



'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor

class 			UpperCamelCase_ (							unittest.TestCase			):





	def __init__(  self		,		A		,		A=7		,		A=3		,		A=18		,		A=30		,		A=400		,		A=True		,		A=None		,		A=True		,		A=[0.5, 0.5, 0.5]		,		A=[0.5, 0.5, 0.5]		,		)			->			int:
								UpperCAmelCase     :		Optional[int]							     =		size if size is not None else {"""height""": 18, """width""": 18}
								UpperCAmelCase     :		List[str]							     =		parent
								UpperCAmelCase     :		str							     =		batch_size
								UpperCAmelCase     :		int							     =		num_channels
								UpperCAmelCase     :		Optional[Any]							     =		image_size
								UpperCAmelCase     :		str							     =		min_resolution
								UpperCAmelCase     :		Optional[Any]							     =		max_resolution
								UpperCAmelCase     :		Any							     =		do_resize
								UpperCAmelCase     :		Optional[Any]							     =		size
								UpperCAmelCase     :		Dict							     =		do_normalize
								UpperCAmelCase     :		List[Any]							     =		image_mean
								UpperCAmelCase     :		Optional[int]							     =		image_std






	def  _lowercase(  self    )			->			Any:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}




@require_torch
@require_vision
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = DPTImageProcessor if is_vision_available() else None





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		DPTImageProcessingTester(self    )





	@property
	def  _lowercase(  self    )			->			Any:
								return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		str							     =		self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(A		,		"""image_mean"""    )    )
								self.assertTrue(hasattr(A		,		"""image_std"""    )    )
								self.assertTrue(hasattr(A		,		"""do_normalize"""    )    )
								self.assertTrue(hasattr(A		,		"""do_resize"""    )    )
								self.assertTrue(hasattr(A		,		"""size"""    )    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		List[str]							     =		self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size		,		{"""height""": 18, """width""": 18}    )

								UpperCAmelCase     :		Optional[Any]							     =		self.image_processing_class.from_dict(self.image_processor_dict		,		size=42    )
								self.assertEqual(image_processor.size		,		{"""height""": 42, """width""": 42}    )





	def  _lowercase(  self    )			->			List[str]:
								# Initialize image_processing
								UpperCAmelCase     :		List[Any]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								UpperCAmelCase     :		Union[str, Any]							     =		prepare_image_inputs(self.image_processor_tester		,		equal_resolution=A    )
								for image in image_inputs:
															self.assertIsInstance(A		,		Image.Image    )

								# Test not batched input
								UpperCAmelCase     :		Any							     =		image_processing(image_inputs[0]		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)

								# Test batched
								UpperCAmelCase     :		Tuple							     =		image_processing(A		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								# Initialize image_processing
								UpperCAmelCase     :		Union[str, Any]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								UpperCAmelCase     :		Dict							     =		prepare_image_inputs(self.image_processor_tester		,		equal_resolution=A		,		numpify=A    )
								for image in image_inputs:
															self.assertIsInstance(A		,		np.ndarray    )

								# Test not batched input
								UpperCAmelCase     :		Dict							     =		image_processing(image_inputs[0]		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)

								# Test batched
								UpperCAmelCase     :		Union[str, Any]							     =		image_processing(A		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)






	def  _lowercase(  self    )			->			List[Any]:
								# Initialize image_processing
								UpperCAmelCase     :		Optional[int]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								UpperCAmelCase     :		str							     =		prepare_image_inputs(self.image_processor_tester		,		equal_resolution=A		,		torchify=A    )
								for image in image_inputs:
															self.assertIsInstance(A		,		torch.Tensor    )

								# Test not batched input
								UpperCAmelCase     :		Optional[int]							     =		image_processing(image_inputs[0]		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)

								# Test batched
								UpperCAmelCase     :		Optional[int]							     =		image_processing(A		,		return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape		,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )		,		)






'''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 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=12		,		A=7		,		A=True		,		A=True		,		A=True		,		A=99		,		A=32		,		A=32		,		A=2		,		A=4		,		A=37		,		A=0.1		,		A=0.1		,		A=512		,		A=0.0_2		,		A=0		,		A=None		,		)			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		parent
								UpperCAmelCase     :		str							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		int							     =		use_input_mask
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		Dict							     =		vocab_size
								UpperCAmelCase     :		str							     =		hidden_size
								UpperCAmelCase     :		List[Any]							     =		projection_dim
								UpperCAmelCase     :		Tuple							     =		num_hidden_layers
								UpperCAmelCase     :		Dict							     =		num_attention_heads
								UpperCAmelCase     :		Optional[Any]							     =		intermediate_size
								UpperCAmelCase     :		Any							     =		dropout
								UpperCAmelCase     :		List[Any]							     =		attention_dropout
								UpperCAmelCase     :		Optional[Any]							     =		max_position_embeddings
								UpperCAmelCase     :		Tuple							     =		initializer_range
								UpperCAmelCase     :		Optional[Any]							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		bos_token_id





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

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

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

								UpperCAmelCase     :		int							     =		self.get_config()

								return config, input_ids, tf.convert_to_tensor(A    )





	def  _lowercase(  self    )			->			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  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		TFBlipTextModel(config=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		model(A		,		attention_mask=A		,		training=A    )
								UpperCAmelCase     :		int							     =		model(A		,		training=A    )
								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  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		config_and_inputs
								UpperCAmelCase     :		Optional[Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = (TFBlipTextModel,) if is_tf_available() else ()
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		BlipTextModelTester(self    )
								UpperCAmelCase     :		List[str]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=37    )





	def  _lowercase(  self    )			->			Tuple:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A    )





	def  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""Blip does not use inputs_embeds"""    )
	def  _lowercase(  self    )			->			Union[str, Any]:
								pass





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





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





	@slow
	def  _lowercase(  self    )			->			Dict:
								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase     :		Any							     =		TFBlipTextModel.from_pretrained(A    )
															self.assertIsNotNone(A    )






	def  _lowercase(  self		,		A=True    )			->			str:
								super().test_pt_tf_model_equivalence(allow_missing_keys=A    )

