




'''simple docstring'''







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,    __UpperCamelCase,    __UpperCamelCase=12,    __UpperCamelCase=7,    __UpperCamelCase=True,    __UpperCamelCase=True,    __UpperCamelCase=True,    __UpperCamelCase=99,    __UpperCamelCase=32,    __UpperCamelCase=32,    __UpperCamelCase=5,    __UpperCamelCase=4,    __UpperCamelCase=37,    __UpperCamelCase=0.1,    __UpperCamelCase=0.1,    __UpperCamelCase=512,    __UpperCamelCase=0.02,    __UpperCamelCase=0,    __UpperCamelCase=None,    ):
							_snake_case      			=			parent
							_snake_case      			=			batch_size
							_snake_case      			=			seq_length
							_snake_case      			=			is_training
							_snake_case      			=			use_input_mask
							_snake_case      			=			use_labels
							_snake_case      			=			vocab_size
							_snake_case      			=			hidden_size
							_snake_case      			=			projection_dim
							_snake_case      			=			num_hidden_layers
							_snake_case      			=			num_attention_heads
							_snake_case      			=			intermediate_size
							_snake_case      			=			dropout
							_snake_case      			=			attention_dropout
							_snake_case      			=			max_position_embeddings
							_snake_case      			=			initializer_range
							_snake_case      			=			scope
							_snake_case      			=			bos_token_id






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			ids_tensor([self.batch_size, self.seq_length],    self.vocab_size					)

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

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

							_snake_case      			=			self.get_config()

							return config, input_ids, input_mask






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							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,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							_snake_case      			=			BlipTextModel(config=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							model.eval()
							with torch.no_grad():
									_snake_case      			=			model(_lowerCamelCase,    attention_mask=_lowerCamelCase					)
									_snake_case      			=			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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.prepare_config_and_inputs()
							_snake_case			,       _snake_case			,       _snake_case      			=			config_and_inputs
							_snake_case      			=			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class 	lowerCamelCase_				(			_lowerCAmelCase,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					(BlipTextModel,) if is_torch_available() else ()
					lowercase__       =					False
					lowercase__       =					False
					lowercase__       =					False






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			BlipTextModelTester(self					)
							_snake_case      			=			ConfigTester(self,    config_class=_lowerCamelCase,    hidden_size=37					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							self.config_tester.run_common_tests()






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							pass






					@unittest.skip(reason="Blip does not use inputs_embeds"					)
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							pass






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






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






					@slow
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case      			=			BlipTextModel.from_pretrained(_lowerCamelCase					)
									self.assertIsNotNone(_lowerCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							super().test_pt_tf_model_equivalence(allow_missing_keys=_lowerCamelCase					)













"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




