






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()
UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						{
    "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",
}
UpperCAmelCase__							  =						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def 		_a       ( a					:Optional[Any]					)				->     Optional[Any]:
 a						    = {}
 with open(a      ,				'''r'''					) as file:
  for line_number, line in enumerate(a					):
   a						    = line.strip()
   if line:
    a						    = line.split()
    a						    = line_number
    a						    = words[0]
    a						    = value
 return result
def 		_a       ( a					:int      ,				a					:str      ,				a					:int      ,				a					:str      ,				a					:Union[str, Any]					)				->     Optional[int]:
 for attribute in key.split('''.'''					):
  a						    = getattr(a      ,				a					)

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

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

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

 logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""					)
def 		_a       ( a					:List[Any]      ,				a					:Optional[int]      ,				a					:List[str]      ,				a					:int      ,				a					:Dict					)				->     Tuple:
 a						    = None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(a					):
   a						    = PARAM_MAPPING[full_name.split('''.'''					)[-1]]
   a						    = '''param'''

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

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


UpperCAmelCase__							  =						{
    "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 		_a       ( a					:int      ,				a					:List[str]      ,				a					:Any=None      ,				a					:List[Any]=None					)				->     Union[str, Any]:
 a						    = False
 for key, mapped_key in MAPPING.items():
  a						    = '''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]:
   a						    = True
   if "*" in mapped_key:
    a						    = name.split(a					)[0].split('''.'''					)[-2]
    a						    = mapped_key.replace('''*'''      ,				a					)
   if "weight_g" in name:
    a						    = '''weight_g'''
   elif "weight_v" in name:
    a						    = '''weight_v'''
   elif "bias" in name:
    a						    = '''bias'''
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    a						    = '''weight'''
   else:
    a						    = 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 		_a       ( a					:Tuple      ,				a					:Union[str, Any]      ,				a					:Any					)				->     Optional[int]:
 a						    = []
 a						    = fairseq_model.state_dict()

 a						    = hf_model.wavaveca.feature_extractor

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

 logger.warning(F"""Unused weights: {unused_weights}"""					)
def 		_a       ( a					:Tuple      ,				a					:Union[str, Any]      ,				a					:Optional[Any]      ,				a					:Tuple      ,				a					:Union[str, Any]					)				->     Dict:
 a						    = full_name.split('''conv_layers.'''					)[-1]
 a						    = name.split('''.'''					)
 a						    = int(items[0]					)
 a						    = 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."""					)
   a						    = 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."""					)
   a						    = 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."""					)
   a						    = 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."""					)
   a						    = 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 		_a       ( a					:Optional[int]      ,				a					:Tuple      ,				a					:Any=None      ,				a					:Tuple=None      ,				a					:List[Any]=True      ,				a					:List[Any]=False					)				->     int:
 if config_path is not None:
  a						    = WavaVecaConfig.from_pretrained(a					)
 else:
  a						    = WavaVecaConfig()

 if is_seq_class:
  a						    = read_txt_into_dict(a					)
  a						    = idalabel
  a						    = WavaVecaForSequenceClassification(a					)
  a						    = WavaVecaFeatureExtractor(
      feature_size=1      ,				sampling_rate=16_000      ,				padding_value=0      ,				do_normalize=a      ,				return_attention_mask=a      ,				)
  feature_extractor.save_pretrained(a					)

 elif is_finetuned:
  if dict_path:
   a						    = Dictionary.load(a					)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   a						    = target_dict.pad_index
   a						    = target_dict.bos_index
   a						    = target_dict.eos_index
   a						    = len(target_dict.symbols					)
   a						    = 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					)
   a						    = target_dict.indices

   # fairseq has the <pad> and <s> switched
   a						    = 0
   a						    = 1
   with open(a      ,				'''w'''      ,				encoding='''utf-8'''					) as vocab_handle:
    json.dump(a      ,				a					)
   a						    = 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      ,				)
   a						    = True if config.feat_extract_norm == '''layer''' else False
   a						    = WavaVecaFeatureExtractor(
       feature_size=1      ,				sampling_rate=16_000      ,				padding_value=0      ,				do_normalize=a      ,				return_attention_mask=a      ,				)
   a						    = WavaVecaProcessor(feature_extractor=a      ,				tokenizer=a					)
   processor.save_pretrained(a					)

  a						    = WavaVecaForCTC(a					)
 else:
  a						    = WavaVecaForPreTraining(a					)

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

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

 a						    = model[0].eval()

 recursively_load_weights(a      ,				a      ,				not is_finetuned					)

 hf_wavavec.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						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",
     )
     UpperCAmelCase__							  =						parser.parse_args()

     UpperCAmelCase__							  =						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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__							  =						{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
     from .processing_layoutxlm import LayoutXLMProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm import LayoutXLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


