
'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE = False   )     ->   Optional[int]:
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(_SCREAMING_SNAKE_CASE   )
							np.random.seed(_SCREAMING_SNAKE_CASE   )
							torch.manual_seed(_SCREAMING_SNAKE_CASE   )
							if is_xpu_available():
														torch.xpu.manual_seed_all(_SCREAMING_SNAKE_CASE   )
							elif is_npu_available():
														torch.npu.manual_seed_all(_SCREAMING_SNAKE_CASE   )
							else:
														torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE   )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(_SCREAMING_SNAKE_CASE   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE = None							,      _SCREAMING_SNAKE_CASE = None   )     ->   Optional[int]:
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														_snake_case     =						torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														_snake_case     =						torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														_snake_case     =						torch.tensor(xm.get_rng_state()   )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														_snake_case     =						torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														_snake_case     =						torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														_snake_case     =						generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							_snake_case     =						AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														_snake_case     =						rng_state.to(xm.xla_device()   )
														xm.collective_broadcast([rng_state]   )
														xm.mark_step()
														_snake_case     =						rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														_snake_case     =						rng_state.to(state.device   )
														torch.distributed.broadcast(_SCREAMING_SNAKE_CASE							,      0   )
														_snake_case     =						rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(_SCREAMING_SNAKE_CASE							,      0   )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(_SCREAMING_SNAKE_CASE   )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(_SCREAMING_SNAKE_CASE   )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(_SCREAMING_SNAKE_CASE   )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(_SCREAMING_SNAKE_CASE   )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()   )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(_SCREAMING_SNAKE_CASE   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE = None   )     ->   Dict:
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE   )							,      generator=_SCREAMING_SNAKE_CASE   )

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__lowerCAmelCase  							= None


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase  							= {
    'facebook/xglm-564M': 2_048,
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		["input_ids", "attention_mask"]
							lowerCAmelCase_      		=		XGLMTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="<unk>"						,						UpperCAmelCase="<pad>"						,						**UpperCAmelCase						,						)  ->     List[Any]:
														# Compatibility with the original tokenizer
														_snake_case     =						7
														_snake_case     =						[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

														_snake_case     =						kwargs.get("""additional_special_tokens"""						,						[]						)
														kwargs["additional_special_tokens"] += [
														    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
														]

														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						**UpperCAmelCase						,						)

														_snake_case     =						vocab_file
														_snake_case     =						False if not self.vocab_file else True

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_snake_case     =						[self.sep_token_id]
														return sep + token_ids_a + sep + sep + token_ids_a

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:
														_snake_case     =						[self.sep_token_id]

														if token_ids_a is None:
																					return len(sep + token_ids_a						) * [0]
														return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														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(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

														return (out_vocab_file,)