


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


a : str		=				logging.get_logger(__name__)

a : int		=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

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

a : List[str]		=				{
    'gpt-neox-20b': 2048,
}
class 	lowerCamelCase_       (				UpperCamelCase_				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]






					def __init__(				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_="<|endoftext|>" ,       snake_case_="<|endoftext|>" ,       snake_case_="<|endoftext|>" ,       snake_case_=False ,       **snake_case_ ,       )   ->			List[str]:







								'''simple docstring'''

								super().__init__(
								    UpperCamelCase__ ,       UpperCamelCase__ ,       tokenizer_file=UpperCamelCase__ ,       unk_token=UpperCamelCase__ ,       bos_token=UpperCamelCase__ ,       eos_token=UpperCamelCase__ ,       add_prefix_space=UpperCamelCase__ ,       **UpperCamelCase__ ,       )

								__lowercase 					=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
								if pre_tok_state.get('''add_prefix_space''' ,       UpperCamelCase__						) != add_prefix_space:
											__lowercase 					=   getattr(UpperCamelCase__ ,       pre_tok_state.pop('''type'''						)						)
											__lowercase 					=   add_prefix_space
											__lowercase 					=   pre_tok_class(**UpperCamelCase__						)

								__lowercase 					=   add_prefix_space






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self._tokenizer.model.save(UpperCamelCase__ ,       name=UpperCamelCase__						)
								return tuple(UpperCamelCase__						)






					def A      (				self ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   []
								for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCamelCase__ ,       add_special_tokens=UpperCamelCase__						) + [self.eos_token_id]						)

								if len(UpperCamelCase__						) > self.model_max_length:
											__lowercase 					=   input_ids[-self.model_max_length :]
								return input_ids






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


