
"""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

_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}
class        _SCREAMING_SNAKE_CASE  (  __a							):






				'''simple docstring'''



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


				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			Optional[Any]=512 + 1				,     UpperCAmelCase_	:			Dict=32 * 32				,     UpperCAmelCase_	:			Union[str, Any]=512				,     UpperCAmelCase_	:			Dict=24				,     UpperCAmelCase_	:			int=8				,     UpperCAmelCase_	:			Dict=None				,     UpperCAmelCase_	:			List[Any]="quick_gelu"				,     UpperCAmelCase_	:			Optional[Any]=0.1				,     UpperCAmelCase_	:			Optional[int]=0.1				,     UpperCAmelCase_	:			List[Any]=0.1				,     UpperCAmelCase_	:			List[Any]=1E-5				,     UpperCAmelCase_	:			int=0.02				,     UpperCAmelCase_	:			str=True				,     UpperCAmelCase_	:			Union[str, Any]=True				,     UpperCAmelCase_	:			Any=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			str=False				,     **UpperCAmelCase_	:			Optional[Any]				,     )		->  List[str]:







											"""simple docstring"""




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

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






class        _SCREAMING_SNAKE_CASE  (  __a							):






				'''simple docstring'''



				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  str:







											"""simple docstring"""




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







				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			"FeatureExtractionMixin"				,     UpperCAmelCase_	:			int = 1				,     UpperCAmelCase_	:			int = -1				,     UpperCAmelCase_	:			bool = False				,     UpperCAmelCase_	:			Optional["TensorType"] = None				,     UpperCAmelCase_	:			int = 3				,     UpperCAmelCase_	:			int = 32				,     UpperCAmelCase_	:			int = 32				,     )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._generate_dummy_images(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__  )
											_lowerCAmelCase  	=			dict(preprocessor(images=snake_case__				,     return_tensors=snake_case__  )  )

											return inputs








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






