





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """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 SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     str	 =    "perceiver"






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







							"""simple docstring"""


							super().__init__(**_A		)

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






						@property
						def UpperCAmelCase_			( self : Tuple		)							->      Mapping[str, Mapping[int, str]]:







							"""simple docstring"""


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






						@property
						def UpperCAmelCase_			( self : Optional[Any]		)							->      float:







							"""simple docstring"""


							return 1E-4






						def UpperCAmelCase_			( self : List[Any]						,				_A : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]						,				_A : int = -1						,				_A : int = -1						,				_A : int = -1						,				_A : bool = False						,				_A : Optional[TensorType] = None						,				_A : int = 3						,				_A : int = 40						,				_A : int = 40						,				)							->      Mapping[str, Any]:







							"""simple docstring"""


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








from decimal import Decimal, getcontext
from math import ceil, factorial


def 							SCREAMING_SNAKE_CASE__  (      __a				):

	if not isinstance(__a				,	__a				):
		raise TypeError('Undefined for non-integers'				)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'				)

	snake_case_						:     Dict     =		precision
	snake_case_						:     str     =		ceil(precision / 14				)
	snake_case_						:     str     =		42_68_80 * Decimal(1_00_05				).sqrt()
	snake_case_						:     Tuple     =		1
	snake_case_						:     int     =		13_59_14_09
	snake_case_						:     Tuple     =		Decimal(__a				)
	for k in range(1				,	__a				):
		snake_case_						:     List[Any]     =		factorial(6 * k				) // (factorial(3 * k				) * factorial(__a				) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term				) / exponential_term
	return str(constant_term / partial_sum				)[:-1]


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						50
						print(F'''The first {n} digits of pi is: {pi(n)}''')

