

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

A_	:			Tuple  	=      logging.get_logger(__name__)

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



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''imagegpt'''
					UpperCAmelCase__:       Dict  							=	['''past_key_values''']
					UpperCAmelCase__:       int  							=	{
					    '''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__   ,	):
						A__							:   int					       =		vocab_size
						A__							:   Tuple					       =		n_positions
						A__							:   Tuple					       =		n_embd
						A__							:   str					       =		n_layer
						A__							:   int					       =		n_head
						A__							:   Any					       =		n_inner
						A__							:   Optional[Any]					       =		activation_function
						A__							:   Any					       =		resid_pdrop
						A__							:   int					       =		embd_pdrop
						A__							:   List[str]					       =		attn_pdrop
						A__							:   Union[str, Any]					       =		layer_norm_epsilon
						A__							:   List[str]					       =		initializer_range
						A__							:   List[str]					       =		scale_attn_weights
						A__							:   List[Any]					       =		use_cache
						A__							:   Optional[int]					       =		scale_attn_by_inverse_layer_idx
						A__							:   Dict					       =		reorder_and_upcast_attn
						A__							:   List[str]					       =		tie_word_embeddings

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



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@property
					def   __A      (			self      ):
						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   ,	):
						A__							:   Tuple					       =		self._generate_dummy_images(A__   ,	A__   ,	A__   ,	A__      )
						A__							:   str					       =		dict(preprocessor(images=A__   ,	return_tensors=A__      )      )

						return inputs






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



