


'''simple docstring'''






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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A		:		List[Any]   =   logging.get_logger(__name__)

__A		:		Dict   =   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A		:		Dict   =   {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__A		:		Union[str, Any]   =   {
    'microsoft/biogpt': 1_024,
}





def    UpperCAmelCase					(						lowerCamelCase_     :Optional[Any]       ):







		'''simple docstring'''
		snake_case_						:							Any					=  set()
		snake_case_						:							Optional[Any]					=  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)       )
				snake_case_						:							int					=  char
		return pairs






class   __UpperCamelCase		(  a_							):
							lowercase	:						Optional[int]      						=     VOCAB_FILES_NAMES
							lowercase	:						Dict      						=     PRETRAINED_VOCAB_FILES_MAP
							lowercase	:						List[Any]      						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase	:						Any      						=     ['input_ids', 'attention_mask']





							def __init__(     self						:List[str]     ,_UpperCamelCase						:Union[str, Any]     ,_UpperCamelCase						:Any     ,_UpperCamelCase						:int="<unk>"     ,_UpperCamelCase						:str="<s>"     ,_UpperCamelCase						:str="</s>"     ,_UpperCamelCase						:List[str]="</s>"     ,_UpperCamelCase						:List[Any]="<pad>"     ,**_UpperCamelCase						:Any     ,):
									super().__init__(
									    bos_token=_UpperCamelCase     ,eos_token=_UpperCamelCase     ,sep_token=_UpperCamelCase     ,unk_token=_UpperCamelCase     ,pad_token=_UpperCamelCase     ,**_UpperCamelCase     ,)

									try:
											import sacremoses
									except ImportError:
											raise ImportError(
											    """You need to install sacremoses to use BioGptTokenizer. """
											    """See https://pypi.org/project/sacremoses/ for installation."""	)

									snake_case_						:							Tuple					=  'en'
									snake_case_						:							Optional[Any]					=  sacremoses
									# cache of sm.MosesTokenizer instance
									snake_case_						:							str					=  {}
									snake_case_						:							Tuple					=  {}
									with open(_UpperCamelCase     ,encoding="""utf-8"""	) as vocab_handle:
											snake_case_						:							Tuple					=  json.load(_UpperCamelCase	)
									snake_case_						:							Union[str, Any]					=  {v: k for k, v in self.encoder.items()}
									with open(_UpperCamelCase     ,encoding="""utf-8"""	) as merges_handle:
											snake_case_						:							List[str]					=  merges_handle.read().split("""\n"""	)[:-1]
									snake_case_						:							str					=  [tuple(merge.split()[:2]	) for merge in merges]
									snake_case_						:							Union[str, Any]					=  dict(zip(_UpperCamelCase     ,range(len(_UpperCamelCase	)	)	)	)
									snake_case_						:							Optional[int]					=  {}





							@property
							def 						a__      (     self						:Optional[Any]	):
									return len(self.encoder	)





							def 						a__      (     self						:List[Any]	):
									return dict(self.encoder     ,**self.added_tokens_encoder	)





							def 						a__      (     self						:int     ,_UpperCamelCase						:int     ,_UpperCamelCase						:int	):
									if lang not in self.cache_moses_tokenizer:
											snake_case_						:							int					=  self.sm.MosesTokenizer(lang=_UpperCamelCase	)
											snake_case_						:							int					=  moses_tokenizer
									return self.cache_moses_tokenizer[lang].tokenize(
									    _UpperCamelCase     ,aggressive_dash_splits=_UpperCamelCase     ,return_str=_UpperCamelCase     ,escape=_UpperCamelCase	)





							def 						a__      (     self						:str     ,_UpperCamelCase						:Optional[int]     ,_UpperCamelCase						:List[str]	):
									if lang not in self.cache_moses_detokenizer:
											snake_case_						:							Union[str, Any]					=  self.sm.MosesDetokenizer(lang=_UpperCamelCase	)
											snake_case_						:							List[str]					=  moses_detokenizer
									return self.cache_moses_detokenizer[lang].detokenize(_UpperCamelCase	)





							def 						a__      (     self						:Tuple     ,_UpperCamelCase						:Optional[int]	):
									snake_case_						:							List[Any]					=  tuple(token[:-1]	) + (token[-1] + '</w>',)
									if token in self.cache:
											return self.cache[token]
									snake_case_						:							int					=  get_pairs(_UpperCamelCase	)

									if not pairs:
											return token + "</w>"

									while True:
											snake_case_						:							str					=  min(_UpperCamelCase     ,key=lambda _UpperCamelCase	: self.bpe_ranks.get(_UpperCamelCase     ,float("""inf"""	)	)	)
											if bigram not in self.bpe_ranks:
													break
											snake_case_						:							Union[str, Any]					=  bigram
											snake_case_						:							Dict					=  []
											snake_case_						:							Optional[Any]					=  0
											while i < len(_UpperCamelCase	):
													try:
															snake_case_						:							Any					=  word.index(_UpperCamelCase     ,_UpperCamelCase	)
													except ValueError:
															new_word.extend(word[i:]	)
															break
													else:
															new_word.extend(word[i:j]	)
															snake_case_						:							Optional[int]					=  j

													if word[i] == first and i < len(_UpperCamelCase	) - 1 and word[i + 1] == second:
															new_word.append(first + second	)
															i += 2
													else:
															new_word.append(word[i]	)
															i += 1
											snake_case_						:							Tuple					=  tuple(_UpperCamelCase	)
											snake_case_						:							Any					=  new_word
											if len(_UpperCamelCase	) == 1:
													break
											else:
													snake_case_						:							List[str]					=  get_pairs(_UpperCamelCase	)
									snake_case_						:							Union[str, Any]					=  ' '.join(_UpperCamelCase	)
									if word == "\n  </w>":
											snake_case_						:							Union[str, Any]					=  '\n</w>'
									snake_case_						:							Any					=  word
									return word





							def 						a__      (     self						:str     ,_UpperCamelCase						:Optional[int]     ,_UpperCamelCase						:Dict=False	):
									if bypass_tokenizer:
											snake_case_						:							Tuple					=  text.split()
									else:
											snake_case_						:							Dict					=  self.moses_tokenize(_UpperCamelCase     ,self.lang	)

									snake_case_						:							List[str]					=  []
									for token in text:
											if token:
													split_tokens.extend(list(self.bpe(_UpperCamelCase	).split(""" """	)	)	)

									return split_tokens





							def 						a__      (     self						:Optional[int]     ,_UpperCamelCase						:Optional[Any]	):
									return self.encoder.get(_UpperCamelCase     ,self.encoder.get(self.unk_token	)	)





							def 						a__      (     self						:str     ,_UpperCamelCase						:int	):
									return self.decoder.get(_UpperCamelCase     ,self.unk_token	)





							def 						a__      (     self						:str     ,_UpperCamelCase						:List[Any]	):
									snake_case_						:							int					=  [t.replace(""" """     ,""""""	).replace("""</w>"""     ,""" """	) for t in tokens]
									snake_case_						:							List[Any]					=  ''.join(_UpperCamelCase	).split()
									# detokenize
									snake_case_						:							List[Any]					=  self.moses_detokenize(_UpperCamelCase     ,self.lang	)
									return text





							def 						a__      (     self						:Union[str, Any]     ,_UpperCamelCase						:List[int]     ,_UpperCamelCase						:Optional[List[int]] = None	):
									if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
									snake_case_						:							int					=  [self.sep_token_id]
									return sep + token_ids_a + sep + token_ids_a





							def 						a__      (     self						:Union[str, Any]     ,_UpperCamelCase						:List[int]     ,_UpperCamelCase						:Optional[List[int]] = None     ,_UpperCamelCase						:bool = False	):
									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_UpperCamelCase     ,token_ids_a=_UpperCamelCase     ,already_has_special_tokens=_UpperCamelCase	)
									# no bos used in fairseq
									if token_ids_a is not None:
											return [1] + ([0] * len(_UpperCamelCase	)) + [1] + ([0] * len(_UpperCamelCase	))
									return [1] + ([0] * len(_UpperCamelCase	))





							def 						a__      (     self						:Any     ,_UpperCamelCase						:List[int]     ,_UpperCamelCase						:Optional[List[int]] = None	):
									snake_case_						:							List[Any]					=  [self.sep_token_id]

									# no bos used in fairseq
									if token_ids_a is None:
											return len(token_ids_a + sep	) * [0]
									return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 						a__      (     self						:Optional[int]     ,_UpperCamelCase						:str     ,_UpperCamelCase						:Optional[str] = None	):
									if not os.path.isdir(_UpperCamelCase	):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
											return
									snake_case_						:							int					=  os.path.join(
									    _UpperCamelCase     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
									snake_case_						:							Dict					=  os.path.join(
									    _UpperCamelCase     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

									with open(_UpperCamelCase     ,"""w"""     ,encoding="""utf-8"""	) as f:
											f.write(json.dumps(self.encoder     ,indent=2     ,sort_keys=_UpperCamelCase     ,ensure_ascii=_UpperCamelCase	) + """\n"""	)

									snake_case_						:							List[Any]					=  0
									with open(_UpperCamelCase     ,"""w"""     ,encoding="""utf-8"""	) as writer:
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,key=lambda _UpperCamelCase	: kv[1]	):
													if index != token_index:
															logger.warning(
															    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
															    """ Please check that the tokenizer is not corrupted!"""	)
															snake_case_						:							Tuple					=  token_index
													writer.write(""" """.join(_UpperCamelCase	) + """\n"""	)
													index += 1

									return vocab_file, merge_file





							def __getstate__(     self						:Tuple	):
									snake_case_						:							List[str]					=  self.__dict__.copy()
									snake_case_						:							str					=  None
									return state





							def __setstate__(     self						:Any     ,_UpperCamelCase						:List[Any]	):
									snake_case_						:							List[Any]					=  d

									try:
											import sacremoses
									except ImportError:
											raise ImportError(
											    """You need to install sacremoses to use XLMTokenizer. """
											    """See https://pypi.org/project/sacremoses/ for installation."""	)

									snake_case_						:							List[str]					=  sacremoses
import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       if version.parse(lowercase_ ) < version.parse(lowercase_ ):
              if "dev" in min_version:
                     _UpperCamelCase   :				Tuple       =     (
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     _UpperCamelCase   :				str       =     F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(lowercase_ )



