





from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					:   int 							=						logging.get_logger(__name__)


UpperCAmelCase_					:   Union[str, Any] 							=						{
    """nielsr/canine-s""": 20_48,
}

# Unicode defines 1,114,112 total “codepoints”
UpperCAmelCase_					:   Union[str, Any] 							=						1_11_41_12

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
UpperCAmelCase_					:   int 							=						0

UpperCAmelCase_					:   int 							=						0XE000
UpperCAmelCase_					:   Union[str, Any] 							=						0XE001
UpperCAmelCase_					:   Tuple 							=						0XE002
UpperCAmelCase_					:   Tuple 							=						0XE003
UpperCAmelCase_					:   List[str] 							=						0XE004

# Maps special codepoints to human-readable names.
UpperCAmelCase_					:   List[Any] 							=						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: """[CLS]""",
    SEP: """[SEP]""",
    BOS: """[BOS]""",
    MASK: """[MASK]""",
    PAD: """[PAD]""",
    RESERVED: """[RESERVED]""",
}

# Maps special codepoint human-readable names to their codepoint values.
UpperCAmelCase_					:   Optional[int] 							=						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class      lowercase__							(							__lowerCamelCase  ):
  __UpperCamelCase	     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







  def __init__(	self    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=chr(UpperCAmelCase_						)    ,						_lowercase=False    ,						_lowercase=2_048    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       Optional[int]    =		AddedToken(UpperCAmelCase_    ,						lstrip=UpperCAmelCase_    ,						rstrip=UpperCAmelCase_						) if isinstance(UpperCAmelCase_    ,						UpperCAmelCase_						) else bos_token
       lowerCAmelCase_					:       List[str]    =		AddedToken(UpperCAmelCase_    ,						lstrip=UpperCAmelCase_    ,						rstrip=UpperCAmelCase_						) if isinstance(UpperCAmelCase_    ,						UpperCAmelCase_						) else eos_token
       lowerCAmelCase_					:       Tuple    =		AddedToken(UpperCAmelCase_    ,						lstrip=UpperCAmelCase_    ,						rstrip=UpperCAmelCase_						) if isinstance(UpperCAmelCase_    ,						UpperCAmelCase_						) else sep_token
       lowerCAmelCase_					:       Dict    =		AddedToken(UpperCAmelCase_    ,						lstrip=UpperCAmelCase_    ,						rstrip=UpperCAmelCase_						) if isinstance(UpperCAmelCase_    ,						UpperCAmelCase_						) else cls_token
       lowerCAmelCase_					:       int    =		AddedToken(UpperCAmelCase_    ,						lstrip=UpperCAmelCase_    ,						rstrip=UpperCAmelCase_						) if isinstance(UpperCAmelCase_    ,						UpperCAmelCase_						) else pad_token

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

       super().__init__(
           bos_token=UpperCAmelCase_    ,						eos_token=UpperCAmelCase_    ,						sep_token=UpperCAmelCase_    ,						cls_token=UpperCAmelCase_    ,						pad_token=UpperCAmelCase_    ,						mask_token=UpperCAmelCase_    ,						add_prefix_space=UpperCAmelCase_    ,						model_max_length=UpperCAmelCase_    ,						**UpperCAmelCase_    ,						)

       # Creates a mapping for looking up the IDs of special symbols.
       lowerCAmelCase_					:       Dict[str, int]    =		{}
       for codepoint, name in SPECIAL_CODEPOINTS.items():
            lowerCAmelCase_					:       List[Any]    =		codepoint

       # Creates a mapping for looking up the string forms of special symbol IDs.
       lowerCAmelCase_					:       Dict[int, str]    =		{
           codepoint: name for name, codepoint in self._special_codepoints.items()
       }

       lowerCAmelCase_					:       Optional[int]    =		UNICODE_VOCAB_SIZE
       lowerCAmelCase_					:       Union[str, Any]    =		len(self._special_codepoints						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return self._unicode_vocab_size







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return list(UpperCAmelCase_						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       try:
            return ord(UpperCAmelCase_						)
       except TypeError:
            raise ValueError(F'invalid token: \'{token}\''						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       try:
            if index in SPECIAL_CODEPOINTS:
                 return SPECIAL_CODEPOINTS[index]
            return chr(UpperCAmelCase_						)
       except TypeError:
            raise ValueError(F'invalid id: {index}'						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return "".join(UpperCAmelCase_						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       List[str]    =		[self.sep_token_id]
       lowerCAmelCase_					:       Union[str, Any]    =		[self.cls_token_id]

       lowerCAmelCase_					:       Dict    =		cls + token_ids_a + sep
       if token_ids_a is not None:
            result += token_ids_a + sep
       return result







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = False						):
       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=UpperCAmelCase_    ,						token_ids_a=UpperCAmelCase_    ,						already_has_special_tokens=UpperCAmelCase_						)

       lowerCAmelCase_					:       Optional[int]    =		[1] + ([0] * len(UpperCAmelCase_						)) + [1]
       if token_ids_a is not None:
            result += ([0] * len(UpperCAmelCase_						)) + [1]
       return result







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Tuple    =		[self.sep_token_id]
       lowerCAmelCase_					:       str    =		[self.cls_token_id]

       lowerCAmelCase_					:       Tuple    =		len(cls + token_ids_a + sep						) * [0]
       if token_ids_a is not None:
            result += len(token_ids_a + sep						) * [1]
       return result







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return ()







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


UpperCAmelCase_					:   Optional[Any] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   Any 							=						{
    """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      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







  def __init__(	self    ,						_lowercase=256    ,						_lowercase=1_280    ,						_lowercase=768    ,						_lowercase=1    ,						_lowercase=26    ,						_lowercase=8    ,						_lowercase=8    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase="kv"    ,						_lowercase=1    ,						_lowercase=1    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=True    ,						_lowercase=262    ,						_lowercase=2_048    ,						_lowercase=56    ,						_lowercase=[368, 496]    ,						_lowercase=16    ,						_lowercase=1_920    ,						_lowercase=16    ,						_lowercase=[1, 16, 224, 224]    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

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





class      lowercase__    (							__A  ):







  @property
  def 	UpperCAmelCase__		(	self						):
       if self.task == "multiple-choice":
            lowerCAmelCase_					:       Optional[Any]    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_					:       Optional[int]    =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""inputs""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return 1e-4







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 40    ,						):
       # 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
            lowerCAmelCase_					:       Union[str, Any]    =		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
            lowerCAmelCase_					:       int    =		preprocessor.num_special_tokens_to_add(_lowercase						)
            lowerCAmelCase_					:       int    =		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
            lowerCAmelCase_					:       Optional[int]    =		[""" """.join(["""a"""]						) * seq_length] * batch_size
            lowerCAmelCase_					:       Optional[Any]    =		dict(preprocessor(_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Optional[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
            lowerCAmelCase_					:       Any    =		compute_effective_axis_dimension(_lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch						)
            lowerCAmelCase_					:       str    =		self._generate_dummy_images(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       List[str]    =		dict(preprocessor(images=_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Tuple    =		inputs.pop("""pixel_values"""						)
            return inputs
       else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""						)
