



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


a_								=       """▁"""

a_								=       {"""vocab_file""": """spiece.model"""}

a_								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

a_								=       {
    """google/pegasus-xsum""": 512,
}


a_								=       logging.get_logger(__name__)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	VOCAB_FILES_NAMES

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




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase="<pad>"						,					__UpperCAmelCase="</s>"						,					__UpperCAmelCase="<unk>"						,					__UpperCAmelCase="<mask_2>"						,					__UpperCAmelCase="<mask_1>"						,					__UpperCAmelCase=None						,					__UpperCAmelCase=103						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     offset
    if additional_special_tokens is not None:
       if not isinstance(__UpperCAmelCase						,					__UpperCAmelCase					):
          raise TypeError(
              F"""additional_special_tokens should be of type {type(__UpperCAmelCase					)}, but is"""
              F""" {type(__UpperCAmelCase					)}"""					)

       __lowerCamelCase    				=     (
           ([mask_token_sent] + additional_special_tokens)
           if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
           else additional_special_tokens
       )
       # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
       additional_special_tokens_extended += [
           F"""<unk_{i}>""" for i in range(len(__UpperCAmelCase					)						,					self.offset - 1					)
       ]

       if len(set(__UpperCAmelCase					)					) != len(__UpperCAmelCase					):
          raise ValueError(
              '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
              F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""					)
       __lowerCamelCase    				=     additional_special_tokens_extended
    else:
       __lowerCamelCase    				=     [mask_token_sent] if mask_token_sent is not None else []
       additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset					)]

    __lowerCamelCase    				=     {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=__UpperCAmelCase						,					unk_token=__UpperCAmelCase						,					mask_token=__UpperCAmelCase						,					pad_token=__UpperCAmelCase						,					mask_token_sent=__UpperCAmelCase						,					offset=__UpperCAmelCase						,					additional_special_tokens=__UpperCAmelCase						,					sp_model_kwargs=self.sp_model_kwargs						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     mask_token_sent
    __lowerCamelCase    				=     vocab_file
    __lowerCamelCase    				=     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(__UpperCAmelCase					)

    # add special tokens to encoder dict
    __lowerCamelCase    				=     {
        0: self.pad_token,
        1: self.eos_token,
    }

    if self.mask_token_sent is not None:
       self.encoder.update(
           {
               2: self.mask_token_sent,
               3: self.mask_token,
           }					)

    if self.offset > 0:
       # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
       # mask_token_sent is already added to list -> so start at 1
       self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,					self.offset - 1					)}					)

    __lowerCamelCase    				=     {v: k for k, v in self.encoder.items()}




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return len(self.sp_model					) + self.offset




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
    vocab.update(self.added_tokens_encoder					)
    return vocab




 def __getstate__(    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.__dict__.copy()
    __lowerCamelCase    				=     None
    return state




 def __setstate__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     d

    # for backward compatibility
    if not hasattr(self						,					'''sp_model_kwargs'''					):
       __lowerCamelCase    				=     {}

    __lowerCamelCase    				=     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(self.vocab_file					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.encode(__UpperCAmelCase						,					out_type=__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    if token in self.decoder:
       return self.decoder[token]
    elif token in self.added_tokens_decoder:
       return self.added_tokens_decoder[token]
    __lowerCamelCase    				=     self.sp_model.piece_to_id(__UpperCAmelCase					)
    return sp_id + self.offset




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    if index in self.encoder:
       return self.encoder[index]
    elif index in self.added_tokens_encoder:
       return self.added_tokens_encoder[index]
    else:
       __lowerCamelCase    				=     self.sp_model.IdToPiece(index - self.offset					)
    return token




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     ''''''
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          out_string += self.sp_model.decode(__UpperCAmelCase					) + token
          __lowerCamelCase    				=     []
       else:
          current_sub_tokens.append(__UpperCAmelCase					)
    out_string += self.sp_model.decode(__UpperCAmelCase					)
    return out_string.strip()




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase=False					):



    '''simple docstring'''
    return 1




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     set(self.all_special_ids					)  # call it once instead of inside list comp
    all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

    return [1 if x in all_special_ids else 0 for x in seq]




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False					):



    '''simple docstring'''
    if already_has_special_tokens:
       return self._special_token_mask(__UpperCAmelCase					)
    elif token_ids_a is None:
       return self._special_token_mask(__UpperCAmelCase					) + [1]
    else:
       return self._special_token_mask(token_ids_a + token_ids_a					) + [1]




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=None					):



    '''simple docstring'''
    if token_ids_a is None:
       return token_ids_a + [self.eos_token_id]
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return token_ids_a + token_ids_a + [self.eos_token_id]




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    if not os.path.isdir(__UpperCAmelCase					):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
       return
    __lowerCamelCase    				=     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					) and os.path.isfile(self.vocab_file					):
       copyfile(self.vocab_file						,					__UpperCAmelCase					)
    elif not os.path.isfile(self.vocab_file					):
       with open(__UpperCAmelCase						,					'''wb'''					) as fi:
          __lowerCamelCase    				=     self.sp_model.serialized_model_proto()
          fi.write(__UpperCAmelCase					)

    return (out_vocab_file,)











from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a_								=       logging.get_logger(__name__)

a_								=       {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""t5"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(    self						,					__UpperCAmelCase=32128						,					__UpperCAmelCase=512						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=6						,					__UpperCAmelCase=None						,					__UpperCAmelCase=8						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     d_model
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     (
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     feed_forward_proj
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     self.feed_forward_proj.split('''-'''					)
    __lowerCamelCase    				=     act_info[-1]
    __lowerCamelCase    				=     act_info[0] == '''gated'''

    if len(__UpperCAmelCase					) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase					) > 2:
       raise ValueError(
           F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
           '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
           '''\'gated-gelu\' or \'relu\''''					)

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
       __lowerCamelCase    				=     '''gelu_new'''

    super().__init__(
        pad_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					is_encoder_decoder=__UpperCAmelCase						,					**__UpperCAmelCase						,					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowerCamelCase    				=     '''past_encoder_sequence + sequence'''
       __lowerCamelCase    				=     {0: '''batch'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}

    if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase						,					direction='''inputs'''					)

    return common_inputs




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 13






