





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


snake_case_							=			logging.get_logger(__name__)


snake_case_							=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_							=			{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

snake_case_							=			{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}
class        snake_case_		(			_A):
  lowerCamelCase    :Optional[int]						=     VOCAB_FILES_NAMES
  lowerCamelCase    :int						=     PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase    :List[Any]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase    :str						=     LEDTokenizer
  lowerCamelCase    :Dict						=     ["input_ids", "attention_mask"]







  def __init__(		self			,     __lowercase=None			,     __lowercase=None			,     __lowercase=None			,     __lowercase="replace"			,     __lowercase="<s>"			,     __lowercase="</s>"			,     __lowercase="</s>"			,     __lowercase="<s>"			,     __lowercase="<unk>"			,     __lowercase="<pad>"			,     __lowercase="<mask>"			,     __lowercase=False			,     __lowercase=True			,     **__lowercase			,     )				->   Optional[int]:
    super().__init__(
        __lowercase			,     __lowercase			,     tokenizer_file=__lowercase			,     errors=__lowercase			,     bos_token=__lowercase			,     eos_token=__lowercase			,     sep_token=__lowercase			,     cls_token=__lowercase			,     unk_token=__lowercase			,     pad_token=__lowercase			,     mask_token=__lowercase			,     add_prefix_space=__lowercase			,     trim_offsets=__lowercase			,     **__lowercase			,     )

    lowerCamelCase   : Optional[int]								=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
    if pre_tok_state.get('''add_prefix_space'''			,     __lowercase   ) != add_prefix_space:
      lowerCamelCase   : List[Any]								=getattr(__lowercase			,     pre_tok_state.pop('''type'''   )   )
      lowerCamelCase   : Optional[Any]								=add_prefix_space
      lowerCamelCase   : str								=pre_tok_class(**__lowercase   )

    lowerCamelCase   : str								=add_prefix_space

    # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
    lowerCamelCase   : Dict								='''post_processor'''
    lowerCamelCase   : int								=getattr(self.backend_tokenizer			,     __lowercase			,     __lowercase   )
    if tokenizer_component_instance:
      lowerCamelCase   : str								=json.loads(tokenizer_component_instance.__getstate__()   )

      # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
      if "sep" in state:
        lowerCamelCase   : Union[str, Any]								=tuple(state['''sep''']   )
      if "cls" in state:
        lowerCamelCase   : Optional[Any]								=tuple(state['''cls''']   )

      lowerCamelCase   : Dict								=False

      if state.get('''add_prefix_space'''			,     __lowercase   ) != add_prefix_space:
        lowerCamelCase   : List[Any]								=add_prefix_space
        lowerCamelCase   : List[str]								=True

      if state.get('''trim_offsets'''			,     __lowercase   ) != trim_offsets:
        lowerCamelCase   : List[Any]								=trim_offsets
        lowerCamelCase   : Dict								=True

      if changes_to_apply:
        lowerCamelCase   : List[Any]								=getattr(__lowercase			,     state.pop('''type'''   )   )
        lowerCamelCase   : Optional[Any]								=component_class(**__lowercase   )
        setattr(self.backend_tokenizer			,     __lowercase			,     __lowercase   )







  @property
  # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
  def 			__lowercase   (		self   )				->   str:
    if self._mask_token is None:
      if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''   )
      return None
    return str(self._mask_token   )







  @mask_token.setter
  def 			__lowercase   (		self			,     __lowercase   )				->   Tuple:
    lowerCamelCase   : Optional[Any]								=AddedToken(__lowercase			,     lstrip=__lowercase			,     rstrip=__lowercase   ) if isinstance(__lowercase			,     __lowercase   ) else value
    lowerCamelCase   : Tuple								=value







  def 			__lowercase   (		self			,     *__lowercase			,     **__lowercase   )				->   BatchEncoding:
    lowerCamelCase   : Union[str, Any]								=kwargs.get('''is_split_into_words'''			,     __lowercase   )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          '''to use it with pretokenized inputs.'''   )

    return super()._batch_encode_plus(*__lowercase			,     **__lowercase   )







  def 			__lowercase   (		self			,     *__lowercase			,     **__lowercase   )				->   BatchEncoding:
    lowerCamelCase   : List[str]								=kwargs.get('''is_split_into_words'''			,     __lowercase   )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          '''to use it with pretokenized inputs.'''   )

    return super()._encode_plus(*__lowercase			,     **__lowercase   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   Tuple[str]:
    lowerCamelCase   : Tuple								=self._tokenizer.model.save(__lowercase			,     name=__lowercase   )
    return tuple(__lowercase   )







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase=None   )				->   Tuple:
    lowerCamelCase   : Tuple								=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
      return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   List[int]:
    lowerCamelCase   : str								=[self.sep_token_id]
    lowerCamelCase   : str								=[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep   ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None			,     __lowercase = PaddingStrategy.DO_NOT_PAD			,     __lowercase = None			,     __lowercase = None			,     )				->   dict:
    lowerCamelCase   : Tuple								=super()._pad(
        encoded_inputs=__lowercase			,     max_length=__lowercase			,     padding_strategy=__lowercase			,     pad_to_multiple_of=__lowercase			,     return_attention_mask=__lowercase			,     )

    # Load from model defaults
    if return_attention_mask is None:
      lowerCamelCase   : List[str]								='''attention_mask''' in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
      lowerCamelCase   : str								=encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      lowerCamelCase   : Optional[Any]								=len(encoded_inputs['''global_attention_mask''']   ) != len(__lowercase   )

      if needs_to_be_padded:
        lowerCamelCase   : Optional[int]								=len(__lowercase   ) - len(encoded_inputs['''global_attention_mask''']   )

        if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          lowerCamelCase   : Optional[Any]								=(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
        elif self.padding_side == "left":
          lowerCamelCase   : int								=[-1] * difference + encoded_inputs[
              '''global_attention_mask'''
          ]
        else:
          raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

    return encoded_inputs








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     "timesformer"







		def __init__(		self			,     __lowercase=2_2_4			,     __lowercase=1_6			,     __lowercase=3			,     __lowercase=8			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=0.0_2			,     __lowercase=1e-6			,     __lowercase=True			,     __lowercase="divided_space_time"			,     __lowercase=0			,     **__lowercase			,     )				->   List[Any]:
				super().__init__(**__lowercase   )

				lowerCamelCase   : int								=image_size
				lowerCamelCase   : List[str]								=patch_size
				lowerCamelCase   : Union[str, Any]								=num_channels
				lowerCamelCase   : str								=num_frames

				lowerCamelCase   : Dict								=hidden_size
				lowerCamelCase   : int								=num_hidden_layers
				lowerCamelCase   : Dict								=num_attention_heads
				lowerCamelCase   : Dict								=intermediate_size
				lowerCamelCase   : Union[str, Any]								=hidden_act
				lowerCamelCase   : str								=hidden_dropout_prob
				lowerCamelCase   : Union[str, Any]								=attention_probs_dropout_prob
				lowerCamelCase   : List[Any]								=initializer_range
				lowerCamelCase   : List[Any]								=layer_norm_eps
				lowerCamelCase   : List[str]								=qkv_bias

				lowerCamelCase   : Tuple								=attention_type
				lowerCamelCase   : List[Any]								=drop_path_rate

