


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowerCAmelCase__						=			logging.get_logger(__name__)

lowerCAmelCase__						=			{'vocab_file': 'vocab.txt'}

lowerCAmelCase__						=			{
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

lowerCAmelCase__						=			{
    'tau/splinter-base': 5_12,
    'tau/splinter-base-qass': 5_12,
    'tau/splinter-large': 5_12,
    'tau/splinter-large-qass': 5_12,
}

lowerCAmelCase__						=			{
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE        =			SplinterTokenizer


     def __init__(						self			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=True			,						__lowerCamelCase="[UNK]"			,						__lowerCamelCase="[SEP]"			,						__lowerCamelCase="[PAD]"			,						__lowerCamelCase="[CLS]"			,						__lowerCamelCase="[MASK]"			,						__lowerCamelCase="[QUESTION]"			,						__lowerCamelCase=True			,						__lowerCamelCase=None			,						**__lowerCamelCase			,						)      ->    Union[str, Any]:
            super().__init__(
                __lowerCamelCase			,						tokenizer_file=__lowerCamelCase			,						do_lower_case=__lowerCamelCase			,						unk_token=__lowerCamelCase			,						sep_token=__lowerCamelCase			,						pad_token=__lowerCamelCase			,						cls_token=__lowerCamelCase			,						mask_token=__lowerCamelCase			,						tokenize_chinese_chars=__lowerCamelCase			,						strip_accents=__lowerCamelCase			,						additional_special_tokens=(question_token,)			,						**__lowerCamelCase			,						)

            _A				:	Tuple       =			json.loads(self.backend_tokenizer.normalizer.__getstate__())
            if (
                pre_tok_state.get("lowercase"			,						__lowerCamelCase) != do_lower_case
                or pre_tok_state.get("strip_accents"			,						__lowerCamelCase) != strip_accents
            ):
                   _A				:	Dict       =			getattr(__lowerCamelCase			,						pre_tok_state.pop("type"))
                   _A				:	List[str]       =			do_lower_case
                   _A				:	Any       =			strip_accents
                   _A				:	int       =			pre_tok_class(**__lowerCamelCase)

            _A				:	int       =			do_lower_case


     @property
     def 			_lowerCamelCase			(						self)      ->    str:
            return self.convert_tokens_to_ids(self.question_token)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

            _A				:	Tuple       =			[self.cls_token_id]
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if self.padding_side == "right":
                   # Input is question-then-context
                   return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
            else:
                   # Input is context-then-question
                   return cls + token_ids_a + sep + token_ids_a + question_suffix + sep


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.cls_token_id]
            _A				:	Union[str, Any]       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if token_ids_a is None:
                   return len(cls + token_ids_a + sep) * [0]

            if self.padding_side == "right":
                   # Input is question-then-context
                   return len(cls + token_ids_a + question_suffix + sep) * [0] + len(token_ids_a + sep) * [1]
            else:
                   # Input is context-then-question
                   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + question_suffix + sep) * [1]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    Tuple[str]:
            _A				:	Tuple       =			self._tokenizer.model.save(__lowerCamelCase			,						name=__lowerCamelCase)
            return tuple(__lowerCamelCase)




import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import BlipTextModel
    from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=0			,						__lowerCamelCase=None			,						)      ->    str:
            _A				:	Optional[int]       =			parent
            _A				:	Optional[int]       =			batch_size
            _A				:	Optional[int]       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Optional[int]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Dict       =			hidden_size
            _A				:	Optional[Any]       =			projection_dim
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	List[str]       =			intermediate_size
            _A				:	Any       =			dropout
            _A				:	List[str]       =			attention_dropout
            _A				:	str       =			max_position_embeddings
            _A				:	List[Any]       =			initializer_range
            _A				:	int       =			scope
            _A				:	List[str]       =			bos_token_id


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			None
            if self.use_input_mask:
                   _A				:	Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length])

            if input_mask is not None:
                   _A   ,      _A				:	int       =			input_mask.shape
                   _A				:	Optional[Any]       =			np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
                   for batch_idx, start_index in enumerate(__lowerCamelCase):
                          _A				:	Tuple       =			1
                          _A				:	List[str]       =			0

            _A				:	str       =			self.get_config()

            return config, input_ids, input_mask


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            return BlipTextConfig(
                vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						projection_dim=self.projection_dim			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						dropout=self.dropout			,						attention_dropout=self.attention_dropout			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						bos_token_id=self.bos_token_id			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			BlipTextModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            with torch.no_grad():
                   _A				:	int       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)
                   _A				:	List[str]       =			model(__lowerCamelCase)
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
            self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size))


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Dict       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	str       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(BlipTextModel,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			BlipTextModelTester(self)
            _A				:	List[str]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     @unittest.skip(reason="Blip does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Union[str, Any]       =			BlipTextModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase)
