

'''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_fnet import FNetTokenizer
else:
  lowercase__         =      None

lowercase__         =      logging.get_logger(__name__)
lowercase__         =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase__         =      {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

lowercase__         =      {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowercase__         =      "▁"




class 	A_			(    a__							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     Dict  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     Optional[int]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     int  		=  ["input_ids", "token_type_ids"]
     UpperCAmelCase_						:     Union[str, Any]  		=  FNetTokenizer


     def __init__(		self			:					Any					,      lowercase_			:					int=None					,      lowercase_			:					str=None					,      lowercase_			:					Any=False					,      lowercase_			:					Tuple=True					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					Optional[int]="<unk>"					,      lowercase_			:					Optional[Any]="[SEP]"					,      lowercase_			:					Tuple="<pad>"					,      lowercase_			:					Union[str, Any]="[CLS]"					,      lowercase_			:					Any="[MASK]"					,      **lowercase_			:					Optional[Any]					,      )						->     Any:
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      UpperCAmelCase	:     List[str]						 =			(
          AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_					,      normalized=SCREAMING_SNAKE_CASE_	)
          if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_	)
          else mask_token
      )

      super().__init__(
          SCREAMING_SNAKE_CASE_					,      tokenizer_file=SCREAMING_SNAKE_CASE_					,      do_lower_case=SCREAMING_SNAKE_CASE_					,      remove_space=SCREAMING_SNAKE_CASE_					,      keep_accents=SCREAMING_SNAKE_CASE_					,      unk_token=SCREAMING_SNAKE_CASE_					,      sep_token=SCREAMING_SNAKE_CASE_					,      pad_token=SCREAMING_SNAKE_CASE_					,      cls_token=SCREAMING_SNAKE_CASE_					,      mask_token=SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_					,      )

      UpperCAmelCase	:     Any						 =			do_lower_case
      UpperCAmelCase	:     List[str]						 =			remove_space
      UpperCAmelCase	:     Dict						 =			keep_accents
      UpperCAmelCase	:     str						 =			vocab_file
      UpperCAmelCase	:     Optional[int]						 =			False if not self.vocab_file else True


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Tuple = None	)						->     List[int]:
      UpperCAmelCase	:     int						 =			[self.sep_token_id]
      UpperCAmelCase	:     Optional[int]						 =			[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        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[Any]					,      lowercase_			:					int = None	)						->     List[int]:
      UpperCAmelCase	:     Dict						 =			[self.sep_token_id]
      UpperCAmelCase	:     List[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	) * [0] + len(token_ids_a + sep	) * [1]




     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					List[str]					,      lowercase_			:					Optional[Any] = None	)						->     Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
      UpperCAmelCase	:     List[str]						 =			os.path.join(
          SCREAMING_SNAKE_CASE_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

      return (out_vocab_file,)







'''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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

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




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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




