import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=["image_processor", "tokenizer"]
			UpperCamelCase__    							="ViltImageProcessor"
			UpperCamelCase__    							=("BertTokenizer", "BertTokenizerFast")





			def __init__(       self	:	Tuple  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	Union[str, Any]=None  ,  **lowercase__	:	int						):
										_lowerCAmelCase							=							None
										if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,  lowercase__  ,  )
																	_lowerCAmelCase							=							kwargs.pop('feature_extractor'						)

										_lowerCAmelCase							=							image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																	raise ValueError('You need to specify an `image_processor`.'						)
										if tokenizer is None:
																	raise ValueError('You need to specify a `tokenizer`.'						)

										super().__init__(lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							self.image_processor





			def __call__(       self	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,  lowercase__	:	bool = True  ,  lowercase__	:	Union[bool, str, PaddingStrategy] = False  ,  lowercase__	:	Union[bool, str, TruncationStrategy] = None  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	int = 0  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	Optional[bool] = None  ,  lowercase__	:	Optional[bool] = None  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = True  ,  lowercase__	:	Optional[Union[str, TensorType]] = None  ,  **lowercase__	:	Optional[int]  ,  ):
										_lowerCAmelCase							=							self.tokenizer(
										    text=lowercase__  ,  add_special_tokens=lowercase__  ,  padding=lowercase__  ,  truncation=lowercase__  ,  max_length=lowercase__  ,  stride=lowercase__  ,  pad_to_multiple_of=lowercase__  ,  return_token_type_ids=lowercase__  ,  return_attention_mask=lowercase__  ,  return_overflowing_tokens=lowercase__  ,  return_special_tokens_mask=lowercase__  ,  return_offsets_mapping=lowercase__  ,  return_length=lowercase__  ,  verbose=lowercase__  ,  return_tensors=lowercase__  ,  **lowercase__  ,  )
										# add pixel_values + pixel_mask
										_lowerCAmelCase							=							self.image_processor(lowercase__  ,  return_tensors=lowercase__						)
										encoding.update(lowercase__						)

										return encoding





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]  ,  *lowercase__	:	Tuple  ,  **lowercase__	:	List[Any]						):
										return self.tokenizer.batch_decode(*lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  *lowercase__	:	Union[str, Any]  ,  **lowercase__	:	List[str]						):
										return self.tokenizer.decode(*lowercase__  ,  **lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							self.tokenizer.model_input_names
										_lowerCAmelCase							=							self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,  lowercase__  ,  )
										return self.image_processor_class





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,  lowercase__  ,  )
										return self.image_processor







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        lowerCamelCase__				:





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[str]=12  ,  lowercase__	:	Optional[int]=7  ,  lowercase__	:	List[Any]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	List[str]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Tuple=32  ,  lowercase__	:	Dict=2  ,  lowercase__	:	Optional[int]=4  ,  lowercase__	:	str=37  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Union[str, Any]=5_12  ,  lowercase__	:	List[str]=0.0_2  ,  lowercase__	:	Tuple=0  ,  lowercase__	:	List[Any]=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							projection_dim
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							dropout
										_lowerCAmelCase							=							attention_dropout
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							bos_token_id





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

										_lowerCAmelCase							=							None
										if self.use_input_mask:
																	_lowerCAmelCase							=							random_attention_mask([self.batch_size, self.seq_length]						)

										if input_mask is not None:
																	_lowerCAmelCase							=							input_mask.numpy()
																	_lowerCAmelCase			,		_lowerCAmelCase							=							input_mask.shape
																	_lowerCAmelCase							=							np.random.randint(1  ,  seq_length - 1  ,  size=(batch_size,)						)
																	for batch_idx, start_index in enumerate(lowercase__						):
																								_lowerCAmelCase							=							1
																								_lowerCAmelCase							=							0

										_lowerCAmelCase							=							self.get_config()

										return config, input_ids, tf.convert_to_tensor(lowercase__						)





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







@require_tf
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(TFBlipTextModel,) if is_tf_available() else ()
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							BlipTextModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  hidden_size=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





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





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





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





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							TFBlipTextModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Any=True						):
										super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase__						)






