





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


lowercase_		:				Any				=						logging.get_logger(__name__)

lowercase_		:				Tuple				=						{
    '''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_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	"perceiver"







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





										'''simple docstring'''







										super().__init__(**lowerCamelCase_					)

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



class 			lowercase				(							a_     ):



						"""simple docstring"""

						@property
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										if self.task == "multiple-choice":
														_snake_case :			List[Any]  						=   {0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
														_snake_case :			Optional[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			:		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 = 40 ,				lowerCamelCase_			:		int = 40 ,				):





										'''simple docstring'''







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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






