






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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",
    "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",
}
UpperCamelCase        =				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 				A		(					lowercase__  :  Any ,		lowercase__  :  List[Any] ,		lowercase__  :  List[Any] ,		lowercase__  :  str ,		lowercase__  :  List[str]  )		-> int:
	for attribute in key.split("""."""  ):
		UpperCamelCase__ :Union[str, Any]						=      getattr(lowercase__ ,		lowercase__  )

	if weight_type is not None:
		UpperCamelCase__ :Optional[int]						=      getattr(lowercase__ ,		lowercase__  ).shape
	else:
		UpperCamelCase__ :Tuple						=      hf_pointer.shape

	assert hf_shape == value.shape, (
	    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__ :Optional[Any]						=      value
	elif weight_type == "weight_g":
		UpperCamelCase__ :List[Any]						=      value
	elif weight_type == "weight_v":
		UpperCamelCase__ :Tuple						=      value
	elif weight_type == "bias":
		UpperCamelCase__ :int						=      value
	else:
		UpperCamelCase__ :Tuple						=      value

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




def 				A		(					lowercase__  :  int ,		lowercase__  :  Dict  )		-> Dict:
	UpperCamelCase__ :int						=      []
	UpperCamelCase__ :Union[str, Any]						=      fairseq_model.state_dict()

	UpperCamelCase__ :str						=      hf_model.feature_extractor
	UpperCamelCase__ :int						=      hf_model.adapter

	for name, value in fairseq_dict.items():
		UpperCamelCase__ :Optional[int]						=      False
		if "conv_layers" in name:
			load_conv_layer(
			    lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		hf_model.config.feat_extract_norm == """group""" ,		)
			UpperCamelCase__ :str						=      True
		elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]  ):
			load_adapter(lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__  )
			UpperCamelCase__ :Dict						=      True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
					UpperCamelCase__ :List[str]						=      True
					if "*" in mapped_key:
						UpperCamelCase__ :str						=      name.split(lowercase__  )[0].split("""."""  )[-2]
						UpperCamelCase__ :Tuple						=      mapped_key.replace("""*""" ,		lowercase__  )
					if "weight_g" in name:
						UpperCamelCase__ :Tuple						=      """weight_g"""
					elif "weight_v" in name:
						UpperCamelCase__ :Union[str, Any]						=      """weight_v"""
					elif "bias" in name:
						UpperCamelCase__ :int						=      """bias"""
					elif "weight" in name:
						UpperCamelCase__ :int						=      """weight"""
					else:
						UpperCamelCase__ :Tuple						=      None
					set_recursively(lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__  )
				continue
		if not is_used:
			unused_weights.append(lowercase__  )

	logger.warning(f"""Unused weights: {unused_weights}"""  )




def 				A		(					lowercase__  :  Union[str, Any] ,		lowercase__  :  Tuple ,		lowercase__  :  Optional[int] ,		lowercase__  :  str ,		lowercase__  :  int  )		-> Optional[int]:
	UpperCamelCase__ :Optional[Any]						=      full_name.split("""conv_layers."""  )[-1]
	UpperCamelCase__ :Optional[Any]						=      name.split("""."""  )
	UpperCamelCase__ :Tuple						=      int(items[0]  )
	UpperCamelCase__ :Tuple						=      int(items[1]  )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			UpperCamelCase__ :List[Any]						=      value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			UpperCamelCase__ :List[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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			UpperCamelCase__ :Dict						=      value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			UpperCamelCase__ :Dict						=      value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
	else:
		unused_weights.append(lowercase__  )




def 				A		(					lowercase__  :  int ,		lowercase__  :  Tuple ,		lowercase__  :  Optional[Any] ,		lowercase__  :  str  )		-> Any:
	UpperCamelCase__ :List[Any]						=      full_name.split("""adaptor."""  )[-1]
	UpperCamelCase__ :Union[str, Any]						=      name.split("""."""  )

	if items[1].isdigit():
		UpperCamelCase__ :Optional[Any]						=      int(items[1]  )
	else:
		UpperCamelCase__ :Dict						=      None

	if "adaptor" not in full_name:
		if "proj_ln" in full_name:
			# has to be layer norm
			if "bias" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.bias.data.shape
				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
				UpperCamelCase__ :Optional[int]						=      value
				logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""  )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.weight.data.shape
				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
				UpperCamelCase__ :Union[str, Any]						=      value
		else:
			# has to be projection layer
			if "bias" in name:
				assert (
				    value.shape == adapter.proj.bias.data.shape
				), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
				UpperCamelCase__ :List[Any]						=      value
				logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""  )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj.weight.data.shape
				), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
				UpperCamelCase__ :Any						=      value
				logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
	elif isinstance(lowercase__ ,		lowercase__  ):
		if "bias" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.bias.data.shape
			), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
			UpperCamelCase__ :Dict						=      value
			logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		elif "weight" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.weight.data.shape
			), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
			UpperCamelCase__ :Optional[int]						=      value
			logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
	else:
		unused_weights.append(lowercase__  )




def 				A		(					lowercase__  :  int  )		-> List[str]:
	UpperCamelCase__				,		UpperCamelCase__ :Dict						=      emb.weight.shape
	UpperCamelCase__ :Any						=      nn.Linear(lowercase__ ,		lowercase__ ,		bias=lowercase__  )
	UpperCamelCase__ :int						=      emb.weight.data
	return lin_layer




@torch.no_grad()
def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  List[str] ,		lowercase__  :  Any ,		lowercase__  :  str ,		lowercase__  :  Optional[Any] ,		lowercase__  :  Union[str, Any] ,		lowercase__  :  Any ,		lowercase__  :  List[str] ,		lowercase__  :  Union[str, Any] ,		lowercase__  :  Optional[Any] ,		lowercase__  :  List[Any] ,		)		-> Union[str, Any]:
	UpperCamelCase__ :Union[str, Any]						=      WavaVecaConfig.from_pretrained(
	    lowercase__ ,		add_adapter=lowercase__ ,		adapter_stride=lowercase__ ,		adapter_kernel_size=lowercase__ ,		use_auth_token=lowercase__ ,		output_hidden_size=lowercase__ ,		)
	UpperCamelCase__ :List[str]						=      MBartConfig.from_pretrained(lowercase__  )

	# load model
	UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path] ,		arg_overrides={
	        """config_yaml""": config_yaml_path,
	        """data""": """/""".join(dict_path.split("""/"""  )[:-1]  ),
	        """w2v_path""": checkpoint_path,
	        """load_pretrained_decoder_from""": None,
	    } ,		)
	UpperCamelCase__ :Tuple						=      model[0].eval()

	# load feature extractor
	UpperCamelCase__ :Union[str, Any]						=      WavaVecaFeatureExtractor.from_pretrained(lowercase__ ,		use_auth_token=lowercase__  )

	# set weights for wav2vec2 encoder
	UpperCamelCase__ :List[Any]						=      WavaVecaModel(lowercase__  )

	recursively_load_weights_wavaveca(model.encoder ,		lowercase__  )

	# load decoder weights
	UpperCamelCase__ :List[str]						=      MBartForCausalLM(lowercase__  )
	UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,		strict=lowercase__  )
	logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""  )
	logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""  )

	UpperCamelCase__ :Tuple						=      SpeechEncoderDecoderModel(encoder=lowercase__ ,		decoder=lowercase__  )
	UpperCamelCase__ :List[Any]						=      False

	UpperCamelCase__ :Optional[Any]						=      MBartaaTokenizer(lowercase__  )
	tokenizer.save_pretrained(lowercase__  )

	UpperCamelCase__ :Optional[int]						=      hf_wavavec.config.to_dict()
	UpperCamelCase__ :List[Any]						=      tokenizer.pad_token_id
	UpperCamelCase__ :Optional[int]						=      tokenizer.bos_token_id
	UpperCamelCase__ :Union[str, Any]						=      tokenizer.eos_token_id
	UpperCamelCase__ :List[str]						=      """mbart50"""
	UpperCamelCase__ :List[Any]						=      """wav2vec2"""

	UpperCamelCase__ :int						=      tokenizer.eos_token_id
	UpperCamelCase__ :Tuple						=      25_0004
	UpperCamelCase__ :Optional[Any]						=      tokenizer.eos_token_id

	UpperCamelCase__ :Any						=      SpeechEncoderDecoderConfig.from_dict(lowercase__  )

	hf_wavavec.save_pretrained(lowercase__  )
	feature_extractor.save_pretrained(lowercase__  )


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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
							parser.add_argument(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-xls-r-1b",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/mbart-large-50-one-to-many-mmt",
							    type=str,
							    help="Path to hf decoder checkpoint config",
							)
							parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
							parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
							parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
							parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
							parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

							UpperCamelCase        =				parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      float     ,		**__snake_case  :      Optional[Any]					)			->							str:
											__magic_name__:	Optional[Any]							=       feature_size
											__magic_name__:	List[Any]							=       sampling_rate
											__magic_name__:	Tuple							=       padding_value

											__magic_name__:	int							=       kwargs.pop("""padding_side"""     ,		"""right"""					)
											__magic_name__:	Optional[Any]							=       kwargs.pop("""return_attention_mask"""     ,		__snake_case					)

											super().__init__(**__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]     ,		__snake_case  :      Union[bool, str, PaddingStrategy] = True     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		)			->							BatchFeature:
											# If we have a list of dicts, let's convert it in a dict of lists
											# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
											if isinstance(__snake_case     ,		(list, tuple)					) and isinstance(processed_features[0]     ,		(dict, BatchFeature)					):
																	__magic_name__:	Union[str, Any]							=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

											# The model's main input name, usually `input_values`, has be passed for padding
											if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
																	    F' to this method that includes {self.model_input_names[0]}, but you provided'
																	    F' {list(processed_features.keys()					)}'					)

											__magic_name__:	Any							=       processed_features[self.model_input_names[0]]
											__magic_name__:	Tuple							=       (
											    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
											)

											if len(__snake_case					) == 0:
																	if return_attention_mask:
																							__magic_name__:	Optional[Any]							=       []
																	return processed_features

											# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
											# and rebuild them afterwards if no return_tensors is specified
											# Note that we lose the specific device the tensor may be on for PyTorch

											__magic_name__:	Tuple							=       required_input[0]
											if isinstance(__snake_case     ,		(list, tuple)					):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	__magic_name__:	str							=       0
																	while len(required_input[index]					) == 0:
																							index += 1
																	if index < len(__snake_case					):
																							__magic_name__:	Optional[int]							=       required_input[index][0]

											if return_tensors is None:
																	if is_tf_tensor(__snake_case					):
																							__magic_name__:	List[str]							=       """tf"""
																	elif is_torch_tensor(__snake_case					):
																							__magic_name__:	Any							=       """pt"""
																	elif isinstance(__snake_case     ,		(int, float, list, tuple, np.ndarray)					):
																							__magic_name__:	int							=       """np"""
																	else:
																							raise ValueError(
																							    F'type of {first_element} unknown: {type(__snake_case					)}. '
																							    """Should be one of a python, numpy, pytorch or tensorflow object."""					)

											for key, value in processed_features.items():
																	if isinstance(value[0]     ,		(int, float)					):
																							__magic_name__:	List[str]							=       to_numpy(__snake_case					)
																	else:
																							__magic_name__:	Any							=       [to_numpy(__snake_case					) for v in value]

        # Convert padding_strategy in PaddingStrategy
											__magic_name__:	str							=       self._get_padding_strategies(padding=__snake_case     ,		max_length=__snake_case					)

											__magic_name__:	str							=       processed_features[self.model_input_names[0]]

											__magic_name__:	str							=       len(__snake_case					)
											if not all(len(__snake_case					) == batch_size for v in processed_features.values()					):
																	raise ValueError("""Some items in the output dictionary have a different batch size than others."""					)

											__magic_name__:	List[Any]							=       []
											for i in range(__snake_case					):
																	__magic_name__:	List[Any]							=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	__magic_name__:	List[Any]							=       self._truncate(
																	    __snake_case     ,		max_length=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		truncation=__snake_case     ,		)
																	truncated_inputs.append(__snake_case					)

											if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	__magic_name__:	Union[str, Any]							=       max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
																	__magic_name__:	Union[str, Any]							=       PaddingStrategy.MAX_LENGTH

											__magic_name__:	List[str]							=       {}
											for i in range(__snake_case					):
																	# padding
																	__magic_name__:	str							=       self._pad(
																	    truncated_inputs[i]     ,		max_length=__snake_case     ,		padding_strategy=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_attention_mask=__snake_case     ,		)

																	for key, value in outputs.items():
																							if key not in batch_outputs:
																													__magic_name__:	Optional[Any]							=       []
																							if value.dtype is np.dtype(np.floataa					):
																													__magic_name__:	Any							=       value.astype(np.floataa					)
																							batch_outputs[key].append(__snake_case					)

											return BatchFeature(__snake_case     ,		tensor_type=__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Union[Dict[str, np.ndarray], BatchFeature]     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		)			->							dict:
											__magic_name__:	List[str]							=       processed_features[self.model_input_names[0]]

											if padding_strategy == PaddingStrategy.LONGEST:
																	__magic_name__:	List[Any]							=       len(__snake_case					)

											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	__magic_name__:	List[str]							=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											__magic_name__:	str							=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case					) < max_length

											if return_attention_mask and "attention_mask" not in processed_features:
																	__magic_name__:	int							=       np.ones(len(__snake_case					)     ,		dtype=np.intaa					)

											if needs_to_be_padded:
																	__magic_name__:	str							=       max_length - len(__snake_case					)
																	if self.padding_side == "right":
																							if return_attention_mask:
																													__magic_name__:	List[Any]							=       np.pad(
																													    processed_features["""attention_mask"""]     ,		(0, difference)					)
																							__magic_name__:	Union[str, Any]							=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																							__magic_name__:	int							=       np.pad(
																							    __snake_case     ,		__snake_case     ,		"""constant"""     ,		constant_values=self.padding_value					)
																	elif self.padding_side == "left":
																							if return_attention_mask:
																													__magic_name__:	Dict							=       np.pad(
																													    processed_features["""attention_mask"""]     ,		(difference, 0)					)
																							__magic_name__:	Optional[Any]							=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																							__magic_name__:	int							=       np.pad(
																							    __snake_case     ,		__snake_case     ,		"""constant"""     ,		constant_values=self.padding_value					)
																	else:
																							raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

											return processed_features

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Union[Dict[str, np.ndarray], BatchFeature]     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		)			->							int:
											if not truncation:
																	return processed_features
											elif truncation and max_length is None:
																	raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""					)

											__magic_name__:	Dict							=       processed_features[self.model_input_names[0]]

											# find `max_length` that fits `pad_to_multiple_of`
											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	__magic_name__:	Optional[Any]							=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											__magic_name__:	Tuple							=       len(__snake_case					) > max_length

											if needs_to_be_truncated:
																	__magic_name__:	Any							=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																							__magic_name__:	List[Any]							=       processed_features["""attention_mask"""][:max_length]

											return processed_features






					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      int=False     ,		__snake_case  :      Tuple=None					)			->							Optional[Any]:

											# Get padding strategy
											if padding is not False:
																	if padding is True:
																							__magic_name__:	Optional[Any]							=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	Tuple							=       PaddingStrategy(__snake_case					)
																	elif isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	Dict							=       padding
											else:
																	__magic_name__:	int							=       PaddingStrategy.DO_NOT_PAD

											# Set max length if needed
											if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																							raise ValueError(
																							    F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'					)

        # Test if we have a padding value
											if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
																	    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""					)

											return padding_strategy



