





'''simple docstring'''
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


a__							:							Optional[Any] =logging.get_logger(__name__)

a__							:							List[Any] ={'''tokenizer_file''': '''tokenizer.json'''}

a__							:							Dict ={
    '''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					Any					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=["input_ids", "attention_mask"]
 SCREAMING_SNAKE_CASE_	:					Any					=None






 def __init__(       self       :							Optional[Any]							,	__A       :							Any=None							,	__A       :							Optional[Any]=None							,	__A       :							Optional[Any]=None							,	__A       :							int="<unk>"							,	__A       :							int="<s>"							,	__A       :							Optional[int]="</s>"							,	__A       :							Union[str, Any]="<pad>"							,	__A       :							List[str]=False							,	__A       :							int=False							,	**__A       :							List[str]							,	):
        super().__init__(
            __A							,	__A							,	tokenizer_file=__A							,	unk_token=__A							,	bos_token=__A							,	eos_token=__A							,	pad_token=__A							,	add_prefix_space=__A							,	clean_up_tokenization_spaces=__A							,	**__A							,	)
        __UpperCamelCase       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get('add_prefix_space'							,	__A	) != add_prefix_space:
               __UpperCamelCase       =  getattr(__A							,	pre_tok_state.pop('type'	)	)
               __UpperCamelCase       =  add_prefix_space
               __UpperCamelCase       =  pre_tok_class(**__A	)

        __UpperCamelCase       =  add_prefix_space






 def        _lowerCamelCase					(       self       :							Dict							,	*__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  kwargs.get('is_split_into_words'							,	__A	)
        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(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        __UpperCamelCase       =  kwargs.get('is_split_into_words'							,	__A	)

        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(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  self._tokenizer.model.save(__A							,	name=__A	)
        return tuple(__A	)






 def        _lowerCamelCase					(       self       :							str							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__A							,	add_special_tokens=__A	) + [self.eos_token_id]	)

        if len(__A	) > self.model_max_length:
               __UpperCamelCase       =  input_ids[-self.model_max_length :]
        return input_ids




"""simple docstring"""




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       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				__a           =			inspect.getfile(accelerate.test_utils      )
				__a           =			os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
				__a           =			os.path.sep.join(
				    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']      )
				__a           =			os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_ops.py''']      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices.'''      )
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
				with patch_environment(omp_num_threads=1      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices.'''      )
				__a           =			['''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(_a							, env=os.environ.copy()      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
				with patch_environment(omp_num_threads=1      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )





		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''      )
				__a           =			['''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(_a							, env=os.environ.copy()      )


if __name__ == "__main__":
				lowercase_			     =  Accelerator()
				lowercase_			     =  (accelerator.state.process_index + 2, 1_0)
				lowercase_			     =  torch.randint(0, 1_0, shape).to(accelerator.device)

				lowercase_			     =  ""

				lowercase_			     =  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)."

				lowercase_			     =  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."
				lowercase_			     =  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)






