import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Tuple=10_24			,					__UpperCamelCase   :	List[Any]=10_24			,					__UpperCamelCase   :	Optional[int]=False			,					**__UpperCamelCase   :	Optional[Any]     )							-> List[str]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(A__     )
		SCREAMING_SNAKE_CASE__      							=				SeqaSeqDataset(A__			,					A__			,					A__			,					A__			,					type_path="""train"""			,					**A__     )
		SCREAMING_SNAKE_CASE__      							=				tok.pad_token_id

		def get_lens(__UpperCamelCase   :	Dict     ):
				SCREAMING_SNAKE_CASE__      							=				tqdm(
				    DataLoader(A__			,					batch_size=5_12			,					num_workers=8			,					shuffle=A__			,					collate_fn=ds.collate_fn     )			,					desc=str(ds.len_file     )			,					)
				SCREAMING_SNAKE_CASE__      							=				[]
				for batch in dl:
						SCREAMING_SNAKE_CASE__      							=				batch["""input_ids"""].ne(A__     ).sum(1     ).tolist()
						SCREAMING_SNAKE_CASE__      							=				batch["""labels"""].ne(A__     ).sum(1     ).tolist()
						if consider_target:
								for src, tgt in zip(A__			,					A__     ):
										max_lens.append(max(A__			,					A__     )     )
						else:
								max_lens.extend(A__     )
				return max_lens

		SCREAMING_SNAKE_CASE__      							=				get_lens(A__     )
		SCREAMING_SNAKE_CASE__      							=				SeqaSeqDataset(A__			,					A__			,					A__			,					A__			,					type_path="""val"""			,					**A__     )
		SCREAMING_SNAKE_CASE__      							=				get_lens(A__     )
		pickle_save(A__			,					train_ds.len_file     )
		pickle_save(A__			,					val_ds.len_file     )


if __name__ == "__main__":
			fire.Fire(save_len_file)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :			Optional[int]							       =		{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[Any]							       =		['''WhisperTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''WhisperForConditionalGeneration''',
       '''WhisperModel''',
       '''WhisperPreTrainedModel''',
       '''WhisperForAudioClassification''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Tuple							       =		[
       '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWhisperForConditionalGeneration''',
       '''TFWhisperModel''',
       '''TFWhisperPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			List[Any]							       =		[
       '''FlaxWhisperForConditionalGeneration''',
       '''FlaxWhisperModel''',
       '''FlaxWhisperPreTrainedModel''',
       '''FlaxWhisperForAudioClassification''',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			Tuple							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
