


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()
lowerCamelCase__							:						List[str]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[str]							     =   {
    """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""",
}
lowerCamelCase__							:						int							     =   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[int]:
  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[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]


lowerCamelCase__							:						Optional[int]							     =   {
    """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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None				)    ->				Union[str, 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Optional[int]:
  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				int:
  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=True							,   __lowerCAmelCase=False				)    ->				List[str]:
  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=1_6000							,   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=1_6000							,   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__":
   lowerCamelCase__							:						Tuple							     =   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""",
   )
   lowerCamelCase__							:						Union[str, Any]							     =   parser.parse_args()

   lowerCamelCase__							:						int							     =   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,
   )





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__							:						List[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'data2vec-vision'

     def __init__(					self:int     ,		_a:Tuple=7_68     ,		_a:int=12     ,		_a:Any=12     ,		_a:Optional[int]=30_72     ,		_a:Optional[int]="gelu"     ,		_a:Any=0.0     ,		_a:Any=0.0     ,		_a:List[str]=0.02     ,		_a:Dict=1e-12     ,		_a:Tuple=2_24     ,		_a:Any=16     ,		_a:str=3     ,		_a:str=False     ,		_a:Union[str, Any]=False     ,		_a:Optional[int]=False     ,		_a:Any=False     ,		_a:Dict=0.1     ,		_a:Dict=0.1     ,		_a:str=True     ,		_a:str=[3, 5, 7, 11]     ,		_a:List[str]=[1, 2, 3, 6]     ,		_a:List[str]=True     ,		_a:Any=0.4     ,		_a:str=2_56     ,		_a:Union[str, Any]=1     ,		_a:int=False     ,		_a:Optional[int]=2_55     ,		**_a:Dict     ,		):
       super().__init__(**_a   )

       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size
       snake_case__						= hidden_act
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps

       snake_case__						= image_size
       snake_case__						= patch_size
       snake_case__						= num_channels
       snake_case__						= use_mask_token
       snake_case__						= use_absolute_position_embeddings
       snake_case__						= use_relative_position_bias
       snake_case__						= use_shared_relative_position_bias
       snake_case__						= layer_scale_init_value
       snake_case__						= drop_path_rate
       snake_case__						= use_mean_pooling
       # decode head attributes (semantic segmentation)
       snake_case__						= out_indices
       snake_case__						= pool_scales
       # auxiliary head attributes (semantic segmentation)
       snake_case__						= use_auxiliary_head
       snake_case__						= auxiliary_loss_weight
       snake_case__						= auxiliary_channels
       snake_case__						= auxiliary_num_convs
       snake_case__						= auxiliary_concat_input
       snake_case__						= semantic_loss_ignore_index




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       version.parse('1.11'				)

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return 1e-4

