'''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

UpperCamelCase_      	=	logging.get_logger(__name__)

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




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							List[Any]     		= 'imagegpt'
						A_			:							Tuple     		= ['past_key_values']
						A_			:							Optional[int]     		= {
						    'hidden_size': 'n_embd',
						    'max_position_embeddings': 'n_positions',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}




						def __init__(    self :       Optional[int]							,							UpperCamelCase_ :       Dict=5_12 + 1							,							UpperCamelCase_ :       Union[str, Any]=32 * 32							,							UpperCamelCase_ :       int=5_12							,							UpperCamelCase_ :       Dict=24							,							UpperCamelCase_ :       Optional[Any]=8							,							UpperCamelCase_ :       List[Any]=None							,							UpperCamelCase_ :       int="quick_gelu"							,							UpperCamelCase_ :       int=0.1							,							UpperCamelCase_ :       int=0.1							,							UpperCamelCase_ :       Optional[int]=0.1							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       Optional[int]=0.02							,							UpperCamelCase_ :       Optional[int]=True							,							UpperCamelCase_ :       Any=True							,							UpperCamelCase_ :       Tuple=False							,							UpperCamelCase_ :       List[str]=False							,							UpperCamelCase_ :       List[Any]=False							,							**UpperCamelCase_ :       Any							,							)     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Dict						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = n_positions
													SCREAMING_SNAKE_CASE__     :str						    = n_embd
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = n_layer
													SCREAMING_SNAKE_CASE__     :List[Any]						    = n_head
													SCREAMING_SNAKE_CASE__     :Dict						    = n_inner
													SCREAMING_SNAKE_CASE__     :List[Any]						    = activation_function
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = resid_pdrop
													SCREAMING_SNAKE_CASE__     :Dict						    = embd_pdrop
													SCREAMING_SNAKE_CASE__     :Dict						    = attn_pdrop
													SCREAMING_SNAKE_CASE__     :Any						    = layer_norm_epsilon
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = initializer_range
													SCREAMING_SNAKE_CASE__     :Dict						    = scale_attn_weights
													SCREAMING_SNAKE_CASE__     :int						    = use_cache
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = scale_attn_by_inverse_layer_idx
													SCREAMING_SNAKE_CASE__     :int						    = reorder_and_upcast_attn
													SCREAMING_SNAKE_CASE__     :Tuple						    = tie_word_embeddings

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




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




						@property
						def      __lowerCamelCase       (    self :       List[str]      )     ->				Mapping[str, Mapping[int, str]]:
													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							,							)     ->				Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._generate_dummy_images(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = dict(preprocessor(images=UpperCamelCase_							,							return_tensors=UpperCamelCase_      )      )

													return inputs







'''simple docstring'''







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase_      	=	'''.'''


if __name__ == "__main__":
		UpperCamelCase_      	=	os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
		UpperCamelCase_      	=	[]
		UpperCamelCase_      	=	[]
		with open(doctest_file_path) as fp:
				for line in fp:
						UpperCamelCase_      	=	line.strip()
						UpperCamelCase_      	=	os.path.join(REPO_PATH, line)
						if not (os.path.isfile(path) or os.path.isdir(path)):
								non_existent_paths.append(line)
						all_paths.append(path)
		if len(non_existent_paths) > 0:
				UpperCamelCase_      	=	'''\n'''.join(non_existent_paths)
				raise ValueError(f"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
		if all_paths != sorted(all_paths):
				raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






