





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "EncodecFeatureExtractor"
						__magic_name__:			List[str]       =       ("T5Tokenizer", "T5TokenizerFast")






						def __init__(      self      :      Dict		,   _A      :      Union[str, Any]		,   _A      :      List[Any]	)							->    str:







							"""simple docstring"""


							super().__init__(_A		,   _A	)
							snake_case_		:			Tuple											=	self.feature_extractor
							snake_case_		:			Union[str, Any]											=	False






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Any=None		,   _A      :      str=None		,   _A      :      Optional[int]=True	)							->    Tuple:







							"""simple docstring"""


							return self.tokenizer.get_decoder_prompt_ids(task=_A		,   language=_A		,   no_timestamps=_A	)






						def __call__(      self      :      Tuple		,   *_A      :      str		,   **_A      :      str	)							->    Union[str, Any]:







							"""simple docstring"""


							if self._in_target_context_manager:
								return self.current_processor(*_A		,   **_A	)

							snake_case_		:			List[str]											=	kwargs.pop('audio'		,   _A	)
							snake_case_		:			Optional[int]											=	kwargs.pop('sampling_rate'		,   _A	)
							snake_case_		:			Optional[Any]											=	kwargs.pop('text'		,   _A	)
							if len(_A	) > 0:
								snake_case_		:			Optional[Any]											=	args[0]
								snake_case_		:			Any											=	args[1:]

							if audio is None and text is None:
								raise ValueError('You need to specify either an `audio` or `text` input to process.'	)

							if text is not None:
								snake_case_		:			List[str]											=	self.tokenizer(_A		,   **_A	)

							if audio is not None:
								snake_case_		:			Union[str, Any]											=	self.feature_extractor(_A		,   *_A		,   sampling_rate=_A		,   **_A	)

							if audio is None:
								return inputs

							elif text is None:
								return audio_inputs

							else:
								snake_case_		:			Tuple											=	audio_inputs['input_values']
								if "padding_mask" in audio_inputs:
									snake_case_		:			Tuple											=	audio_inputs['padding_mask']
								return inputs






						def UpperCAmelCase_				(      self      :      Optional[int]		,   *_A      :      Union[str, Any]		,   **_A      :      Optional[int]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	kwargs.pop('audio'		,   _A	)
							snake_case_		:			Tuple											=	kwargs.pop('padding_mask'		,   _A	)

							if len(_A	) > 0:
								snake_case_		:			int											=	args[0]
								snake_case_		:			List[Any]											=	args[1:]

							if audio_values is not None:
								return self._decode_audio(_A		,   padding_mask=_A	)
							else:
								return self.tokenizer.batch_decode(*_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   *_A      :      Union[str, Any]		,   **_A      :      Tuple	)							->    int:







							"""simple docstring"""


							return self.tokenizer.decode(*_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Optional[Any]		,   _A      :      Optional = None	)							->    List[np.ndarray]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	to_numpy(_A	)
							snake_case_						,snake_case_						,snake_case_		:			Tuple											=	audio_values.shape

							if padding_mask is None:
								return list(_A	)

							snake_case_		:			List[Any]											=	to_numpy(_A	)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							snake_case_		:			Union[str, Any]											=	seq_len - padding_mask.shape[-1]
							snake_case_		:			List[str]											=	1 - self.feature_extractor.padding_value
							snake_case_		:			Tuple											=	np.pad(_A		,   ((0, 0), (0, difference))		,   'constant'		,   constant_values=_A	)

							snake_case_		:			Dict											=	audio_values.tolist()
							for i in range(_A	):
								snake_case_		:			Optional[Any]											=	np.asarray(audio_values[i]	)[
								    padding_mask[i][None, :] != self.feature_extractor.padding_value
								]
								snake_case_		:			Any											=	sliced_audio.reshape(_A		,   -1	)

							return audio_values








import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Load checkpoint
	snake_case_		:			Union[str, Any]											=	torch.load(__a   , map_location='cpu'   )

	snake_case_		:			Union[str, Any]											=	chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	snake_case_		:			str											=	{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			snake_case_		:			Tuple											=	v
		else:
			snake_case_		:			Dict											=	v

	snake_case_		:			Tuple											=	chkpt['params']
	snake_case_		:			List[Any]											=	{n: v for n, v in config.items() if not isinstance(__a   , (torch.FloatTensor, numpy.ndarray)   )}

	snake_case_		:			Optional[int]											=	chkpt['dico_word2id']
	snake_case_		:			List[str]											=	{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   , ''   ): i for s, i in vocab.items()}

	# Save pytorch-model
	snake_case_		:			List[str]											=	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	snake_case_		:			Dict											=	pytorch_dump_folder_path + '/' + CONFIG_NAME
	snake_case_		:			Any											=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(__a   , __a   )

	print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )

	print(f"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

