




'''simple docstring'''




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


UpperCAmelCase_												=   logging.get_logger(__name__)


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

UpperCAmelCase_												=   {
    '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',
    },
}

UpperCAmelCase_												=   {
    'allenai/led-base-16384': 1_6_3_8_4,
}

class     lowerCAmelCase_ (			__A ):







  '''simple docstring'''







  lowerCAmelCase_					:       Union[str, Any]					  =    VOCAB_FILES_NAMES
  lowerCAmelCase_					:       str					  =    PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					:       Optional[Any]					  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					:       str					  =    LEDTokenizer
  lowerCAmelCase_					:       str					  =    ['input_ids', 'attention_mask']


  def __init__(    self     :						int    ,		_UpperCAmelCase     :						str=None    ,		_UpperCAmelCase     :						List[Any]=None    ,		_UpperCAmelCase     :						Optional[Any]=None    ,		_UpperCAmelCase     :						Optional[int]="replace"    ,		_UpperCAmelCase     :						List[str]="<s>"    ,		_UpperCAmelCase     :						List[Any]="</s>"    ,		_UpperCAmelCase     :						Optional[int]="</s>"    ,		_UpperCAmelCase     :						Tuple="<s>"    ,		_UpperCAmelCase     :						Optional[int]="<unk>"    ,		_UpperCAmelCase     :						Any="<pad>"    ,		_UpperCAmelCase     :						List[Any]="<mask>"    ,		_UpperCAmelCase     :						Optional[Any]=False    ,		_UpperCAmelCase     :						str=True    ,		**_UpperCAmelCase     :						List[Any]    ,		):






   """simple docstring"""






   super().__init__(
       snake_case_    ,		snake_case_    ,		tokenizer_file=snake_case_    ,		errors=snake_case_    ,		bos_token=snake_case_    ,		eos_token=snake_case_    ,		sep_token=snake_case_    ,		cls_token=snake_case_    ,		unk_token=snake_case_    ,		pad_token=snake_case_    ,		mask_token=snake_case_    ,		add_prefix_space=snake_case_    ,		trim_offsets=snake_case_    ,		**snake_case_    ,		)

   UpperCAmelCase__      				=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
   if pre_tok_state.get("""add_prefix_space"""    ,		snake_case_			) != add_prefix_space:
    UpperCAmelCase__      				=     getattr(snake_case_    ,		pre_tok_state.pop("""type"""			)			)
    UpperCAmelCase__      				=     add_prefix_space
    UpperCAmelCase__      				=     pre_tok_class(**snake_case_			)

   UpperCAmelCase__      				=     add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   UpperCAmelCase__      				=     """post_processor"""
   UpperCAmelCase__      				=     getattr(self.backend_tokenizer    ,		snake_case_    ,		snake_case_			)
   if tokenizer_component_instance:
    UpperCAmelCase__      				=     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:
     UpperCAmelCase__      				=     tuple(state["""sep"""]			)
    if "cls" in state:
     UpperCAmelCase__      				=     tuple(state["""cls"""]			)

    UpperCAmelCase__      				=     False

    if state.get("""add_prefix_space"""    ,		snake_case_			) != add_prefix_space:
     UpperCAmelCase__      				=     add_prefix_space
     UpperCAmelCase__      				=     True

    if state.get("""trim_offsets"""    ,		snake_case_			) != trim_offsets:
     UpperCAmelCase__      				=     trim_offsets
     UpperCAmelCase__      				=     True

    if changes_to_apply:
     UpperCAmelCase__      				=     getattr(snake_case_    ,		state.pop("""type"""			)			)
     UpperCAmelCase__      				=     component_class(**snake_case_			)
     setattr(self.backend_tokenizer    ,		snake_case_    ,		snake_case_			)


  @property
  # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[Any]			):






   """simple docstring"""






   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 				SCREAMING_SNAKE_CASE__      (    self     :						Dict    ,		_UpperCAmelCase     :						Optional[Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     AddedToken(snake_case_    ,		lstrip=snake_case_    ,		rstrip=snake_case_			) if isinstance(snake_case_    ,		snake_case_			) else value
   UpperCAmelCase__      				=     value


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any    ,		*_UpperCAmelCase     :						int    ,		**_UpperCAmelCase     :						Tuple			):






   """simple docstring"""






   UpperCAmelCase__      				=     kwargs.get("""is_split_into_words"""    ,		snake_case_			)

   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(*snake_case_    ,		**snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]    ,		*_UpperCAmelCase     :						Optional[Any]    ,		**_UpperCAmelCase     :						Union[str, Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     kwargs.get("""is_split_into_words"""    ,		snake_case_			)

   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(*snake_case_    ,		**snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Dict    ,		_UpperCAmelCase     :						Dict    ,		_UpperCAmelCase     :						Dict = None			):






   """simple docstring"""






   UpperCAmelCase__      				=     self._tokenizer.model.save(snake_case_    ,		name=snake_case_			)
   return tuple(snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any    ,		_UpperCAmelCase     :						Optional[Any]    ,		_UpperCAmelCase     :						Dict=None			):






   """simple docstring"""






   UpperCAmelCase__      				=     [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 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]    ,		_UpperCAmelCase     :						Any    ,		_UpperCAmelCase     :						Tuple = None			):






   """simple docstring"""






   UpperCAmelCase__      				=     [self.sep_token_id]
   UpperCAmelCase__      				=     [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 				SCREAMING_SNAKE_CASE__      (    self     :						List[Any]    ,		_UpperCAmelCase     :						Tuple    ,		_UpperCAmelCase     :						Union[str, Any] = None    ,		_UpperCAmelCase     :						Union[str, Any] = PaddingStrategy.DO_NOT_PAD    ,		_UpperCAmelCase     :						Dict = None    ,		_UpperCAmelCase     :						str = None    ,		):






   """simple docstring"""






   UpperCAmelCase__      				=     super()._pad(
       encoded_inputs=snake_case_    ,		max_length=snake_case_    ,		padding_strategy=snake_case_    ,		pad_to_multiple_of=snake_case_    ,		return_attention_mask=snake_case_    ,		)

   # Load from model defaults
   if return_attention_mask is None:
    UpperCAmelCase__      				=     """attention_mask""" in self.model_input_names

   if return_attention_mask and "global_attention_mask" in encoded_inputs:
    UpperCAmelCase__      				=     encoded_inputs[self.model_input_names[0]]
    # `global_attention_mask` need to have the same length as other (sequential) inputs.
    UpperCAmelCase__      				=     len(encoded_inputs["""global_attention_mask"""]			) != len(snake_case_			)

    if needs_to_be_padded:
     UpperCAmelCase__      				=     len(snake_case_			) - 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`
      UpperCAmelCase__      				=     (
          encoded_inputs["""global_attention_mask"""] + [-1] * difference
      )
     elif self.padding_side == "left":
      UpperCAmelCase__      				=     [-1] * difference + encoded_inputs[
          """global_attention_mask"""
      ]
     else:
      raise ValueError("""Invalid padding strategy:""" + str(self.padding_side			)			)

   return encoded_inputs

"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





