





"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						UpperCamelCase_	 =None

UpperCamelCase_	 =logging.get_logger(__name__)
UpperCamelCase_	 ={"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_ ,      UpperCamelCase_	 ="""[INST]""", """[/INST]"""
UpperCamelCase_ ,      UpperCamelCase_	 ="""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCamelCase_	 ="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       LlamaTokenizer
				UpperCamelCase								=       '''left'''
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']






				def __init__(					self						:       List[str],					lowerCAmelCase__						:       Tuple=None,					lowerCAmelCase__						:       int=None,					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       int="<unk>",					lowerCAmelCase__						:       Union[str, Any]="<s>",					lowerCAmelCase__						:       str="</s>",					lowerCAmelCase__						:       List[Any]=True,					lowerCAmelCase__						:       Any=False,					**lowerCAmelCase__						:       Union[str, Any],					)      -> int:




										'''simple docstring'''



										super().__init__(
										    vocab_file=lowerCAmelCase__,					tokenizer_file=lowerCAmelCase__,					clean_up_tokenization_spaces=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					bos_token=lowerCAmelCase__,					eos_token=lowerCAmelCase__,					**lowerCAmelCase__,					)
										_UpperCamelCase     :   Dict      =       add_bos_token
										_UpperCamelCase     :   Any      =       add_eos_token
										self.update_post_processor()

										_UpperCamelCase     :   List[Any]      =       vocab_file
										_UpperCamelCase     :   List[Any]      =       False if not self.vocab_file else True






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.bos_token
										_UpperCamelCase     :   List[Any]      =       self.bos_token_id

										_UpperCamelCase     :   str      =       self.eos_token
										_UpperCamelCase     :   Tuple      =       self.eos_token_id

										_UpperCamelCase     :   Optional[Any]      =       f"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
										_UpperCamelCase     :   int      =       f"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

										_UpperCamelCase     :   Union[str, Any]      =       []
										if self.add_bos_token:
																special_tokens.append((bos, bos_token_id)				)
										if self.add_eos_token:
																special_tokens.append((eos, eos_token_id)				)
										_UpperCamelCase     :   Any      =       processors.TemplateProcessing(
										    single=lowerCAmelCase__,					pair=lowerCAmelCase__,					special_tokens=lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										return self._add_eos_token






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''simple docstring'''



										return self._add_bos_token






				@add_eos_token.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       value
										self.update_post_processor()






				@add_bos_token.setter
				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[str]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       value
										self.update_post_processor()






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[str] = None				)      -> Tuple[str]:




										'''simple docstring'''



										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																    '''tokenizer.'''				)

										if not os.path.isdir(lowerCAmelCase__				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																return
										_UpperCamelCase     :   Optional[int]      =       os.path.join(
										    lowerCAmelCase__,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				):
																copyfile(self.vocab_file,					lowerCAmelCase__				)

										return (out_vocab_file,)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       "Conversation"				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       list(conversation.iter_texts()				)
										if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
										    [not is_user for is_user, msg in dialogue[1::2]]				):
																raise ValueError(
																    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

										_UpperCamelCase     :   Optional[Any]      =       []
										if len(conversation.past_user_inputs				) > 0:
																if not conversation.past_user_inputs[0].startswith(lowerCAmelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																						_UpperCamelCase     :   Any      =       (
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
										elif not dialogue[0][1].startswith(lowerCAmelCase__				) or E_SYS not in dialogue[0][1]:
																_UpperCamelCase     :   Tuple      =       (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

										dialog_tokens += sum(
										    [
										        [self.bos_token_id]
										        + self.encode(
										            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """,					add_special_tokens=lowerCAmelCase__				)
										        + [self.eos_token_id]
										        for prompt, answer in zip(dialogue[::2],					dialogue[1::2]				)
										    ],					[],					)
										if not (dialogue[-1][0]):
																raise ValueError(f"""Last message must be from user, got {dialogue[-1]['role']}"""				)
										dialog_tokens += [self.bos_token_id] + self.encode(
										    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}""",					add_special_tokens=lowerCAmelCase__				)
										return dialog_tokens





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('shards_per_node'     ,						[1, 2, 3]							)
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

			UpperCAmelCase__		:Dict 		=    split_dataset_by_node(full_ds.shuffle(seed=42							)     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(full_ds.shuffle()     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)




@pytest.mark.parametrize('streaming'     ,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)




@pytest.mark.parametrize(
    'nproc_per_node, num_workers'     ,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]     ,						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





