


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


snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Optional[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 	snake_case_       (lowercase__				):
					UpperCAmelCase__ :   Optional[int]   				=      '''perceiver'''
					def __init__(				self       :Any      ,__snake_case       :Dict=2_56      ,__snake_case       :List[Any]=12_80      ,__snake_case       :Optional[Any]=7_68      ,__snake_case       :Optional[Any]=1      ,__snake_case       :Optional[int]=26      ,__snake_case       :str=8      ,__snake_case       :Union[str, Any]=8      ,__snake_case       :List[Any]=None      ,__snake_case       :Union[str, Any]=None      ,__snake_case       :Union[str, Any]="kv"      ,__snake_case       :Tuple=1      ,__snake_case       :int=1      ,__snake_case       :Dict="gelu"      ,__snake_case       :Optional[Any]=0.1      ,__snake_case       :Tuple=0.02      ,__snake_case       :Dict=1E-12      ,__snake_case       :Optional[Any]=True      ,__snake_case       :int=2_62      ,__snake_case       :Tuple=20_48      ,__snake_case       :str=56      ,__snake_case       :Tuple=[3_68, 4_96]      ,__snake_case       :List[str]=16      ,__snake_case       :Any=19_20      ,__snake_case       :Tuple=16      ,__snake_case       :List[Any]=[1, 16, 2_24, 2_24]      ,**__snake_case       :Tuple      ,) ->   str:
												super().__init__(**_a				)

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







class 	snake_case_       (lowercase__				):
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   int:






												if self.task == "multiple-choice":
																			a__					=					{0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																			a__					=					{0: 'batch', 1: 'sequence'}
												return OrderedDict(
												    [
												        ('inputs', dynamic_axis),
												        ('attention_mask', dynamic_axis),
												    ]				)
					@property
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												return 1E-4



					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :bool = False      ,__snake_case       :Optional[TensorType] = None      ,__snake_case       :int = 3      ,__snake_case       :int = 40      ,__snake_case       :int = 40      ,) ->   Dict:






												if isinstance(_a      ,_a				):
																			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																			a__					=					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
																			a__					=					preprocessor.num_special_tokens_to_add(_a				)
																			a__					=					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
																			a__					=					[' '.join(['a']				) * seq_length] * batch_size
																			a__					=					dict(preprocessor(_a      ,return_tensors=_a				)				)
																			a__					=					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
																			a__					=					compute_effective_axis_dimension(_a      ,fixed_dimension=OnnxConfig.default_fixed_batch				)
																			a__					=					self._generate_dummy_images(_a      ,_a      ,_a      ,_a				)
																			a__					=					dict(preprocessor(images=_a      ,return_tensors=_a				)				)
																			a__					=					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 collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


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




