


'''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


__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 				UpperCAmelCase			(			__a						):
 UpperCAmelCase				=					"perceiver"


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

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




class 				UpperCAmelCase			(			__a						):


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):


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


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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[TensorType] = None			,       __lowerCamelCase							:	int = 3			,       __lowerCamelCase							:	int = 4_0			,       __lowerCamelCase							:	int = 4_0			,       ):


    if isinstance(lowercase_			,       lowercase_ ):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase__	:int      =      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__	:Optional[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__	:Optional[Any]      =      [''' '''.join(['''a'''] ) * seq_length] * batch_size
       UpperCAmelCase__	:int      =      dict(preprocessor(lowercase_			,       return_tensors=lowercase_ ) )
       UpperCAmelCase__	:Dict      =      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__	:Union[str, Any]      =      compute_effective_axis_dimension(lowercase_			,       fixed_dimension=OnnxConfig.default_fixed_batch )
       UpperCAmelCase__	:List[Any]      =      self._generate_dummy_images(lowercase_			,       lowercase_			,       lowercase_			,       lowercase_ )
       UpperCAmelCase__	:List[str]      =      dict(preprocessor(images=lowercase_			,       return_tensors=lowercase_ ) )
       UpperCAmelCase__	:Union[str, 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'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if number < 0:
    raise ValueError("""number must not be negative"""						)
  return number & (number - 1) == 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

