






'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowerCamelCase   :					Any   							=					logging.get_logger(__name__)

__lowerCamelCase   :					str   							=					{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''gpt_neo'''
							a__   		=	['''past_key_values''']
							a__   		=	{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:Tuple=50_257       ,							lowerCAmelCase__			:Optional[Any]=2_048       ,							lowerCAmelCase__			:Tuple=2_048       ,							lowerCAmelCase__			:Optional[int]=24       ,							lowerCAmelCase__			:List[str]=[[["global", "local"], 12]]       ,							lowerCAmelCase__			:Optional[int]=16       ,							lowerCAmelCase__			:List[Any]=None       ,							lowerCAmelCase__			:Dict=256       ,							lowerCAmelCase__			:Union[str, Any]="gelu_new"       ,							lowerCAmelCase__			:Dict=0.0       ,							lowerCAmelCase__			:Optional[Any]=0.0       ,							lowerCAmelCase__			:Union[str, Any]=0.0       ,							lowerCAmelCase__			:str=0.1       ,							lowerCAmelCase__			:Any=1E-5       ,							lowerCAmelCase__			:Optional[Any]=0.0_2       ,							lowerCAmelCase__			:Dict=True       ,							lowerCAmelCase__			:int=50_256       ,							lowerCAmelCase__			:Tuple=50_256       ,							**lowerCAmelCase__			:Any       ,							)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Dict   =       vocab_size
										snake_case_				:					Tuple   =       max_position_embeddings
										snake_case_				:					Tuple   =       hidden_size
										snake_case_				:					int   =       num_layers
										snake_case_				:					List[str]   =       num_heads
										snake_case_				:					List[Any]   =       intermediate_size
										snake_case_				:					str   =       window_size
										snake_case_				:					List[Any]   =       activation_function
										snake_case_				:					List[Any]   =       resid_dropout
										snake_case_				:					Union[str, Any]   =       embed_dropout
										snake_case_				:					List[str]   =       attention_dropout
										snake_case_				:					List[Any]   =       classifier_dropout
										snake_case_				:					List[Any]   =       layer_norm_epsilon
										snake_case_				:					Union[str, Any]   =       initializer_range
										snake_case_				:					Optional[int]   =       use_cache

										snake_case_				:					int   =       bos_token_id
										snake_case_				:					Dict   =       eos_token_id

										snake_case_				:					Any   =       attention_types
										snake_case_				:					Union[str, Any]   =       self.expand_attention_types_params(lowerCAmelCase__				)

										if len(self.attention_layers				) != self.num_layers:
													raise ValueError(
													    "Configuration for convolutional module is incorrect. "
													    "It is required that `len(config.attention_layers)` == `config.num_layers` "
													    F'''but is `len(config.attention_layers) = {len(self.attention_layers				)}`, '''
													    F'''`config.num_layers = {self.num_layers}`. '''
													    "`config.attention_layers` is prepared using `config.attention_types`. "
													    "Please verify the value of `config.attention_types` argument."				)

										super().__init__(bos_token_id=lowerCAmelCase__       ,							eos_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)






							@staticmethod
							def 							_A	(		lowerCAmelCase__			:Optional[Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					int   =       []
										for item in attention_types:
													for _ in range(item[1]				):
																attentions.extend(item[0]				)
										return attentions

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			import torch

			snake_case_				:					Optional[Any]   =       input.size()
			snake_case_				:					Optional[int]   =       len(__magic_name__						)
			snake_case_				:					List[Any]   =       shape[dimension]

			snake_case_				:					List[Any]   =       torch.arange(0     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					str   =       torch.div(sizedim - size     ,__magic_name__     ,rounding_mode="floor"						) + 1
			snake_case_				:					Dict   =       torch.arange(__magic_name__						) + low_indices[:min_length][:, None]

			snake_case_				:					List[str]   =       [slice(__magic_name__						)] * rank
			snake_case_				:					List[str]   =       indices
			snake_case_				:					Optional[Any]   =       input[s]

			snake_case_				:					List[str]   =       list(range(0     ,rank + 1						)						)
			perm.append(perm.pop(dimension + 1						)						)

			return sliced.permute(__magic_name__						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			import torch

			snake_case_				:					List[Any]   =       torch.arange(1     ,__magic_name__						)
			snake_case_				:					List[str]   =       torch.remainder(__magic_name__     ,__magic_name__						)
			snake_case_				:					Optional[int]   =       remainders == 0
			snake_case_				:					Union[str, Any]   =       candidates[divisor_indices]
			snake_case_				:					Any   =       torch.max(__magic_name__						)
			return largest_divisor, torch.div(__magic_name__     ,__magic_name__     ,rounding_mode="floor"						)







class    A_	(a_							):
							"""simple docstring"""




							@property
							def 							_A	(		self			:Optional[Any]				)				->  Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										snake_case_				:					str   =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
										if self.use_past:
													self.fill_with_past_key_values_(lowerCAmelCase__       ,							direction="inputs"				)
													snake_case_				:					str   =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													snake_case_				:					Optional[Any]   =       {0: "batch", 1: "sequence"}

										return common_inputs

							@property
							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										return self._config.num_heads

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:PreTrainedTokenizer       ,							lowerCAmelCase__			:int = -1       ,							lowerCAmelCase__			:int = -1       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:Optional[TensorType] = None       ,							)				->  Mapping[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       super(lowerCAmelCase__       ,							self				).generate_dummy_inputs(
										    lowerCAmelCase__       ,							batch_size=lowerCAmelCase__       ,							seq_length=lowerCAmelCase__       ,							is_pair=lowerCAmelCase__       ,							framework=lowerCAmelCase__				)

										# We need to order the input in the way they appears in the forward()
										snake_case_				:					Union[str, Any]   =       OrderedDict({"input_ids": common_inputs["input_ids"]}				)

										# Need to add the past_keys
										if self.use_past:
													if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
													else:
																import torch

																snake_case_,	snake_case_				:					int   =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																snake_case_				:					List[str]   =       seqlen + 2
																snake_case_				:					int   =       (
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																snake_case_				:					Union[str, Any]   =       [
																    (torch.zeros(lowerCAmelCase__				), torch.zeros(lowerCAmelCase__				)) for _ in range(self.num_layers				)
																]

										snake_case_				:					List[str]   =       common_inputs["attention_mask"]
										if self.use_past:
													snake_case_				:					Any   =       ordered_inputs["attention_mask"].dtype
													snake_case_				:					str   =       torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__       ,							lowerCAmelCase__       ,							dtype=lowerCAmelCase__				)]       ,							dim=1				)

										return ordered_inputs






							@property
							def 							_A	(		self			:Any				)				->  int:


										'''simple docstring'''




										return 13












'''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    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




