


"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Optional[Any]=1_2					,				UpperCamelCase_  :					List[Any]=7					,				UpperCamelCase_  :					Optional[Any]=True					,				UpperCamelCase_  :					Dict=True					,				UpperCamelCase_  :					Optional[int]=True					,				UpperCamelCase_  :					List[Any]=9_9					,				UpperCamelCase_  :					Tuple=3_2					,				UpperCamelCase_  :					int=3_2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					List[str]=4					,				UpperCamelCase_  :					List[str]=3_7					,				UpperCamelCase_  :					Tuple=0.1					,				UpperCamelCase_  :					Tuple=0.1					,				UpperCamelCase_  :					List[Any]=5_1_2					,				UpperCamelCase_  :					Union[str, Any]=0.02					,				UpperCamelCase_  :					Optional[int]=0					,				UpperCamelCase_  :					List[str]=None					,				):
           lowerCAmelCase					:	int       =   parent
           lowerCAmelCase					:	Union[str, Any]       =   batch_size
           lowerCAmelCase					:	str       =   seq_length
           lowerCAmelCase					:	Any       =   is_training
           lowerCAmelCase					:	Tuple       =   use_input_mask
           lowerCAmelCase					:	List[Any]       =   use_labels
           lowerCAmelCase					:	List[str]       =   vocab_size
           lowerCAmelCase					:	Optional[Any]       =   hidden_size
           lowerCAmelCase					:	Any       =   projection_dim
           lowerCAmelCase					:	List[str]       =   num_hidden_layers
           lowerCAmelCase					:	Tuple       =   num_attention_heads
           lowerCAmelCase					:	str       =   intermediate_size
           lowerCAmelCase					:	List[str]       =   dropout
           lowerCAmelCase					:	int       =   attention_dropout
           lowerCAmelCase					:	str       =   max_position_embeddings
           lowerCAmelCase					:	Union[str, Any]       =   initializer_range
           lowerCAmelCase					:	List[str]       =   scope
           lowerCAmelCase					:	List[str]       =   bos_token_id






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	List[Any]       =   ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size				)

           lowerCAmelCase					:	int       =   None
           if self.use_input_mask:
                 lowerCAmelCase					:	List[Any]       =   random_attention_mask([self.batch_size, self.seq_length]				)

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

           lowerCAmelCase					:	Tuple       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           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  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	List[Any]       =   TFBlipTextModel(config=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   model(UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				training=UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   model(UpperCamelCase_					,				training=UpperCamelCase_				)
           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  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[int]       =   self.prepare_config_and_inputs()
           lowerCAmelCase					:	Optional[int]       =   config_and_inputs
           lowerCAmelCase					:	str       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict



@require_tf
class 		snake_case_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					(TFBlipTextModel,) if is_tf_available() else ()
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	List[str]       =   BlipTextModelTester(self				)
           lowerCAmelCase					:	int       =   ConfigTester(self					,				config_class=UpperCamelCase_					,				hidden_size=3_7				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           self.config_tester.run_common_tests()






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Dict				):
           pass






     def      lowerCamelCase__     (	self  :					int				):
           pass






     @unittest.skip(reason='''Blip does not use inputs_embeds'''				)
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           pass






     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''				)
     def      lowerCamelCase__     (	self  :					Tuple				):
           pass






     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''				)
     def      lowerCamelCase__     (	self  :					List[Any]				):
           pass






     @slow
     def      lowerCamelCase__     (	self  :					Tuple				):
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase					:	str       =   TFBlipTextModel.from_pretrained(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					Optional[int]=True				):
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase_				)






"""simple docstring"""





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case__		:     Optional[Any]    				=				None

snake_case__		:     Union[str, Any]    				=				logging.get_logger(__name__)
snake_case__		:     List[str]    				=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__		:     Any    				=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case__		:     int    				=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


snake_case__		:     Optional[Any]    				=				'''▁'''



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					BigBirdTokenizer
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']
     __UpperCamelCase  =					[]






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					str="<unk>"					,				UpperCamelCase_  :					str="<s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					int="<pad>"					,				UpperCamelCase_  :					List[Any]="[SEP]"					,				UpperCamelCase_  :					Dict="[MASK]"					,				UpperCamelCase_  :					Any="[CLS]"					,				**UpperCamelCase_  :					Any					,				):
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else bos_token
           lowerCAmelCase					:	int       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else eos_token
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else unk_token
           lowerCAmelCase					:	List[str]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else pad_token
           lowerCAmelCase					:	Any       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else cls_token
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCAmelCase					:	Optional[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else mask_token

           super().__init__(
               UpperCamelCase_					,				tokenizer_file=UpperCamelCase_					,				bos_token=UpperCamelCase_					,				eos_token=UpperCamelCase_					,				unk_token=UpperCamelCase_					,				sep_token=UpperCamelCase_					,				pad_token=UpperCamelCase_					,				cls_token=UpperCamelCase_					,				mask_token=UpperCamelCase_					,				**UpperCamelCase_					,				)

           lowerCAmelCase					:	Optional[int]       =   vocab_file
           lowerCAmelCase					:	Optional[int]       =   False if not self.vocab_file else True






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	str       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None					,				UpperCamelCase_  :					bool = False				):

           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''				)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                 return [1] + ([0] * len(UpperCamelCase_				)) + [1]
           return [1] + ([0] * len(UpperCamelCase_				)) + [1] + ([0] * len(UpperCamelCase_				)) + [1]






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	Tuple       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
           return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = 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
           lowerCAmelCase					:	Optional[int]       =   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_				)

           return (out_vocab_file,)


