

'''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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''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 ):
       lowerCAmelCase_												=			"perceiver"







       def __init__(				self      :			List[Any]    ,   __lowercase      :			Union[str, Any]=256    ,   __lowercase      :			List[Any]=1280    ,   __lowercase      :			Optional[Any]=768    ,   __lowercase      :			Any=1    ,   __lowercase      :			Optional[int]=26    ,   __lowercase      :			Dict=8    ,   __lowercase      :			Optional[Any]=8    ,   __lowercase      :			Optional[Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str="kv"    ,   __lowercase      :			str=1    ,   __lowercase      :			List[Any]=1    ,   __lowercase      :			str="gelu"    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]=0.0_2    ,   __lowercase      :			Optional[int]=1E-12    ,   __lowercase      :			Union[str, Any]=True    ,   __lowercase      :			List[str]=262    ,   __lowercase      :			int=2048    ,   __lowercase      :			Union[str, Any]=56    ,   __lowercase      :			str=[368, 496]    ,   __lowercase      :			Union[str, Any]=16    ,   __lowercase      :			str=1920    ,   __lowercase      :			Optional[int]=16    ,   __lowercase      :			Optional[int]=[1, 16, 224, 224]    ,   **__lowercase      :			str    ,   ):







            """simple docstring"""







            super().__init__(**SCREAMING_SNAKE_CASE__   )

            __lowercase      		=num_latents
            __lowercase      		=d_latents
            __lowercase      		=d_model
            __lowercase      		=num_blocks
            __lowercase      		=num_self_attends_per_block
            __lowercase      		=num_self_attention_heads
            __lowercase      		=num_cross_attention_heads
            __lowercase      		=qk_channels
            __lowercase      		=v_channels
            __lowercase      		=cross_attention_shape_for_attention
            __lowercase      		=self_attention_widening_factor
            __lowercase      		=cross_attention_widening_factor
            __lowercase      		=hidden_act
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=initializer_range
            __lowercase      		=layer_norm_eps
            __lowercase      		=use_query_residual
            # masked language modeling attributes
            __lowercase      		=vocab_size
            __lowercase      		=max_position_embeddings
            # image classification attributes
            __lowercase      		=image_size
            # flow attributes
            __lowercase      		=train_size
            # multimodal autoencoding attributes
            __lowercase      		=num_frames
            __lowercase      		=audio_samples_per_frame
            __lowercase      		=samples_per_patch
            __lowercase      		=output_shape






class 	lowerCAmelCase  (   _lowerCAmelCase ):







       @property
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







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







       @property
       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            return 1E-4







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Any    ,   __lowercase      :			Union[str, Any] = -1    ,   __lowercase      :			List[Any] = -1    ,   __lowercase      :			Union[str, Any] = -1    ,   __lowercase      :			Optional[int] = False    ,   __lowercase      :			Any = None    ,   __lowercase      :			List[Any] = 3    ,   __lowercase      :			List[str] = 40    ,   __lowercase      :			List[str] = 40    ,   ):







            """simple docstring"""







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






	"""simple docstring"""





	if not isinstance(UpperCAmelCase_,	UpperCAmelCase_     ):
		raise ValueError("Input must be an integer"     )
	if input_num <= 0:
		raise ValueError("Input must be positive"     )
	return sum(
	    divisor for divisor in range(1,	input_num // 2 + 1     ) if input_num % divisor == 0     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




