
"""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      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=12		,	lowercase__=7		,	lowercase__=True		,	lowercase__=True		,	lowercase__=True		,	lowercase__=99		,	lowercase__=32		,	lowercase__=32		,	lowercase__=5		,	lowercase__=4		,	lowercase__=37		,	lowercase__=0.1		,	lowercase__=0.1		,	lowercase__=512		,	lowercase__=0.02		,	lowercase__=0		,	lowercase__=None		,	)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	parent
									SCREAMING_SNAKE_CASE				=	batch_size
									SCREAMING_SNAKE_CASE				=	seq_length
									SCREAMING_SNAKE_CASE				=	is_training
									SCREAMING_SNAKE_CASE				=	use_input_mask
									SCREAMING_SNAKE_CASE				=	use_labels
									SCREAMING_SNAKE_CASE				=	vocab_size
									SCREAMING_SNAKE_CASE				=	hidden_size
									SCREAMING_SNAKE_CASE				=	projection_dim
									SCREAMING_SNAKE_CASE				=	num_hidden_layers
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	intermediate_size
									SCREAMING_SNAKE_CASE				=	dropout
									SCREAMING_SNAKE_CASE				=	attention_dropout
									SCREAMING_SNAKE_CASE				=	max_position_embeddings
									SCREAMING_SNAKE_CASE				=	initializer_range
									SCREAMING_SNAKE_CASE				=	scope
									SCREAMING_SNAKE_CASE				=	bos_token_id


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)

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

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

									SCREAMING_SNAKE_CASE				=	self.get_config()

									return config, input_ids, input_mask


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									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 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	BlipTextModel(config=lowercase__			)
									model.to(lowercase__			)
									model.eval()
									with torch.no_grad():
														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	attention_mask=lowercase__			)
														SCREAMING_SNAKE_CASE				=	model(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 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	config_and_inputs
									SCREAMING_SNAKE_CASE				=	{'input_ids': input_ids, 'attention_mask': input_mask}
									return config, inputs_dict






@require_torch
class      UpperCamelCase	(       __magic_name__  ,      unittest.TestCase      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Dict						  =				(BlipTextModel,) if is_torch_available() else ()
				UpperCAmelCase_				:      Dict						  =				False
				UpperCAmelCase_				:      Dict						  =				False
				UpperCAmelCase_				:      Optional[Any]						  =				False


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	BlipTextModelTester(self			)
									SCREAMING_SNAKE_CASE				=	ConfigTester(self		,	config_class=lowercase__		,	hidden_size=37			)


				def 							A					(				self			)    ->    List[Any]:
									"""simple docstring"""







									self.config_tester.run_common_tests()


				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase__			)


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									pass


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									pass


				@unittest.skip(reason='Blip does not use inputs_embeds'			)
				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									pass


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







									pass


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







									pass


				@slow
				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE				=	BlipTextModel.from_pretrained(lowercase__			)
														self.assertIsNotNone(lowercase__			)




				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase__			)


"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


snake_case    =		logging.get_logger(__name__)


snake_case    =		Dict[str, Any]
snake_case    =		List[Prediction]




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									super().__init__(*lowercase__		,	**lowercase__			)

									if self.framework == "tf":
														raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''			)

									requires_backends(self		,	'vision'			)
									self.check_model_type(
									    dict(
									        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)


				def 							A					(				self		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{}
									SCREAMING_SNAKE_CASE				=	{}
									if "subtask" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
									if "threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['threshold']
									if "mask_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(				self		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







									return super().__call__(lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	load_image(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																			SCREAMING_SNAKE_CASE				=	{}
														else:
																			SCREAMING_SNAKE_CASE				=	{'task_inputs': [subtask]}
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'		,	**lowercase__			)
														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    inputs['task_inputs']		,	padding='max_length'		,	max_length=self.model.config.task_seq_len		,	return_tensors=self.framework		,	)['input_ids']
									else:
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'			)
									SCREAMING_SNAKE_CASE				=	target_size
									return inputs


				def 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	None
									if subtask in {"panoptic", None} and hasattr(self.image_processor		,	'post_process_panoptic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor		,	'post_process_instance_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_instance_segmentation

									if fn is not None:
														SCREAMING_SNAKE_CASE				=	fn(
														    lowercase__		,	threshold=lowercase__		,	mask_threshold=lowercase__		,	overlap_mask_area_threshold=lowercase__		,	target_sizes=model_outputs['target_size']		,	)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs['segmentation']

														for segment in outputs["segments_info"]:
																			SCREAMING_SNAKE_CASE				=	(segmentation == segment['id']) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.numpy().astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[segment['label_id']]
																			SCREAMING_SNAKE_CASE				=	segment['score']
																			annotation.append({'score': score, 'label': label, 'mask': mask}			)

									elif subtask in {"semantic", None} and hasattr(self.image_processor		,	'post_process_semantic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_semantic_segmentation(
														    lowercase__		,	target_sizes=model_outputs['target_size']			)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs.numpy()
														SCREAMING_SNAKE_CASE				=	np.unique(lowercase__			)

														for label in labels:
																			SCREAMING_SNAKE_CASE				=	(segmentation == label) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[label]
																			annotation.append({'score': None, 'label': label, 'mask': mask}			)
									else:
														raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
									return annotation
