
'''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


lowercase    :							str      =					logging.get_logger(__name__)

lowercase    :							int      =					{
    "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      (					_lowerCamelCase						):
							__lowercase      		=       """perceiver"""


							def __init__(       self    ,   lowerCAmelCase_=2_56    ,   lowerCAmelCase_=12_80    ,   lowerCAmelCase_=7_68    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=26    ,   lowerCAmelCase_=8    ,   lowerCAmelCase_=8    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_="kv"    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=2_62    ,   lowerCAmelCase_=20_48    ,   lowerCAmelCase_=56    ,   lowerCAmelCase_=[3_68, 4_96]    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=19_20    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=[1, 16, 2_24, 2_24]    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )

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


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return 1E-4




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = -1    ,   lowerCAmelCase_ = -1    ,   lowerCAmelCase_ = -1    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 3    ,   lowerCAmelCase_ = 40    ,   lowerCAmelCase_ = 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   						=		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   						=		preprocessor.num_special_tokens_to_add(lowerCAmelCase_  )
									_snake_case   						=		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   						=		[' '.join(['a']  ) * seq_length] * batch_size
									_snake_case   						=		dict(preprocessor(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_  )  )
									_snake_case   						=		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   						=		compute_effective_axis_dimension(lowerCAmelCase_    ,   fixed_dimension=OnnxConfig.default_fixed_batch  )
									_snake_case   						=		self._generate_dummy_images(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		dict(preprocessor(images=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_  )  )
									_snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A = 1_000_000	)       ->	int:
	_snake_case   						=		limit + 1
	_snake_case   						=		[0] * limit
	for first_term in range(1			,				__A	):
		for n in range(__A			,				__A			,				__A	):
			_snake_case   						=		first_term + n / first_term
			if common_difference % 4:  # d must be divisble by 4
				continue
			else:
				common_difference /= 4
				if (
				    first_term > common_difference
				    and first_term < 4 * common_difference
				):  # since x,y,z are positive integers
					frequency[n] += 1  # so z>0 and a>d ,also 4d<a

	_snake_case   						=		sum(1 for x in frequency[1:limit] if x == 10	)

	return count


if __name__ == "__main__":
					print(F'''{solution() = }''')

