


"""simple docstring"""







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCamelCase_   :   Dict									=  logging.get_logger(__name__)


lowerCamelCase_   :   int									=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase_   :   Optional[Any]									=  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_   :   Optional[Any]									=  {
    """allenai/led-base-16384""": 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _A						(						):







    """simple docstring"""
    a 		=(
        list(range(ord('''!'''		)       ,    ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)       ,    ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)       ,    ord('''ÿ'''		) + 1		)		)
    )
    a 		=bs[:]
    a 		=0
    for b in range(2**8		):
        if b not in bs:
            bs.append(lowercase		)
            cs.append(2**8 + n		)
            n += 1
    a 		=[chr(lowercase		) for n in cs]
    return dict(zip(lowercase       ,    lowercase		)		)
def _A						(						lowercase		):







    """simple docstring"""
    a 		=set()
    a 		=word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        a 		=char
    return pairs






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  VOCAB_FILES_NAMES
 __lowerCAmelCase 					=  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase 					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase 					=  ["input_ids", "attention_mask"]


 def __init__(		self ,    __A ,    __A ,    __A="replace" ,    __A="<s>" ,    __A="</s>" ,    __A="</s>" ,    __A="<s>" ,    __A="<unk>" ,    __A="<pad>" ,    __A="<mask>" ,    __A=False ,    **__A ,    )					->     List[Any]:
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else bos_token
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else eos_token
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else sep_token
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else cls_token
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else unk_token
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     a 		=AddedToken(__A ,    lstrip=__A ,    rstrip=__A						) if isinstance(__A ,    __A						) else mask_token

     super().__init__(
         errors=__A ,    bos_token=__A ,    eos_token=__A ,    unk_token=__A ,    sep_token=__A ,    cls_token=__A ,    pad_token=__A ,    mask_token=__A ,    add_prefix_space=__A ,    **__A ,    )

     with open(__A ,    encoding='''utf-8'''						) as vocab_handle:
         a 		=json.load(__A						)
     a 		={v: k for k, v in self.encoder.items()}
     a 		=errors  # how to handle errors in decoding
     a 		=bytes_to_unicode()
     a 		={v: k for k, v in self.byte_encoder.items()}
     with open(__A ,    encoding='''utf-8'''						) as merges_handle:
         a 		=merges_handle.read().split('''\n'''						)[1:-1]
     a 		=[tuple(merge.split()						) for merge in bpe_merges]
     a 		=dict(zip(__A ,    range(len(__A						)						)						)						)
     a 		={}
     a 		=add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     a 		=re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''						)


 @property
 # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     return len(self.encoder						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     return dict(self.encoder ,    **self.added_tokens_encoder						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Union[str, Any]:
     if token in self.cache:
         return self.cache[token]
     a 		=tuple(__A						)
     a 		=get_pairs(__A						)

     if not pairs:
         return token

     while True:
         a 		=min(__A ,    key=lambda __A						: self.bpe_ranks.get(__A ,    float('''inf'''						)						)						)
         if bigram not in self.bpe_ranks:
             break
         a		,      a 		=bigram
         a 		=[]
         a 		=0
         while i < len(__A						):
             try:
                 a 		=word.index(__A ,    __A						)
             except ValueError:
                 new_word.extend(word[i:]						)
                 break
             else:
                 new_word.extend(word[i:j]						)
                 a 		=j

             if word[i] == first and i < len(__A						) - 1 and word[i + 1] == second:
                 new_word.append(first + second						)
                 i += 2
             else:
                 new_word.append(word[i]						)
                 i += 1
         a 		=tuple(__A						)
         a 		=new_word
         if len(__A						) == 1:
             break
         else:
             a 		=get_pairs(__A						)
     a 		=''' '''.join(__A						)
     a 		=word
     return word


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Optional[Any]:
     a 		=[]
     for token in re.findall(self.pat ,    __A						):
         a 		=''''''.join(
             self.byte_encoder[b] for b in token.encode('''utf-8'''						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
         bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A						).split(''' '''						)						)
     return bpe_tokens


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     List[Any]:
     return self.encoder.get(__A ,    self.encoder.get(self.unk_token						)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Optional[int]:
     return self.decoder.get(__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Any:
     a 		=''''''.join(__A						)
     a 		=bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8''' ,    errors=self.errors						)
     return text


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None						)					->     Tuple[str]:
     if not os.path.isdir(__A						):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
         return
     a 		=os.path.join(
         __A ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
     a 		=os.path.join(
         __A ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

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

     a 		=0
     with open(__A ,    '''w''' ,    encoding='''utf-8'''						) as writer:
         writer.write('''#version: 0.2\n'''						)
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,    key=lambda __A						: 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!'''						)
                 a 		=token_index
             writer.write(''' '''.join(__A						) + '''\n'''						)
             index += 1

     return vocab_file, merge_file


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None						)					->     List[int]:
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     a 		=[self.cls_token_id]
     a 		=[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = False						)					->     List[int]:
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=__A ,    token_ids_a=__A ,    already_has_special_tokens=__A						)

     if token_ids_a is None:
         return [1] + ([0] * len(__A						)) + [1]
     return [1] + ([0] * len(__A						)) + [1, 1] + ([0] * len(__A						)) + [1]


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None						)					->     List[int]:
     a 		=[self.sep_token_id]
     a 		=[self.cls_token_id]

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


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=False ,    **__A						)					->     str:
     a 		=kwargs.pop('''add_prefix_space''' ,    self.add_prefix_space						)
     if (is_split_into_words or add_prefix_space) and (len(__A						) > 0 and not text[0].isspace()):
         a 		=''' ''' + text
     return (text, kwargs)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = PaddingStrategy.DO_NOT_PAD ,    __A = None ,    __A = None ,    )					->     dict:
     a 		=super()._pad(
         encoded_inputs=__A ,    max_length=__A ,    padding_strategy=__A ,    pad_to_multiple_of=__A ,    return_attention_mask=__A ,    )

     # Load from model defaults
     if return_attention_mask is None:
         a 		='''attention_mask''' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
         a 		=encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         a 		=len(encoded_inputs['''global_attention_mask''']						) != len(__A						)

         if needs_to_be_padded:
             a 		=len(__A						) - len(encoded_inputs['''global_attention_mask''']						)

             if self.padding_side == "right":
                 # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                 a 		=(
                     encoded_inputs['''global_attention_mask'''] + [-1] * difference
                 )
             elif self.padding_side == "left":
                 a 		=[-1] * difference + encoded_inputs[
                     '''global_attention_mask'''
                 ]
             else:
                 raise ValueError('''Invalid padding strategy:''' + str(self.padding_side						)						)

     return encoded_inputs






from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


