



"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, TensorType

lowerCAmelCase__		:					Optional[int]							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class        UpperCAmelCase_  (       _lowerCamelCase	):

						'''simple docstring'''




						UpperCamelCase__      :			int      =  '''imagegpt'''
						UpperCamelCase__      :			Union[str, Any]      =  ['''past_key_values''']
						UpperCamelCase__      :			str      =  {
						    '''hidden_size''': '''n_embd''',
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}






						def __init__(   self				,							_A=512 + 1				,							_A=32 * 32				,							_A=512				,							_A=24				,							_A=8				,							_A=None				,							_A="quick_gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=1e-5				,							_A=0.0_2				,							_A=True				,							_A=True				,							_A=False				,							_A=False				,							_A=False				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= vocab_size
													__SCREAMING_SNAKE_CASE									= n_positions
													__SCREAMING_SNAKE_CASE									= n_embd
													__SCREAMING_SNAKE_CASE									= n_layer
													__SCREAMING_SNAKE_CASE									= n_head
													__SCREAMING_SNAKE_CASE									= n_inner
													__SCREAMING_SNAKE_CASE									= activation_function
													__SCREAMING_SNAKE_CASE									= resid_pdrop
													__SCREAMING_SNAKE_CASE									= embd_pdrop
													__SCREAMING_SNAKE_CASE									= attn_pdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_epsilon
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= scale_attn_weights
													__SCREAMING_SNAKE_CASE									= use_cache
													__SCREAMING_SNAKE_CASE									= scale_attn_by_inverse_layer_idx
													__SCREAMING_SNAKE_CASE									= reorder_and_upcast_attn
													__SCREAMING_SNAKE_CASE									= tie_word_embeddings

													super().__init__(tie_word_embeddings=_A				,							**_A		)







class        UpperCAmelCase_  (       _lowerCamelCase	):

						'''simple docstring'''




						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'sequence'}),
													    ]		)






						def 	_A     (   self				,							_A				,							_A = 1				,							_A = -1				,							_A = False				,							_A = None				,							_A = 3				,							_A = 32				,							_A = 32				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._generate_dummy_images(_A				,							_A				,							_A				,							_A		)
													__SCREAMING_SNAKE_CASE									= dict(preprocessor(images=_A				,							return_tensors=_A		)		)

													return inputs











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 0
							for a in range(1							,       n // 3       ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






