

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__												=						logging.get_logger(__name__)

a__												=						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







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


							def __init__(     self			,				_a=5_1_2 + 1			,				_a=3_2 * 3_2			,				_a=5_1_2			,				_a=2_4			,				_a=8			,				_a=None			,				_a="quick_gelu"			,				_a=0.1			,				_a=0.1			,				_a=0.1			,				_a=1e-5			,				_a=0.02			,				_a=True			,				_a=True			,				_a=False			,				_a=False			,				_a=False			,				**_a			,				)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						vocab_size
												_a      :       str   =						n_positions
												_a      :       Tuple   =						n_embd
												_a      :       Tuple   =						n_layer
												_a      :       Optional[Any]   =						n_head
												_a      :       str   =						n_inner
												_a      :       Optional[Any]   =						activation_function
												_a      :       List[str]   =						resid_pdrop
												_a      :       Optional[Any]   =						embd_pdrop
												_a      :       Tuple   =						attn_pdrop
												_a      :       Optional[Any]   =						layer_norm_epsilon
												_a      :       List[Any]   =						initializer_range
												_a      :       List[str]   =						scale_attn_weights
												_a      :       str   =						use_cache
												_a      :       Any   =						scale_attn_by_inverse_layer_idx
												_a      :       Optional[Any]   =						reorder_and_upcast_attn
												_a      :       List[str]   =						tie_word_embeddings

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



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			,				_a			,				_a = 1			,				_a = -1			,				_a = False			,				_a = None			,				_a = 3			,				_a = 3_2			,				_a = 3_2			,				)       ->       Mapping[str, Any]:
												_a      :       Optional[Any]   =						self._generate_dummy_images(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
												_a      :       Dict   =						dict(preprocessor(images=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			)			)

												return inputs





from __future__ import annotations




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):  # noqa: E741


				'''simple docstring'''
				while r - l > 1:
								lowercase_	    =   (l + r) // 2
								if v[m] >= key:
												lowercase_	    =   m
								else:
												lowercase_	    =   m  # noqa: E741
				return r




def 							a		(				snake_case__:   list[int]   ):


				'''simple docstring'''
				if len(snake_case__   ) == 0:
								return 0

				lowercase_	    =   [0] * len(snake_case__   )
				lowercase_	    =   1

				lowercase_	    =   v[0]

				for i in range(1		,  len(snake_case__   )   ):
								if v[i] < tail[0]:
												lowercase_	    =   v[i]
								elif v[i] > tail[length - 1]:
												lowercase_	    =   v[i]
												length += 1
								else:
												lowercase_	    =   v[i]

				return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




