import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_							=      {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    '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',
}
a_							=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




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






  for attribute in key.split("."					):
    __UpperCAmelCase						    =       getattr(A_						,	A_					)

  if weight_type is not None:
    __UpperCAmelCase						    =       getattr(A_						,	A_					).shape
  else:
    __UpperCAmelCase						    =       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						    =       value
  elif weight_type == "weight_g":
    __UpperCAmelCase						    =       value
  elif weight_type == "weight_v":
    __UpperCAmelCase						    =       value
  elif weight_type == "bias":
    __UpperCAmelCase						    =       value
  elif weight_type == "running_mean":
    __UpperCAmelCase						    =       value
  elif weight_type == "running_var":
    __UpperCAmelCase						    =       value
  elif weight_type == "num_batches_tracked":
    __UpperCAmelCase						    =       value
  elif weight_type == "inv_freq":
    __UpperCAmelCase						    =       value
  else:
    __UpperCAmelCase						    =       value

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




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : Union[str, Any]						,	A_   : Dict					)    ->  int:
  __UpperCAmelCase						    =       []
  __UpperCAmelCase						    =       fairseq_model.state_dict()

  __UpperCAmelCase						    =       hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase						    =       False
    if "conv_layers" in name:
      load_conv_layer(
          A_						,	A_						,	A_						,	A_						,	hf_model.config.feat_extract_norm == "group"						,	)
      __UpperCAmelCase						    =       True
    else:
      for key, mapped_key in MAPPING.items():
        __UpperCAmelCase						    =       '''wav2vec2_conformer.''' + 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						    =       True
          if "*" in mapped_key:
            __UpperCAmelCase						    =       name.split(A_					)[0].split("."					)[-2]
            __UpperCAmelCase						    =       mapped_key.replace("*"						,	A_					)
          if "pos_bias_u" in name:
            __UpperCAmelCase						    =       None
          elif "pos_bias_v" in name:
            __UpperCAmelCase						    =       None
          elif "weight_g" in name:
            __UpperCAmelCase						    =       '''weight_g'''
          elif "weight_v" in name:
            __UpperCAmelCase						    =       '''weight_v'''
          elif "bias" in name:
            __UpperCAmelCase						    =       '''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __UpperCAmelCase						    =       '''weight'''
          elif "running_mean" in name:
            __UpperCAmelCase						    =       '''running_mean'''
          elif "inv_freq" in name:
            __UpperCAmelCase						    =       '''inv_freq'''
          elif "running_var" in name:
            __UpperCAmelCase						    =       '''running_var'''
          elif "num_batches_tracked" in name:
            __UpperCAmelCase						    =       '''num_batches_tracked'''
          else:
            __UpperCAmelCase						    =       None
          set_recursively(A_						,	A_						,	A_						,	A_						,	A_					)
        continue
    if not is_used:
      unused_weights.append(A_					)

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




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Any						,	A_   : List[Any]						,	A_   : Tuple						,	A_   : Tuple					)    ->  Any:
  __UpperCAmelCase						    =       full_name.split("conv_layers."					)[-1]
  __UpperCAmelCase						    =       name.split("."					)
  __UpperCAmelCase						    =       int(items[0]					)
  __UpperCAmelCase						    =       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.'''					)
      __UpperCAmelCase						    =       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.'''					)
      __UpperCAmelCase						    =       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.'''					)
      __UpperCAmelCase						    =       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.'''					)
      __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : int						,	A_   : Optional[int]=None						,	A_   : Union[str, Any]=None						,	A_   : Optional[Any]=True					)    ->  List[Any]:






  if config_path is not None:
    __UpperCAmelCase						    =       WavaVecaConformerConfig.from_pretrained(A_						,	hidden_act="swish"					)
  else:
    __UpperCAmelCase						    =       WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    __UpperCAmelCase						    =       '''rotary'''

  if is_finetuned:
    if dict_path:
      __UpperCAmelCase						    =       Dictionary.load(A_					)

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

      # fairseq has the <pad> and <s> switched
      __UpperCAmelCase						    =       0
      __UpperCAmelCase						    =       1
      with open(A_						,	"w"						,	encoding="utf-8"					) as vocab_handle:
        json.dump(A_						,	A_					)
      __UpperCAmelCase						    =       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_						,	)
      __UpperCAmelCase						    =       True if config.feat_extract_norm == '''layer''' else False
      __UpperCAmelCase						    =       WavaVecaFeatureExtractor(
          feature_size=1						,	sampling_rate=1_60_00						,	padding_value=0						,	do_normalize=A_						,	return_attention_mask=A_						,	)
      __UpperCAmelCase						    =       WavaVecaProcessor(feature_extractor=A_						,	tokenizer=A_					)
      processor.save_pretrained(A_					)

    __UpperCAmelCase						    =       WavaVecaConformerForCTC(A_					)
  else:
    __UpperCAmelCase						    =       WavaVecaConformerForPreTraining(A_					)

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

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

  __UpperCAmelCase						    =       model[0].eval()

  recursively_load_weights(A_						,	A_						,	not is_finetuned					)

  hf_wavavec.save_pretrained(A_					)


if __name__ == "__main__":
       a_							=      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"""
       )
       a_							=      parser.parse_args()
       convert_wavaveca_conformer_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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


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

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

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""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

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



