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

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


lowercase  :					int													=       logging.get_logger(__name__)

lowercase  :					Optional[Any]													=       {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'gptj'
	_A								=     {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(					self		:Union[str, Any]     ,   a		:Union[str, Any]=5_0_4_0_0     ,   a		:List[str]=2_0_4_8     ,   a		:int=4_0_9_6     ,   a		:Optional[int]=2_8     ,   a		:str=1_6     ,   a		:int=6_4     ,   a		:Tuple=None     ,   a		:Dict="gelu_new"     ,   a		:Union[str, Any]=0.0     ,   a		:Tuple=0.0     ,   a		:int=0.0     ,   a		:List[str]=1E-5     ,   a		:Union[str, Any]=0.02     ,   a		:Any=True     ,   a		:List[str]=5_0_2_5_6     ,   a		:List[str]=5_0_2_5_6     ,   a		:Optional[Any]=False     ,   **a		:Optional[int]     ,   )		-> Any:
								__UpperCamelCase	:   Any			   =					vocab_size
								__UpperCamelCase	:   Optional[int]			   =					n_positions
								__UpperCamelCase	:   Tuple			   =					n_embd
								__UpperCamelCase	:   Union[str, Any]			   =					n_layer
								__UpperCamelCase	:   Optional[int]			   =					n_head
								__UpperCamelCase	:   Tuple			   =					n_inner
								__UpperCamelCase	:   int			   =					rotary_dim
								__UpperCamelCase	:   Union[str, Any]			   =					activation_function
								__UpperCamelCase	:   str			   =					resid_pdrop
								__UpperCamelCase	:   Any			   =					embd_pdrop
								__UpperCamelCase	:   Any			   =					attn_pdrop
								__UpperCamelCase	:   List[str]			   =					layer_norm_epsilon
								__UpperCamelCase	:   List[str]			   =					initializer_range
								__UpperCamelCase	:   Any			   =					use_cache

								__UpperCamelCase	:   Optional[int]			   =					bos_token_id
								__UpperCamelCase	:   Tuple			   =					eos_token_id

								super().__init__(
								    bos_token_id=a     ,   eos_token_id=a     ,   tie_word_embeddings=a     ,   **a						)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Dict     ,   a		:PretrainedConfig     ,   a		:str = "default"     ,   a		:List[PatchingSpec] = None     ,   a		:bool = False     ,   )		-> Optional[Any]:
								super().__init__(a     ,   task=a     ,   patching_specs=a     ,   use_past=a						)
								if not getattr(self._config     ,   "pad_token_id"     ,   a						):
															# TODO: how to do that better?
															__UpperCamelCase	:   Optional[Any]			   =					0







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Mapping[str, Mapping[int, str]]:
								__UpperCamelCase	:   Any			   =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
								if self.use_past:
															self.fill_with_past_key_values_(a     ,   direction="inputs"						)
															__UpperCamelCase	:   List[Any]			   =					{0: "batch", 1: "past_sequence + sequence"}
								else:
															__UpperCamelCase	:   Union[str, Any]			   =					{0: "batch", 1: "sequence"}

								return common_inputs







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								return self._config.n_layer







	@property
	def    _lowerCamelCase				(					self		:List[Any]						)		-> int:
								return self._config.n_head







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:PreTrainedTokenizer     ,   a		:int = -1     ,   a		:int = -1     ,   a		:bool = False     ,   a		:Optional[TensorType] = None     ,   )		-> Mapping[str, Any]:
								__UpperCamelCase	:   List[str]			   =					super(a     ,   self						).generate_dummy_inputs(
								    a     ,   batch_size=a     ,   seq_length=a     ,   is_pair=a     ,   framework=a						)

								# We need to order the input in the way they appears in the forward()
								__UpperCamelCase	:   Optional[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

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

								__UpperCamelCase	:   Optional[int]			   =					common_inputs["attention_mask"]
								if self.use_past:
															__UpperCamelCase	:   Dict			   =					ordered_inputs["attention_mask"].dtype
															__UpperCamelCase	:   Optional[Any]			   =					torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(a     ,   a     ,   dtype=a						)]     ,   dim=1						)

								return ordered_inputs







	@property
	def    _lowerCamelCase				(					self		:str						)		-> int:
								return 1_3

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)