
"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_big_bird import BigBirdTokenizer
else:
		UpperCamelCase_       :    Optional[int]						      =			None

UpperCamelCase_       :    Optional[Any]						      =			logging.get_logger(__name__)
UpperCamelCase_       :    Optional[int]						      =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_       :    Dict						      =			{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_       :    int						      =			{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


UpperCamelCase_       :    int						      =			'''▁'''



class __lowerCAmelCase					(       snake_case_   ):




			"""simple docstring"""







			snake_case						   =					VOCAB_FILES_NAMES
			snake_case						   =					PRETRAINED_VOCAB_FILES_MAP
			snake_case						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case						   =					BigBirdTokenizer
			snake_case						   =					['''input_ids''', '''attention_mask''']
			snake_case						   =					[]




			def __init__(					self    :       Union[str, Any]							, _snake_case    :       List[str]=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Optional[int]="<unk>"							, _snake_case    :       Optional[Any]="<s>"							, _snake_case    :       str="</s>"							, _snake_case    :       Optional[Any]="<pad>"							, _snake_case    :       Union[str, Any]="[SEP]"							, _snake_case    :       Dict="[MASK]"							, _snake_case    :       Dict="[CLS]"							, **_snake_case    :       str							, )   ->			List[str]:







								"""simple docstring"""
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else bos_token
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else eos_token
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else unk_token
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else pad_token
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else cls_token
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else sep_token

								# Mask token behave like a normal word, i.e. include the space before it
								A_								=    AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else mask_token

								super().__init__(
								    __snake_case							, tokenizer_file=__snake_case							, bos_token=__snake_case							, eos_token=__snake_case							, unk_token=__snake_case							, sep_token=__snake_case							, pad_token=__snake_case							, cls_token=__snake_case							, mask_token=__snake_case							, **__snake_case							, )

								A_								=    vocab_file
								A_								=    False if not self.vocab_file else True




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







								"""simple docstring"""
								A_								=    [self.sep_token_id]
								A_								=    [self.cls_token_id]
								if token_ids_a is None:
													return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep




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







								"""simple docstring"""

								if already_has_special_tokens:
													if token_ids_a is not None:
																		raise ValueError(
																		    "You should not supply a second sequence if the provided sequence of "
																		    "ids is already formatted with special tokens for the model."						)
													return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is None:
													return [1] + ([0] * len(__snake_case						)) + [1]
								return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								A_								=    [self.sep_token_id]
								A_								=    [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						) * [0] + len(token_ids_a + sep						) * [1]






			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       str							, _snake_case    :       Optional[str] = None						)   ->			Tuple[str]:







								"""simple docstring"""
								if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."						)

								if not os.path.isdir(__snake_case						):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
													return
								A_								=    os.path.join(
								    __snake_case							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

								if os.path.abspath(self.vocab_file						) != os.path.abspath(__snake_case						):
													copyfile(self.vocab_file							, __snake_case						)

								return (out_vocab_file,)











'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






