


"""simple docstring"""



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:
  __snake_case   			=    None


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case   			=    {
    """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
__snake_case   			=    {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Tuple							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Optional[Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       Union[str, Any]							  =  ['''input_ids''', '''attention_mask''']
   __UpperCAmelCase :       Optional[Any]							  =  TaTokenizer

   __UpperCAmelCase :       List[int]							  =  []







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__="</s>"						,	UpperCamelCase__="<unk>"						,	UpperCamelCase__="<pad>"						,	UpperCamelCase__=100						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		List[Any]:

       '''simple docstring'''




       if extra_ids > 0 and additional_special_tokens is None:
           snake_case    :      List[Any]      					=    [F'<extra_id_{i}>' for i in range(UpperCamelCase__						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           snake_case    :      Optional[Any]      					=    len(set(filter(lambda UpperCamelCase__						: bool("extra_id_" in str(UpperCamelCase__						)						)						,	UpperCamelCase__						)						)						)
           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__(
           UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	pad_token=UpperCamelCase__						,	extra_ids=UpperCamelCase__						,	additional_special_tokens=UpperCamelCase__						,	**UpperCamelCase__						,	)

       snake_case    :      int      					=    vocab_file
       snake_case    :      Union[str, Any]      					=    False if not self.vocab_file else True
       snake_case    :      int      					=    extra_ids







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           snake_case    :      Dict      					=    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."						,	UpperCamelCase__						,	)

       return max_model_length







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = 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(UpperCamelCase__						):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
           return
       snake_case    :      List[str]      					=    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__						):
           copyfile(self.vocab_file						,	UpperCamelCase__						)
           logger.info(F'Copy vocab file to {out_vocab_file}'						)

       return (out_vocab_file,)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      str      					=    token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
       else:
           snake_case    :      List[Any]      					=    token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [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  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




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









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  '''xmod'''







   def __init__(	self						,	UpperCamelCase__=3_0522						,	UpperCamelCase__=768						,	UpperCamelCase__=12						,	UpperCamelCase__=12						,	UpperCamelCase__=3072						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=1e-12						,	UpperCamelCase__=1						,	UpperCamelCase__=0						,	UpperCamelCase__=2						,	UpperCamelCase__="absolute"						,	UpperCamelCase__=True						,	UpperCamelCase__=None						,	UpperCamelCase__=False						,	UpperCamelCase__=2						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=("en_XX",)						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		int:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,	bos_token_id=UpperCamelCase__						,	eos_token_id=UpperCamelCase__						,	**UpperCamelCase__						)

       snake_case    :      List[Any]      					=    vocab_size
       snake_case    :      List[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Any      					=    num_attention_heads
       snake_case    :      List[str]      					=    hidden_act
       snake_case    :      Union[str, Any]      					=    intermediate_size
       snake_case    :      int      					=    hidden_dropout_prob
       snake_case    :      Any      					=    attention_probs_dropout_prob
       snake_case    :      Optional[int]      					=    max_position_embeddings
       snake_case    :      Tuple      					=    type_vocab_size
       snake_case    :      List[str]      					=    initializer_range
       snake_case    :      int      					=    layer_norm_eps
       snake_case    :      Optional[Any]      					=    position_embedding_type
       snake_case    :      int      					=    use_cache
       snake_case    :      Dict      					=    classifier_dropout
       snake_case    :      Dict      					=    pre_norm
       snake_case    :      Union[str, Any]      					=    adapter_reduction_factor
       snake_case    :      Any      					=    adapter_layer_norm
       snake_case    :      Optional[int]      					=    adapter_reuse_layer_norm
       snake_case    :      List[Any]      					=    ln_before_adapter
       snake_case    :      str      					=    list(UpperCamelCase__						)
       snake_case    :      int      					=    default_language







class _lowerCAmelCase				(      snake_case_	):







   @property
   def  lowerCamelCase		(	self						)	->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case    :      Optional[int]      					=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case    :      Optional[Any]      					=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]						)





