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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{'''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase       =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	int									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	Dict									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	str									=			["""input_ids""", """attention_mask"""]
	_UpperCamelCase	:	Tuple									=			None





	def __init__(			self						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case="<unk>"						,       snake_case="<s>"						,       snake_case="</s>"						,       snake_case="<pad>"						,       snake_case=False						,       snake_case=False						,       **snake_case						,       ):
		super().__init__(
		    snake_case						,       snake_case						,       tokenizer_file=snake_case						,       unk_token=snake_case						,       bos_token=snake_case						,       eos_token=snake_case						,       pad_token=snake_case						,       add_prefix_space=snake_case						,       clean_up_tokenization_spaces=snake_case						,       **snake_case						,       )
		lowercase	    =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
		if pre_tok_state.get('add_prefix_space'						,       snake_case			) != add_prefix_space:
			lowercase	    =       getattr(snake_case						,       pre_tok_state.pop('type'			)			)
			lowercase	    =       add_prefix_space
			lowercase	    =       pre_tok_class(**snake_case			)

		lowercase	    =       add_prefix_space





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)
		if not (self.add_prefix_space or not is_split_into_words):
			raise Exception(
			    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
			    ' pretokenized inputs.'			)

		return super()._batch_encode_plus(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)

		if not (self.add_prefix_space or not is_split_into_words):
			raise Exception(
			    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
			    ' pretokenized inputs.'			)

		return super()._encode_plus(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       self._tokenizer.model.save(snake_case						,       name=snake_case			)
		return tuple(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       []
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(snake_case						,       add_special_tokens=snake_case			) + [self.eos_token_id]			)

		if len(snake_case			) > self.model_max_length:
			lowercase	    =       input_ids[-self.model_max_length :]
		return input_ids







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE__     :     int		    =       logging.get_logger(__name__)



@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= [
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]





	def __init__(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										SCREAMING_SNAKE_CASE_								=						deprecated_arg[3:]
										setattr(self      ,     SCREAMING_SNAKE_CASE      ,     not kwargs.pop(SCREAMING_SNAKE_CASE      )      )
										logger.warning(
										    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
										    f' {positive_arg}={kwargs[positive_arg]}'      )

				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torchscript'      ,     self.torchscript      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torch_xla_tpu_print_metrics'      ,     self.torch_xla_tpu_print_metrics      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('fp16_opt_level'      ,     self.fpaa_opt_level      )
				super().__init__(**SCREAMING_SNAKE_CASE      )

	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Trace the models using torchscript'''}     )
	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
	A    							= field(
	    default='''O1'''  ,    metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }  ,    )





	@cached_property
	def 						A_(							self      )  ->					Tuple["torch.device", int]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				logger.info('PyTorch: setting up devices'      )
				if not self.cuda:
							SCREAMING_SNAKE_CASE_								=						torch.device('cpu'      )
							SCREAMING_SNAKE_CASE_								=						0
				elif is_torch_tpu_available():
							SCREAMING_SNAKE_CASE_								=						xm.xla_device()
							SCREAMING_SNAKE_CASE_								=						0
				else:
							SCREAMING_SNAKE_CASE_								=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
							SCREAMING_SNAKE_CASE_								=						torch.cuda.device_count()
				return device, n_gpu





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				return is_torch_tpu_available() and self.tpu





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()





	@property
	def 						A_(							self      )  ->					"torch.device":




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[0]





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




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[1]





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return self.n_gpu > 0
