





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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'perceiver'

 def __init__(  self,		A_=256,		A_=1280,		A_=768,		A_=1,		A_=26,		A_=8,		A_=8,		A_=None,		A_=None,		A_="kv",		A_=1,		A_=1,		A_="gelu",		A_=0.1,		A_=0.02,		A_=1E-12,		A_=True,		A_=262,		A_=2048,		A_=56,		A_=[368, 496],		A_=16,		A_=1920,		A_=16,		A_=[1, 16, 224, 224],		**A_,		)	->       Optional[Any]:
    super().__init__(**A_		)

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

class     snake_case_		(   a							):






 '''simple docstring'''





 @property
 def  __UpperCAmelCase						(  self		)	->       Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
       UpperCAmelCase__						={0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCAmelCase__						={0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("inputs", dynamic_axis),
            ("attention_mask", dynamic_axis),
        ]		)

 @property
 def  __UpperCAmelCase						(  self		)	->       float:
    return 1E-4






 def  __UpperCAmelCase						(  self,		A_,		A_ = -1,		A_ = -1,		A_ = -1,		A_ = False,		A_ = None,		A_ = 3,		A_ = 40,		A_ = 40,		)	->       Mapping[str, Any]:
    # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

    if isinstance(A_,		A_		):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase__						=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
       UpperCAmelCase__						=preprocessor.num_special_tokens_to_add(A_		)
       UpperCAmelCase__						=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
       UpperCAmelCase__						=[" ".join(["a"]		) * seq_length] * batch_size
       UpperCAmelCase__						=dict(preprocessor(A_,		return_tensors=A_		)		)
       UpperCAmelCase__						=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
       UpperCAmelCase__						=compute_effective_axis_dimension(A_,		fixed_dimension=OnnxConfig.default_fixed_batch		)
       UpperCAmelCase__						=self._generate_dummy_images(A_,		A_,		A_,		A_		)
       UpperCAmelCase__						=dict(preprocessor(images=A_,		return_tensors=A_		)		)
       UpperCAmelCase__						=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 				_UpperCAmelCase			(    A = 20     ):




   '''simple docstring'''

   UpperCAmelCase__						=2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
   # 2, 3,...
   UpperCAmelCase__						=n // 2

   return int(factorial(A     ) / (factorial(A     ) * factorial(n - k     ))     )


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution(20))
    else:
        try:
            UpperCamelCase_												=	int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number.')



