


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:
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				None


SCREAMING_SNAKE_CASE       :		str         =				logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE       :		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
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	int									=VOCAB_FILES_NAMES
   lowercase					:	List[Any]									=PRETRAINED_VOCAB_FILES_MAP
   lowercase					:	str									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase					:	Optional[Any]									=["""input_ids""", """attention_mask"""]
   lowercase					:	Dict									=TaTokenizer

   lowercase					:	List[int]									=[]
   def __init__(     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="<unk>"   ,							UpperCamelCase_="<pad>"   ,							UpperCamelCase_=100   ,							UpperCamelCase_=None   ,							**UpperCamelCase_   ,							):
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
             lowercase_       :Optional[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
             lowercase_       :int     =       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_   ,							)

        lowercase_       :Tuple     =       vocab_file
        lowercase_       :Union[str, Any]     =       False if not self.vocab_file else True
        lowercase_       :List[str]     =       extra_ids
   @staticmethod
   def 		UpperCamelCase  (     UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             lowercase_       :Optional[int]     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        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
        lowercase_       :Dict     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        lowercase_       :Any     =       token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
        else:
             lowercase_       :int     =       token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        lowercase_       :List[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 		UpperCamelCase  (     self		):
        return list(
            set(filter(lambda UpperCamelCase_		: bool(re.search(R'''<extra_id_\d+>'''   ,							UpperCamelCase_		)		) is not None   ,							self.additional_special_tokens		)		)		)




   def 		UpperCamelCase  (     self		):
        return [self.convert_tokens_to_ids(UpperCamelCase_		) for token in self.get_sentinel_tokens()]





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=13   ,							UpperCamelCase_=7   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=99   ,							UpperCamelCase_=32   ,							UpperCamelCase_=5   ,							UpperCamelCase_=4   ,							UpperCamelCase_=37   ,							UpperCamelCase_="gelu"   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=512   ,							UpperCamelCase_=16   ,							UpperCamelCase_=2   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=4   ,							):
        lowercase_       :Union[str, Any]     =       parent
        lowercase_       :int     =       batch_size
        lowercase_       :int     =       seq_length
        lowercase_       :str     =       is_training
        lowercase_       :Dict     =       use_attention_mask
        lowercase_       :List[Any]     =       use_token_type_ids
        lowercase_       :str     =       use_labels
        lowercase_       :str     =       vocab_size
        lowercase_       :Optional[int]     =       hidden_size
        lowercase_       :Dict     =       num_hidden_layers
        lowercase_       :List[str]     =       num_attention_heads
        lowercase_       :int     =       intermediate_size
        lowercase_       :Union[str, Any]     =       hidden_act
        lowercase_       :Optional[int]     =       hidden_dropout_prob
        lowercase_       :Tuple     =       attention_probs_dropout_prob
        lowercase_       :int     =       max_position_embeddings
        lowercase_       :List[Any]     =       type_vocab_size
        lowercase_       :Any     =       type_sequence_label_size
        lowercase_       :Tuple     =       initializer_range
        lowercase_       :Any     =       num_choices
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :List[str]     =       None
        if self.use_attention_mask:
             lowercase_       :Any     =       random_attention_mask([self.batch_size, self.seq_length]		)

        lowercase_       :List[Any]     =       None
        if self.use_token_type_ids:
             lowercase_       :Union[str, Any]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.type_vocab_size		)

        lowercase_       :Union[str, Any]     =       BertConfig(
            vocab_size=self.vocab_size   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							max_position_embeddings=self.max_position_embeddings   ,							type_vocab_size=self.type_vocab_size   ,							is_decoder=UpperCamelCase_   ,							initializer_range=self.initializer_range   ,							)

        return config, input_ids, token_type_ids, attention_mask
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :List[str]     =       config_and_inputs
        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict




   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Dict     =       config_and_inputs

        lowercase_       :str     =       True
        lowercase_       :Dict     =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        lowercase_       :Optional[Any]     =       ids_tensor([self.batch_size, self.seq_length]   ,							vocab_size=2		)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )






@require_flax
class      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	int									=True

   lowercase					:	Dict									=(
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       FlaxBertModelTester(self		)




   @slow
   def 		UpperCamelCase  (     self		):
        # Only check this for base model, not necessary for all model classes.
        # This will also help speed-up tests.
        lowercase_       :Dict     =       FlaxBertModel.from_pretrained('''bert-base-cased'''		)
        lowercase_       :str     =       model(np.ones((1, 1)		)		)
        self.assertIsNotNone(UpperCamelCase_		)

