




import os
import re
import warnings
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_ta import TaTokenizer
else:
 lowerCamelCase							:						int					       =  None


lowerCamelCase							:						int					       =  logging.get_logger(__name__)

lowerCamelCase							:						Optional[Any]					       =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase							:						List[Any]					       =  {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:						Optional[int]					       =  {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		VOCAB_FILES_NAMES
 UpperCamelCase		  =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		  =		['''input_ids''', '''attention_mask''']
 UpperCamelCase		  =		TaTokenizer

 UpperCamelCase		  =		[]

 def __init__(							self   :    str		,				A_   :    int=None		,				A_   :    Optional[int]=None		,				A_   :    Optional[Any]="</s>"		,				A_   :    str="<unk>"		,				A_   :    Union[str, Any]="<pad>"		,				A_   :    Union[str, Any]=100		,				A_   :    Optional[int]=None		,				**A_   :    List[str]		,				)  ->      Optional[int]:

       """simple docstring"""






       if extra_ids > 0 and additional_special_tokens is None:
             lowerCamelCase_							=       [f"""<extra_id_{i}>""" for i in range(A_    )]
       elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             lowerCamelCase_							=       len(set(filter(lambda A_    : bool('extra_id_' in str(A_    )    )		,				A_    )    )    )
             if extra_tokens != extra_ids:
                   raise ValueError(
                       f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                       ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                       ' tokens'    )

       super().__init__(
           A_		,				tokenizer_file=A_		,				eos_token=A_		,				unk_token=A_		,				pad_token=A_		,				extra_ids=A_		,				additional_special_tokens=A_		,				**A_		,				)

       lowerCamelCase_							=       vocab_file
       lowerCamelCase_							=       False if not self.vocab_file else True
       lowerCamelCase_							=       extra_ids

 @staticmethod
 def 							a__					(							A_   :    List[str]		,				A_   :    List[str]		,				A_   :    Any    )  ->      Optional[Any]:

       """simple docstring"""






       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             lowerCamelCase_							=       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                   return init_max_model_length
             elif init_max_model_length is None:
                   warnings.warn(
                       'This tokenizer was incorrectly instantiated with a model max length of'
                       f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                       ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                       ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                       f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                       f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                       f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                       ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                       ' instantiate this tokenizer with `model_max_length` set to your preferred value.'		,				A_		,				)

       return max_model_length

 def 							a__					(							self   :    Any		,				A_   :    str		,				A_   :    Optional[str] = None    )  ->      Tuple[str]:

       """simple docstring"""






       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(A_    ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
       lowerCamelCase_							=       os.path.join(
           A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ):
             copyfile(self.vocab_file		,				A_    )
             logger.info(f"""Copy vocab file to {out_vocab_file}"""    )

       return (out_vocab_file,)

 def 							a__					(							self   :    Tuple		,				A_   :    List[int]		,				A_   :    Optional[List[int]] = None    )  ->      List[int]:

       """simple docstring"""






       lowerCamelCase_							=       token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
       else:
             lowerCamelCase_							=       token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a

 def 							a__					(							self   :    str		,				A_   :    List[int]		,				A_   :    Optional[List[int]] = None    )  ->      List[int]:

       """simple docstring"""






       lowerCamelCase_							=       [self.eos_token_id]

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

 def 							a__					(							self   :    Dict    )  ->      Optional[Any]:

       """simple docstring"""






       return list(
           set(filter(lambda A_    : bool(re.search(r'<extra_id_\d+>'		,				A_    )    ) is not None		,				self.additional_special_tokens    )    )    )





 def 							a__					(							self   :    int    )  ->      Dict:

       """simple docstring"""






       return [self.convert_tokens_to_ids(A_    ) for token in self.get_sentinel_tokens()]





from math import factorial


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					float       ):
       if successes > trials:
              raise ValueError("successes must be lower or equal to trials"       )
       if trials < 0 or successes < 0:
              raise ValueError("the function is defined for non-negative integers"       )
       if not isinstance(__lowerCamelCase							,   __lowerCamelCase       ) or not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              raise ValueError("the function is defined for non-negative integers"       )
       if not 0 < prob < 1:
              raise ValueError("prob has to be in range of 1 - 0"       )
       snake_case			:  Tuple     							= (prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case			:  str     							= float(factorial(__lowerCamelCase       )       )
       coefficient /= factorial(__lowerCamelCase       ) * factorial(trials - successes       )
       return probability * coefficient


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print("""Probability of 2 successes out of 4 trails""")
   print("""with probability of 0.75 is:""", end=""" """)
   print(binomial_distribution(2, 4, 0.75))





