
'''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()
UpperCAmelCase_							: Optional[Any] =							logging.get_logger(__name__)

UpperCAmelCase_							: int =							{
    '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_							: Any =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase					: int		    = {}
      with open(_lowerCAmelCase					,						"r"				) as file:
            for line_number, line in enumerate(_lowerCAmelCase				):
                  _lowerCamelCase					: Union[str, Any]		    = line.strip()
                  if line:
                        _lowerCamelCase					: int		    = line.split()
                        _lowerCamelCase					: Optional[Any]		    = line_number
                        _lowerCamelCase					: Tuple		    = words[0]
                        _lowerCamelCase					: Optional[int]		    = value
      return result







def 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      for attribute in key.split("."				):
            _lowerCamelCase					: Optional[int]		    = getattr(_lowerCAmelCase					,						_lowerCAmelCase				)

      _lowerCamelCase					: List[Any]		    = None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_lowerCAmelCase				):
                  _lowerCamelCase					: str		    = PARAM_MAPPING[full_name.split("."				)[-1]]
                  _lowerCamelCase					: Any		    = "param"

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

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

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







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_lowerCAmelCase				):
                  _lowerCamelCase					: str		    = PARAM_MAPPING[full_name.split("."				)[-1]]
                  _lowerCamelCase					: List[Any]		    = "param"

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

      _lowerCamelCase					: Union[str, Any]		    = value if "lm_head" in full_key else value[0]


UpperCAmelCase_							: Dict =							{
    '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_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     List[Any]=None					,						_lowerCAmelCase :     Optional[int]=None				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = False
      for key, mapped_key in MAPPING.items():
            _lowerCamelCase					: Dict		    = "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]:
                  _lowerCamelCase					: List[str]		    = True
                  if "*" in mapped_key:
                        _lowerCamelCase					: Optional[int]		    = name.split(_lowerCAmelCase				)[0].split("."				)[-2]
                        _lowerCamelCase					: Tuple		    = mapped_key.replace("*"					,						_lowerCAmelCase				)
                  if "weight_g" in name:
                        _lowerCamelCase					: List[Any]		    = "weight_g"
                  elif "weight_v" in name:
                        _lowerCamelCase					: int		    = "weight_v"
                  elif "bias" in name:
                        _lowerCamelCase					: List[Any]		    = "bias"
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowerCamelCase					: int		    = "weight"
                  else:
                        _lowerCamelCase					: Union[str, Any]		    = 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 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = []
      _lowerCamelCase					: str		    = fairseq_model.state_dict()

      _lowerCamelCase					: List[str]		    = hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCamelCase					: Union[str, Any]		    = False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						hf_model.config.feat_extract_norm == "group"					,						)
                  _lowerCamelCase					: str		    = True
            else:
                  _lowerCamelCase					: str		    = load_wavaveca_layer(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
            if not is_used:
                  unused_weights.append(_lowerCAmelCase				)

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







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




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



      """simple docstring"""




      if config_path is not None:
            _lowerCamelCase					: Tuple		    = WavaVecaConfig.from_pretrained(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[str]		    = WavaVecaConfig()

      if is_seq_class:
            _lowerCamelCase					: Tuple		    = read_txt_into_dict(_lowerCAmelCase				)
            _lowerCamelCase					: str		    = idalabel
            _lowerCamelCase					: int		    = WavaVecaForSequenceClassification(_lowerCAmelCase				)
            _lowerCamelCase					: Union[str, Any]		    = 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:
                  _lowerCamelCase					: List[str]		    = Dictionary.load(_lowerCAmelCase				)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _lowerCamelCase					: Optional[int]		    = target_dict.pad_index
                  _lowerCamelCase					: str		    = target_dict.bos_index
                  _lowerCamelCase					: Tuple		    = target_dict.eos_index
                  _lowerCamelCase					: Optional[Any]		    = len(target_dict.symbols				)
                  _lowerCamelCase					: Optional[int]		    = 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				)
                  _lowerCamelCase					: Optional[int]		    = target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase					: str		    = 0
                  _lowerCamelCase					: List[str]		    = 1
                  with open(_lowerCAmelCase					,						"w"					,						encoding="utf-8"				) as vocab_handle:
                        json.dump(_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: Tuple		    = 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					,						)
                  _lowerCamelCase					: Union[str, Any]		    = True if config.feat_extract_norm == "layer" else False
                  _lowerCamelCase					: Optional[int]		    = WavaVecaFeatureExtractor(
                      feature_size=1					,						sampling_rate=16000					,						padding_value=0					,						do_normalize=_lowerCAmelCase					,						return_attention_mask=_lowerCAmelCase					,						)
                  _lowerCamelCase					: Any		    = WavaVecaProcessor(feature_extractor=_lowerCAmelCase					,						tokenizer=_lowerCAmelCase				)
                  processor.save_pretrained(_lowerCAmelCase				)

            _lowerCamelCase					: List[str]		    = WavaVecaForCTC(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[str]		    = WavaVecaForPreTraining(_lowerCAmelCase				)

      if is_finetuned or is_seq_class:
            _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: Dict		    = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]					,						arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
      else:
            _lowerCamelCase					: str		    = argparse.Namespace(task="audio_pretraining"				)
            _lowerCamelCase					: Optional[Any]		    = fairseq.tasks.setup_task(_lowerCAmelCase				)

            _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: str		    = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,						task=_lowerCAmelCase				)

      _lowerCamelCase					: Optional[Any]		    = model[0].eval()

      recursively_load_weights(_lowerCAmelCase					,						_lowerCAmelCase					,						not is_finetuned				)

      hf_wavavec.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: List[Any] =							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_							: List[Any] =							parser.parse_args()

 UpperCAmelCase_							: Union[str, Any] =							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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :List[str]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
