






'''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()
SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_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',
}
SCREAMING_SNAKE_CASE							=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]


def 				lowercase_					(     __A			:							Optional[int]      )							->   Optional[int]:



 """simple docstring"""




 lowercase       : Optional[int]						={}
 with open(__A       ,   '''r'''      ) as file:
  for line_number, line in enumerate(__A      ):
   lowercase       : List[Any]						=line.strip()
   if line:
    lowercase       : int						=line.split()
    lowercase       : int						=line_number
    lowercase       : str						=words[0]
    lowercase       : Dict						=value
 return result


def 				lowercase_					(     __A			:							str       ,   __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Dict       ,   __A			:							Union[str, Any]      )							->   Optional[Any]:



 """simple docstring"""




 for attribute in key.split('''.'''      ):
  lowercase       : Optional[Any]						=getattr(__A       ,   __A      )

 lowercase       : Dict						=None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(__A      ):
   lowercase       : Optional[int]						=PARAM_MAPPING[full_name.split('''.'''      )[-1]]
   lowercase       : int						='''param'''

 if weight_type is not None and weight_type != "param":
  lowercase       : Union[str, Any]						=getattr(__A       ,   __A      ).shape
 elif weight_type is not None and weight_type == "param":
  lowercase       : Dict						=hf_pointer
  for attribute in hf_param_name.split('''.'''      ):
   lowercase       : Optional[int]						=getattr(__A       ,   __A      )
  lowercase       : List[str]						=shape_pointer.shape

  # let's reduce dimension
  lowercase       : Any						=value[0]
 else:
  lowercase       : 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":
  lowercase       : int						=value
 elif weight_type == "weight_g":
  lowercase       : Optional[int]						=value
 elif weight_type == "weight_v":
  lowercase       : str						=value
 elif weight_type == "bias":
  lowercase       : str						=value
 elif weight_type == "param":
  for attribute in hf_param_name.split('''.'''      ):
   lowercase       : Union[str, Any]						=getattr(__A       ,   __A      )
  lowercase       : List[Any]						=value
 else:
  lowercase       : List[str]						=value

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


def 				lowercase_					(     __A			:							Union[str, Any]       ,   __A			:							int       ,   __A			:							Optional[int]       ,   __A			:							str       ,   __A			:							List[Any]      )							->   Tuple:



 """simple docstring"""




 lowercase       : Optional[Any]						=None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(__A      ):
   lowercase       : Tuple						=PARAM_MAPPING[full_name.split('''.'''      )[-1]]
   lowercase       : List[str]						='''param'''

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

 lowercase       : Any						=value if '''lm_head''' in full_key else value[0]


SCREAMING_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 				lowercase_					(     __A			:							List[str]       ,   __A			:							Optional[int]       ,   __A			:							int=None       ,   __A			:							str=None      )							->   Optional[int]:



 """simple docstring"""




 lowercase       : Any						=False
 for key, mapped_key in MAPPING.items():
  lowercase       : Union[str, Any]						='''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]:
   lowercase       : Dict						=True
   if "*" in mapped_key:
    lowercase       : Union[str, Any]						=name.split(__A      )[0].split('''.'''      )[-2]
    lowercase       : Union[str, Any]						=mapped_key.replace('''*'''       ,   __A      )
   if "weight_g" in name:
    lowercase       : Optional[int]						='''weight_g'''
   elif "weight_v" in name:
    lowercase       : Optional[Any]						='''weight_v'''
   elif "bias" in name:
    lowercase       : Optional[int]						='''bias'''
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    lowercase       : str						='''weight'''
   else:
    lowercase       : str						=None
   if hf_dict is not None:
    rename_dict(__A       ,   __A       ,   __A       ,   __A       ,   __A      )
   else:
    set_recursively(__A       ,   __A       ,   __A       ,   __A       ,   __A      )
   return is_used
 return is_used


def 				lowercase_					(     __A			:							List[str]       ,   __A			:							Dict       ,   __A			:							Optional[Any]      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : Union[str, Any]						=[]
 lowercase       : str						=fairseq_model.state_dict()

 lowercase       : Tuple						=hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  lowercase       : List[str]						=False
  if "conv_layers" in name:
   load_conv_layer(
       __A       ,   __A       ,   __A       ,   __A       ,   hf_model.config.feat_extract_norm == '''group'''       ,   )
   lowercase       : Tuple						=True
  else:
   lowercase       : Dict						=load_wavaveca_layer(__A       ,   __A       ,   __A      )
  if not is_used:
   unused_weights.append(__A      )

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


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Tuple       ,   __A			:							List[Any]       ,   __A			:							Dict       ,   __A			:							Optional[int]      )							->   List[str]:



 """simple docstring"""




 lowercase       : List[str]						=full_name.split('''conv_layers.'''      )[-1]
 lowercase       : str						=name.split('''.'''      )
 lowercase       : Optional[int]						=int(items[0]      )
 lowercase       : Optional[int]						=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.'      )
   lowercase       : List[Any]						=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.'      )
   lowercase       : str						=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.'      )
   lowercase       : Union[str, Any]						=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.'      )
   lowercase       : List[Any]						=value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
 else:
  unused_weights.append(__A      )


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



 """simple docstring"""




 if config_path is not None:
  lowercase       : Union[str, Any]						=WavaVecaConfig.from_pretrained(__A      )
 else:
  lowercase       : Optional[Any]						=WavaVecaConfig()

 if is_seq_class:
  lowercase       : Optional[Any]						=read_txt_into_dict(__A      )
  lowercase       : int						=idalabel
  lowercase       : Dict						=WavaVecaForSequenceClassification(__A      )
  lowercase       : str						=WavaVecaFeatureExtractor(
      feature_size=1       ,   sampling_rate=1_6_0_0_0       ,   padding_value=0       ,   do_normalize=__A       ,   return_attention_mask=__A       ,   )
  feature_extractor.save_pretrained(__A      )

 elif is_finetuned:
  if dict_path:
   lowercase       : int						=Dictionary.load(__A      )

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

   # fairseq has the <pad> and <s> switched
   lowercase       : int						=0
   lowercase       : Tuple						=1
   with open(__A       ,   '''w'''       ,   encoding='''utf-8'''      ) as vocab_handle:
    json.dump(__A       ,   __A      )
   lowercase       : Any						=WavaVecaCTCTokenizer(
       __A       ,   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=__A       ,   )
   lowercase       : List[str]						=True if config.feat_extract_norm == '''layer''' else False
   lowercase       : str						=WavaVecaFeatureExtractor(
       feature_size=1       ,   sampling_rate=1_6_0_0_0       ,   padding_value=0       ,   do_normalize=__A       ,   return_attention_mask=__A       ,   )
   lowercase       : str						=WavaVecaProcessor(feature_extractor=__A       ,   tokenizer=__A      )
   processor.save_pretrained(__A      )

  lowercase       : Optional[int]						=WavaVecaForCTC(__A      )
 else:
  lowercase       : Tuple						=WavaVecaForPreTraining(__A      )

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

  lowercase	,		lowercase	,		lowercase       : Any						=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,   task=__A      )

 lowercase       : Any						=model[0].eval()

 recursively_load_weights(__A       ,   __A       ,   not is_finetuned      )

 hf_wavavec.save_pretrained(__A      )


if __name__ == "__main__":
      SCREAMING_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',
      )
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      SCREAMING_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"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Union[str, Any]    =						'beit'






       def __init__(   self			,					snake_case=8_192			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=224			,					snake_case=16			,					snake_case=3			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=True			,					snake_case=[3, 5, 7, 11]			,					snake_case=[1, 2, 3, 6]			,					snake_case=True			,					snake_case=0.4			,					snake_case=256			,					snake_case=1			,					snake_case=False			,					snake_case=255			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				str 			=				num_hidden_layers
        lowerCAmelCase__   :				Any 			=				num_attention_heads
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				str 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps

        lowerCAmelCase__   :				int 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				Dict 			=				num_channels
        lowerCAmelCase__   :				Optional[Any] 			=				use_mask_token
        lowerCAmelCase__   :				Dict 			=				use_absolute_position_embeddings
        lowerCAmelCase__   :				Any 			=				use_relative_position_bias
        lowerCAmelCase__   :				List[Any] 			=				use_shared_relative_position_bias
        lowerCAmelCase__   :				Dict 			=				layer_scale_init_value
        lowerCAmelCase__   :				Optional[int] 			=				drop_path_rate
        lowerCAmelCase__   :				Optional[Any] 			=				use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowerCAmelCase__   :				Optional[int] 			=				out_indices
        lowerCAmelCase__   :				List[Any] 			=				pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase__   :				List[Any] 			=				use_auxiliary_head
        lowerCAmelCase__   :				Optional[int] 			=				auxiliary_loss_weight
        lowerCAmelCase__   :				List[str] 			=				auxiliary_channels
        lowerCAmelCase__   :				Optional[Any] 			=				auxiliary_num_convs
        lowerCAmelCase__   :				Union[str, Any] 			=				auxiliary_concat_input
        lowerCAmelCase__   :				List[str] 			=				semantic_loss_ignore_index






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						version.parse('1.11'       )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 1e-4






