

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

lowerCAmelCase_       	=	logging.get_logger(__name__)

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





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				"imagegpt"
			SCREAMING_SNAKE_CASE     :   List[str]									=				["past_key_values"]
			SCREAMING_SNAKE_CASE     :   List[Any]									=				{
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}







			def __init__(     self :				Union[str, Any]							,   _UpperCamelCase :				Tuple=5_1_2 + 1							,   _UpperCamelCase :				List[Any]=3_2 * 3_2							,   _UpperCamelCase :				Optional[Any]=5_1_2							,   _UpperCamelCase :				Any=2_4							,   _UpperCamelCase :				Union[str, Any]=8							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				List[str]="quick_gelu"							,   _UpperCamelCase :				List[Any]=0.1							,   _UpperCamelCase :				Optional[Any]=0.1							,   _UpperCamelCase :				Any=0.1							,   _UpperCamelCase :				Optional[int]=1e-5							,   _UpperCamelCase :				Dict=0.02							,   _UpperCamelCase :				List[Any]=True							,   _UpperCamelCase :				int=True							,   _UpperCamelCase :				Optional[int]=False							,   _UpperCamelCase :				Tuple=False							,   _UpperCamelCase :				Dict=False							,   **_UpperCamelCase :				Dict							,   )      ->str:
									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=_UpperCamelCase							,   **_UpperCamelCase							)






class 	snake_case_   (		__A					):







			'''simple docstring'''

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







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				"FeatureExtractionMixin"							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				int = -1							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				Optional["TensorType"] = None							,   _UpperCamelCase :				int = 3							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				int = 3_2							,   )      ->Mapping[str, Any]:
									snake_case_   =	self._generate_dummy_images(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
									snake_case_   =	dict(preprocessor(images=_UpperCamelCase							,   return_tensors=_UpperCamelCase							)							)

									return inputs



'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu

@require_cpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[Any]:
        lowerCAmelCase     =			torch.nn.Linear(1_0    ,      1_0    )
        lowerCAmelCase     =			torch.optim.SGD(model.parameters()    ,      0.1    )
        lowerCAmelCase     =			Accelerator()
        lowerCAmelCase     =			accelerator.prepare(UpperCAmelCase__    )
        try:
           pickle.loads(pickle.dumps(UpperCAmelCase__    )    )
        except Exception as e:
           self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
        AcceleratorState._reset_state()





