

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

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


if is_sentencepiece_available():
					from .tokenization_reformer import ReformerTokenizer
else:
					lowerCAmelCase_	     =     None


lowerCAmelCase_	     =     logging.get_logger(__name__)


lowerCAmelCase_	     =     '''▁'''

lowerCAmelCase_	     =     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_	     =     {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

lowerCAmelCase_	     =     {
    '''google/reformer-crime-and-punishment''': 52_42_88,
}





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[Any]						      =    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE					:       List[str]						      =    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    ["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE					:       Dict						      =    ReformerTokenizer







			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Dict=None							,       _UpperCamelCase			:    Union[str, Any]=None							,       _UpperCamelCase			:    Union[str, Any]="</s>"							,       _UpperCamelCase			:    List[str]="<unk>"							,       _UpperCamelCase			:    Any=[]							,       **_UpperCamelCase			:    Optional[int]							,       )				->Union[str, Any]:
									super().__init__(
									    _UpperCamelCase							,       tokenizer_file=_UpperCamelCase							,       eos_token=_UpperCamelCase							,       unk_token=_UpperCamelCase							,       additional_special_tokens=_UpperCamelCase							,       **_UpperCamelCase							,       )

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







			def     snake_case__(					self			:    Any							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    Optional[str] = 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,)


import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												snake_case_       =     (1,)

						snake_case_       =     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												snake_case_       =     '''int16'''

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												snake_case_       =     weight[0]
						snake_case_       =     torch.tensor(SCREAMING_SNAKE_CASE__	)
						if weight_info["dtype"] == "bfloat16":
												snake_case_       =     weight.view(torch.bfloataa	)

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		'''index.json'''	)
						if os.path.isfile(SCREAMING_SNAKE_CASE__	):
												with open(SCREAMING_SNAKE_CASE__  ,		'''r'''  ,		encoding='''utf-8'''	) as f:
																		snake_case_       =     json.load(SCREAMING_SNAKE_CASE__	)
						else:
												snake_case_       =     {}
						current_index.update(SCREAMING_SNAKE_CASE__	)

						with open(SCREAMING_SNAKE_CASE__  ,		'''w'''  ,		encoding='''utf-8'''	) as f:
												json.dump(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		indent=2	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

									snake_case_       =     {} if state_dict is None else state_dict
									snake_case_       =     save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(_UpperCamelCase							,       '''index.json'''							)							) as f:
																					snake_case_       =     json.load(_UpperCamelCase							)
									snake_case_       =     {} if index is None else index
									snake_case_       =     list(self.state_dict.keys()							)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]							)
									snake_case_       =     device







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     self.index[key]
									if weight_info.get('''safetensors_file'''							) is not None:
															if not is_safetensors_available():
																					raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''							)

															from safetensors import safe_open

															snake_case_       =     '''cpu''' if self.device is None else self.device
															with safe_open(weight_info['''safetensors_file''']							,       framework='''pt'''							,       device=_UpperCamelCase							) as f:
																					snake_case_       =     f.get_tensor(weight_info.get('''weight_name'''							,       _UpperCamelCase							)							)

															if "dtype" in weight_info:
																					return tensor.to(getattr(_UpperCamelCase							,       weight_info['''dtype''']							)							)
															else:
																					return tensor

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result