

'''simple docstring'''






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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =						{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class 			lowerCAmelCase__     (					lowerCAmelCase__						):




     """simple docstring"""





     __UpperCamelCase									= """perceiver"""







     def __init__(		self						:	Optional[Any]      ,						A__						:	Union[str, Any]=2_5_6      ,						A__						:	Tuple=1_2_8_0      ,						A__						:	Optional[Any]=7_6_8      ,						A__						:	List[Any]=1      ,						A__						:	Tuple=2_6      ,						A__						:	Dict=8      ,						A__						:	Dict=8      ,						A__						:	int=None      ,						A__						:	Optional[Any]=None      ,						A__						:	str="kv"      ,						A__						:	Union[str, Any]=1      ,						A__						:	Tuple=1      ,						A__						:	Optional[Any]="gelu"      ,						A__						:	Optional[int]=0.1      ,						A__						:	int=0.02      ,						A__						:	List[str]=1E-12      ,						A__						:	Any=True      ,						A__						:	int=2_6_2      ,						A__						:	int=2_0_4_8      ,						A__						:	str=5_6      ,						A__						:	Dict=[3_6_8, 4_9_6]      ,						A__						:	Any=1_6      ,						A__						:	int=1_9_2_0      ,						A__						:	List[Any]=1_6      ,						A__						:	Optional[Any]=[1, 1_6, 2_2_4, 2_2_4]      ,						**A__						:	List[Any]      ,						)       ->			str:
          '''simple docstring'''




          super().__init__(**__a	)

          a__   :      Tuple     =						num_latents
          a__   :      Union[str, Any]     =						d_latents
          a__   :      Dict     =						d_model
          a__   :      List[Any]     =						num_blocks
          a__   :      Union[str, Any]     =						num_self_attends_per_block
          a__   :      Tuple     =						num_self_attention_heads
          a__   :      Optional[Any]     =						num_cross_attention_heads
          a__   :      List[str]     =						qk_channels
          a__   :      Any     =						v_channels
          a__   :      Any     =						cross_attention_shape_for_attention
          a__   :      str     =						self_attention_widening_factor
          a__   :      Optional[int]     =						cross_attention_widening_factor
          a__   :      List[Any]     =						hidden_act
          a__   :      Tuple     =						attention_probs_dropout_prob
          a__   :      Optional[int]     =						initializer_range
          a__   :      List[str]     =						layer_norm_eps
          a__   :      List[Any]     =						use_query_residual
          # masked language modeling attributes
          a__   :      Optional[Any]     =						vocab_size
          a__   :      Union[str, Any]     =						max_position_embeddings
          # image classification attributes
          a__   :      Any     =						image_size
          # flow attributes
          a__   :      Optional[Any]     =						train_size
          # multimodal autoencoding attributes
          a__   :      int     =						num_frames
          a__   :      Optional[int]     =						audio_samples_per_frame
          a__   :      Dict     =						samples_per_patch
          a__   :      List[str]     =						output_shape






class 			lowerCAmelCase__     (					lowerCAmelCase__						):




     """simple docstring"""





     @property
     def 			__lowerCAmelCase	(		self						:	List[str]	)       ->			Any:
          '''simple docstring'''




          if self.task == "multiple-choice":
               a__   :      List[Any]     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               a__   :      Any     =						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''inputs''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]	)







     @property
     def 			__lowerCAmelCase	(		self						:	str	)       ->			str:
          '''simple docstring'''




          return 1E-4







     def 			__lowerCAmelCase	(		self						:	int      ,						A__						:	Optional[Any]      ,						A__						:	int = -1      ,						A__						:	Optional[int] = -1      ,						A__						:	Union[str, Any] = -1      ,						A__						:	Tuple = False      ,						A__						:	int = None      ,						A__						:	Any = 3      ,						A__						:	Any = 4_0      ,						A__						:	str = 4_0      ,						)       ->			Dict:
          '''simple docstring'''




          if isinstance(__a      ,						__a	):
               # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
               a__   :      Union[str, Any]     =						compute_effective_axis_dimension(
                   __a      ,						fixed_dimension=OnnxConfig.default_fixed_batch      ,						num_token_to_add=0	)
               # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
               a__   :      Union[str, Any]     =						preprocessor.num_special_tokens_to_add(__a	)
               a__   :      List[Any]     =						compute_effective_axis_dimension(
                   __a      ,						fixed_dimension=OnnxConfig.default_fixed_sequence      ,						num_token_to_add=__a	)
               # Generate dummy inputs according to compute batch and sequence
               a__   :      Optional[Any]     =						[''' '''.join(['''a''']	) * seq_length] * batch_size
               a__   :      List[str]     =						dict(preprocessor(__a      ,						return_tensors=__a	)	)
               a__   :      str     =						inputs.pop('''input_ids'''	)
               return inputs
          elif isinstance(__a      ,						__a	) and preprocessor.model_input_names[0] == "pixel_values":
               # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
               a__   :      str     =						compute_effective_axis_dimension(__a      ,						fixed_dimension=OnnxConfig.default_fixed_batch	)
               a__   :      Optional[Any]     =						self._generate_dummy_images(__a      ,						__a      ,						__a      ,						__a	)
               a__   :      List[str]     =						dict(preprocessor(images=__a      ,						return_tensors=__a	)	)
               a__   :      List[Any]     =						inputs.pop('''pixel_values'''	)
               return inputs
          else:
               raise ValueError(
                   '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''	)




"""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_xlnet import XLNetTokenizer
else:
	A	:  Optional[Any]            =     None


A	:  Any            =     logging.get_logger(__name__)

A	:  Union[str, Any]            =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A	:  List[Any]            =     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A	:  Union[str, Any]            =     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A	:  Optional[int]            =     "▁"

# Segments (not really needed)
A	:  str            =     0
A	:  Dict            =     1
A	:  List[str]            =     2
A	:  Tuple            =     3
A	:  Tuple            =     4
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       Optional[int]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Dict							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       List[Any]							 ="""left"""
							__UpperCAmelCase		:       List[str]							 =XLNetTokenizer

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=False       ,  __a=True       ,  __a=False       ,  __a="<s>"       ,  __a="</s>"       ,  __a="<unk>"       ,  __a="<sep>"       ,  __a="<pad>"       ,  __a="<cls>"       ,  __a="<mask>"       ,  __a=["<eop>", "<eod>"]       ,  **__a       ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else mask_token

										super().__init__(
										    vocab_file=__a       ,  tokenizer_file=__a       ,  do_lower_case=__a       ,  remove_space=__a       ,  keep_accents=__a       ,  bos_token=__a       ,  eos_token=__a       ,  unk_token=__a       ,  sep_token=__a       ,  pad_token=__a       ,  cls_token=__a       ,  mask_token=__a       ,  additional_special_tokens=__a       ,  **__a       ,  )

										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     do_lower_case
										__lowerCAmelCase							       =     remove_space
										__lowerCAmelCase							       =     keep_accents
										__lowerCAmelCase							       =     vocab_file
										__lowerCAmelCase							       =     False if not self.vocab_file else True

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [2]

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



							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										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(__a      ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
													return
										__lowerCAmelCase							       =     os.path.join(
										    __a       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

										return (out_vocab_file,)




