





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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """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    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[Any]      					="""perceiver"""







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int=2_56      ,				UpperCamelCase      :					str=12_80      ,				UpperCamelCase      :					List[str]=7_68      ,				UpperCamelCase      :					Tuple=1      ,				UpperCamelCase      :					Optional[int]=26      ,				UpperCamelCase      :					Tuple=8      ,				UpperCamelCase      :					Dict=8      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[Any]="kv"      ,				UpperCamelCase      :					List[Any]=1      ,				UpperCamelCase      :					Optional[Any]=1      ,				UpperCamelCase      :					Optional[Any]="gelu"      ,				UpperCamelCase      :					Tuple=0.1      ,				UpperCamelCase      :					int=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					Any=2_62      ,				UpperCamelCase      :					Any=20_48      ,				UpperCamelCase      :					Optional[Any]=56      ,				UpperCamelCase      :					Any=[3_68, 4_96]      ,				UpperCamelCase      :					List[Any]=16      ,				UpperCamelCase      :					List[str]=19_20      ,				UpperCamelCase      :					int=16      ,				UpperCamelCase      :					Any=[1, 16, 2_24, 2_24]      ,				**UpperCamelCase      :					Tuple      ,				):


    '''simple docstring'''





    super().__init__(**UpperCamelCase							)

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




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    return 1e-4







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					Optional[TensorType] = None      ,				UpperCamelCase      :					int = 3      ,				UpperCamelCase      :					int = 40      ,				UpperCamelCase      :					int = 40      ,				):


    '''simple docstring'''





    if isinstance(UpperCamelCase      ,				UpperCamelCase							):
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _snake_case   :				Tuple														=	compute_effective_axis_dimension(
          UpperCamelCase      ,				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
      _snake_case   :				List[str]														=	preprocessor.num_special_tokens_to_add(UpperCamelCase							)
      _snake_case   :				Union[str, Any]														=	compute_effective_axis_dimension(
          UpperCamelCase      ,				fixed_dimension=OnnxConfig.default_fixed_sequence      ,				num_token_to_add=UpperCamelCase							)
      # Generate dummy inputs according to compute batch and sequence
      _snake_case   :				List[str]														=	[' '.join(['a']							) * seq_length] * batch_size
      _snake_case   :				List[Any]														=	dict(preprocessor(UpperCamelCase      ,				return_tensors=UpperCamelCase							)							)
      _snake_case   :				List[str]														=	inputs.pop('input_ids'							)
      return inputs
    elif isinstance(UpperCamelCase      ,				UpperCamelCase							) 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
      _snake_case   :				Optional[int]														=	compute_effective_axis_dimension(UpperCamelCase      ,				fixed_dimension=OnnxConfig.default_fixed_batch							)
      _snake_case   :				Optional[int]														=	self._generate_dummy_images(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)
      _snake_case   :				Any														=	dict(preprocessor(images=UpperCamelCase      ,				return_tensors=UpperCamelCase							)							)
      _snake_case   :				Optional[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.'							)











def     lowerCamelCase_	(      lowerCAmelCase:	bytes				)->		str:
  return "".join([hex(lowerCAmelCase				)[2:].zfill(2				).upper() for byte in list(lowerCAmelCase				)]				)


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		bytes:
  # Check data validity, following RFC3548
  # https://www.ietf.org/rfc/rfc3548.txt
  if (len(lowerCAmelCase				) % 2) != 0:
    raise ValueError(
        'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'				)
  # Check the character set - the standard base16 alphabet
  # is uppercase according to RFC3548 section 6
  if not set(lowerCAmelCase				) <= set('0123456789ABCDEF'				):
    raise ValueError(
        'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'				)
  # For every two hexadecimal digits (= a byte), turn it into an integer.
  # Then, string the result together into bytes, and return it.
  return bytes(int(data[i] + data[i + 1]    ,							16				) for i in range(0    ,							len(lowerCAmelCase				)    ,							2				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




