           A_						       =   seq_len
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   d_kv







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   config_dict["""vision_config"""]

           if "model_type" in config_dict and hasattr(cls		,					"""model_type"""							) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                      f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''							)

           return cls.from_dict(UpperCamelCase__		,					**UpperCamelCase__							)


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct"
    lowercase						       =		True



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=1.0		,					UpperCamelCase__=0.02		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           super().__init__(tie_word_embeddings=UpperCamelCase__		,					is_encoder_decoder=UpperCamelCase__		,					**UpperCamelCase__							)

           if text_config is None:
                  A_						       =   {}
                  logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""							)

           if vision_config is None:
                  A_						       =   {}
                  logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""							)

           A_						       =   PixaStructTextConfig(**UpperCamelCase__							)
           A_						       =   PixaStructVisionConfig(**UpperCamelCase__							)

           A_						       =   self.text_config.decoder_start_token_id
           A_						       =   self.text_config.pad_token_id
           A_						       =   self.text_config.eos_token_id

           A_						       =   initializer_factor
           A_						       =   initializer_range

           A_						       =   self.initializer_range
           A_						       =   self.initializer_range

           A_						       =   is_vqa



    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           return cls(text_config=text_config.to_dict()		,					vision_config=vision_config.to_dict()		,					**UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   copy.deepcopy(self.__dict__							)
           A_						       =   self.text_config.to_dict()
           A_						       =   self.vision_config.to_dict()
           A_						       =   self.__class__.model_type
           return output
