





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

UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''imagegpt'''
					lowerCAmelCase_							=	['''past_key_values''']
					lowerCAmelCase_							=	{
					    '''hidden_size''': '''n_embd''',
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}




					def __init__(		self				,						lowerCAmelCase_=512 + 1				,						lowerCAmelCase_=32 * 32				,						lowerCAmelCase_=512				,						lowerCAmelCase_=24				,						lowerCAmelCase_=8				,						lowerCAmelCase_=None				,						lowerCAmelCase_="quick_gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=1E-5				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=False				,						lowerCAmelCase_=False				,						lowerCAmelCase_=False				,						**lowerCAmelCase_				,						)		->							Optional[Any]:
										_snake_case									=				vocab_size
										_snake_case									=				n_positions
										_snake_case									=				n_embd
										_snake_case									=				n_layer
										_snake_case									=				n_head
										_snake_case									=				n_inner
										_snake_case									=				activation_function
										_snake_case									=				resid_pdrop
										_snake_case									=				embd_pdrop
										_snake_case									=				attn_pdrop
										_snake_case									=				layer_norm_epsilon
										_snake_case									=				initializer_range
										_snake_case									=				scale_attn_weights
										_snake_case									=				use_cache
										_snake_case									=				scale_attn_by_inverse_layer_idx
										_snake_case									=				reorder_and_upcast_attn
										_snake_case									=				tie_word_embeddings

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





class 			UpperCamelCase_		(  _lowerCamelCase					):




					@property
					def 	lowerCAmelCase			(		self  )		->							Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										    ]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = 1				,						lowerCAmelCase_ = -1				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 3				,						lowerCAmelCase_ = 32				,						lowerCAmelCase_ = 32				,						)		->							Mapping[str, Any]:
										_snake_case									=				self._generate_dummy_images(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )
										_snake_case									=				dict(preprocessor(images=lowerCAmelCase_				,						return_tensors=lowerCAmelCase_  )  )

										return inputs










import random





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       List[Any]						)			-> tuple:



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				[], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase__						)
										elif element > pivot:
															greater.append(UpperCamelCase__						)
										else:
															equal.append(UpperCamelCase__						)
					return less, equal, greater





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       int						)			-> Dict:



					'''simple docstring'''




					if index >= len(UpperCamelCase__						) or index < 0:
										return None

					_snake_case									=				items[random.randint(0       ,    len(UpperCamelCase__						) - 1						)]
					_snake_case									=				0
					_snake_case							,     _snake_case							,     _snake_case									=				_partition(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase__       ,    UpperCamelCase__						)
					# must be in larger
					else:
										return quick_select(UpperCamelCase__       ,    index - (m + count)						)



