





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[int]					 =		inspect.getfile(accelerate.test_utils	)
     lowerCamelCase__    :      Optional[Any]					 =		os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
     lowerCamelCase__    :      Optional[Any]					 =		os.path.sep.join(
         mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
     lowerCamelCase__    :      int					 =		os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)
 @require_multi_gpu
 def      A_	(      self  :      Optional[int]	)			->	Any:
     print(F"""Found {torch.cuda.device_count()} devices."""	)
     lowerCamelCase__    :      Optional[Any]					 =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
     with patch_environment(omp_num_threads=1	):
         execute_subprocess_async(lowerCAmelCase__			,     env=os.environ.copy()	)
 @require_multi_gpu
 def      A_	(      self  :      Optional[int]	)			->	Tuple:
     print(F"""Found {torch.cuda.device_count()} devices."""	)
     lowerCamelCase__    :      Union[str, Any]					 =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
     print(F"""Command: {cmd}"""	)
     with patch_environment(omp_num_threads=1	):
         execute_subprocess_async(lowerCAmelCase__			,     env=os.environ.copy()	)
 @require_multi_gpu
 def      A_	(      self  :      Tuple	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
     with patch_environment(omp_num_threads=1	):
         execute_subprocess_async(lowerCAmelCase__			,     env=os.environ.copy()	)







 @require_multi_gpu
 def      A_	(      self  :      str	)			->	int:
     print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
     lowerCamelCase__    :      Dict					 =		['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
     with patch_environment(omp_num_threads=1			,     cuda_visible_devices='0,1'	):
         execute_subprocess_async(lowerCAmelCase__			,     env=os.environ.copy()	)


if __name__ == "__main__":
    _UpperCAmelCase				:       Dict	   =				Accelerator()
    _UpperCAmelCase				:       Any	   =				(accelerator.state.process_index + 2, 10)
    _UpperCAmelCase				:       List[Any]	   =				torch.randint(0, 10, shape).to(accelerator.device)

    _UpperCAmelCase				:       Tuple	   =				''

    _UpperCAmelCase				:       Union[str, Any]	   =				accelerator.pad_across_processes(tensor)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
        error_msg += "Padding was not done with the right value (0)."

    _UpperCAmelCase				:       Union[str, Any]	   =				accelerator.pad_across_processes(tensor, pad_first=True)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    _UpperCAmelCase				:       Optional[int]	   =				accelerator.state.num_processes - accelerator.state.process_index - 1
    if not torch.equal(tensora[index:], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[:index] == 0):
        error_msg += "Padding was not done with the right value (0)."

    # Raise error at the end to make sure we don't stop at the first failure.
    if len(error_msg) > 0:
        raise ValueError(error_msg)









import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	: Optional[int]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
