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

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


UpperCAmelCase							= logging.get_logger(__name__)

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

UpperCAmelCase							= {
    '''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'''},
}

UpperCAmelCase							= {
    '''microsoft/biogpt''': 10_24,
}


def SCREAMING_SNAKE_CASE (   lowercase_:		Optional[Any] ):
       _UpperCamelCase   :				Any       =     set()
       _UpperCamelCase   :				Optional[Any]       =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCamelCase   :				int       =     char
       return pairs





class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int] 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Dict 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		List[Any] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Any 			=       ['input_ids', 'attention_mask']

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

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

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

 @property
 def     __snake_case    (	self				:						Optional[Any]     )       ->							Tuple:
        return len(self.encoder     )

 def     __snake_case    (	self				:						List[Any]     )       ->							Optional[Any]:
        return dict(self.encoder				,	**self.added_tokens_encoder     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int     )       ->							str:
        if lang not in self.cache_moses_tokenizer:
               _UpperCamelCase   :				int       =     self.sm.MosesTokenizer(lang=lowerCamelCase     )
               _UpperCamelCase   :				int       =     moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            lowerCamelCase				,	aggressive_dash_splits=lowerCamelCase				,	return_str=lowerCamelCase				,	escape=lowerCamelCase     )

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

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

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

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

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

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						Dict=False     )       ->							Optional[Any]:
        if bypass_tokenizer:
               _UpperCamelCase   :				Tuple       =     text.split()
        else:
               _UpperCamelCase   :				Dict       =     self.moses_tokenize(lowerCamelCase				,	self.lang     )

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

        return split_tokens

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Optional[Any]     )       ->							Tuple:
        return self.encoder.get(lowerCamelCase				,	self.encoder.get(self.unk_token     )     )

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						int     )       ->							List[Any]:
        return self.decoder.get(lowerCamelCase				,	self.unk_token     )

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

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

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

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

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

        _UpperCamelCase   :				List[Any]       =     0
        with open(lowerCamelCase				,	'w'				,	encoding='utf-8'     ) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,	key=lambda lowerCamelCase     : 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!'     )
                             _UpperCamelCase   :				Tuple       =     token_index
                      writer.write(' '.join(lowerCamelCase     ) + '\n'     )
                      index += 1

        return vocab_file, merge_file

 def __getstate__(	self				:						Tuple     )       ->							List[Any]:
        _UpperCamelCase   :				List[str]       =     self.__dict__.copy()
        _UpperCamelCase   :				str       =     None
        return state

 def __setstate__(	self				:						Any				,	lowerCamelCase				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				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.'     )

        _UpperCamelCase   :				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_ )



