






'''simple docstring'''





from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

_UpperCamelCase		:		Tuple            = logging.get_logger(__name__)


_UpperCamelCase		:		Tuple            = Dict[str, Any]
_UpperCamelCase		:		str            = List[Prediction]


@add_end_docstrings(__a)
class       snake_case__	( __a):




					def __init__(   self   :						List[Any]							,      *_A   :						str							,      **_A   :						List[Any]	)  ->				Optional[Any]:
								super().__init__(*lowerCAmelCase__							,      **lowerCAmelCase__	)

								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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()	)	)




					def 						A	(   self   :						List[Any]							,      **_A   :						Any	)  ->				List[str]:
								UpperCAmelCase_	:    Union[str, Any]	  =    {}
								if "threshold" in kwargs:
											UpperCAmelCase_	:    Dict	  =    kwargs['''threshold''']
								return {}, {}, postprocess_kwargs




					def __call__(   self   :						Any							,      *_A   :						str							,      **_A   :						List[str]	)  ->				Dict:
								return super().__call__(*lowerCAmelCase__							,      **lowerCAmelCase__	)




					def 						A	(   self   :						Optional[Any]							,      _A   :						Dict	)  ->				Union[str, Any]:
								UpperCAmelCase_	:    str	  =    load_image(lowerCAmelCase__	)
								UpperCAmelCase_	:    Union[str, Any]	  =    torch.IntTensor([[image.height, image.width]]	)
								UpperCAmelCase_	:    Tuple	  =    self.image_processor(images=[image]							,      return_tensors='''pt'''	)
								if self.tokenizer is not None:
											UpperCAmelCase_	:    Optional[Any]	  =    self.tokenizer(text=inputs['''words''']							,      boxes=inputs['''boxes''']							,      return_tensors='''pt'''	)
								UpperCAmelCase_	:    str	  =    target_size
								return inputs




					def 						A	(   self   :						Tuple							,      _A   :						int	)  ->				Optional[int]:
								UpperCAmelCase_	:    List[Any]	  =    model_inputs.pop('''target_size'''	)
								UpperCAmelCase_	:    List[str]	  =    self.model(**lowerCAmelCase__	)
								UpperCAmelCase_	:    Union[str, Any]	  =    outputs.__class__({'''target_size''': target_size, **outputs}	)
								if self.tokenizer is not None:
											UpperCAmelCase_	:    Optional[Any]	  =    model_inputs['''bbox''']
								return model_outputs




					def 						A	(   self   :						Any							,      _A   :						List[str]							,      _A   :						List[Any]=0.9	)  ->				Tuple:
								UpperCAmelCase_	:    int	  =    model_outputs['''target_size''']
								if self.tokenizer is not None:
											# This is a LayoutLMForTokenClassification variant.
											# The OCR got the boxes and the model classified the words.
											UpperCAmelCase_  ,	UpperCAmelCase_	:    Any	  =    target_size[0].tolist()

											def unnormalize(_A   :						str	):
														return self._get_bounding_box(
														    torch.Tensor(
														        [
														            (width * bbox[0] / 10_00),
														            (height * bbox[1] / 10_00),
														            (width * bbox[2] / 10_00),
														            (height * bbox[3] / 10_00),
														        ]	)	)

											UpperCAmelCase_  ,	UpperCAmelCase_	:    int	  =    model_outputs['''logits'''].squeeze(0	).softmax(dim=-1	).max(dim=-1	)
											UpperCAmelCase_	:    Optional[int]	  =    [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
											UpperCAmelCase_	:    List[str]	  =    [unnormalize(lowerCAmelCase__	) for bbox in model_outputs['''bbox'''].squeeze(0	)]
											UpperCAmelCase_	:    Optional[Any]	  =    ['''score''', '''label''', '''box''']
											UpperCAmelCase_	:    Optional[int]	  =    [dict(zip(lowerCAmelCase__							,      lowerCAmelCase__	)	) for vals in zip(scores.tolist()							,      lowerCAmelCase__							,      lowerCAmelCase__	) if vals[0] > threshold]
								else:
											# This is a regular ForObjectDetectionModel
											UpperCAmelCase_	:    str	  =    self.image_processor.post_process_object_detection(lowerCAmelCase__							,      lowerCAmelCase__							,      lowerCAmelCase__	)
											UpperCAmelCase_	:    Any	  =    raw_annotations[0]
											UpperCAmelCase_	:    Optional[Any]	  =    raw_annotation['''scores''']
											UpperCAmelCase_	:    List[str]	  =    raw_annotation['''labels''']
											UpperCAmelCase_	:    Union[str, Any]	  =    raw_annotation['''boxes''']

											UpperCAmelCase_	:    Optional[Any]	  =    scores.tolist()
											UpperCAmelCase_	:    Union[str, Any]	  =    [self.model.config.idalabel[label.item()] for label in labels]
											UpperCAmelCase_	:    List[Any]	  =    [self._get_bounding_box(lowerCAmelCase__	) for box in boxes]

											# {"scores": [...], ...} --> [{"score":x, ...}, ...]
											UpperCAmelCase_	:    Dict	  =    ['''score''', '''label''', '''box''']
											UpperCAmelCase_	:    Tuple	  =    [
											    dict(zip(lowerCAmelCase__							,      lowerCAmelCase__	)	)
											    for vals in zip(raw_annotation['''scores''']							,      raw_annotation['''labels''']							,      raw_annotation['''boxes''']	)
											]

								return annotation





					def 						A	(   self   :						Union[str, Any]							,      _A   :						str	)  ->				Union[str, Any]:
								if self.framework != "pt":
											raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''	)
								UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_	:    Optional[int]	  =    box.int().tolist()
								UpperCAmelCase_	:    List[str]	  =    {
								    '''xmin''': xmin,
								    '''ymin''': ymin,
								    '''xmax''': xmax,
								    '''ymax''': ymax,
								}
								return bbox




"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		KandinskyVaaImgaImgPipeline
		__lowercase							:  str						   =		['''image_embeds''', '''negative_image_embeds''', '''image''']
		__lowercase							:  Dict						   =		[
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		]
		__lowercase							:  Tuple						   =		[
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''strength''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		__lowercase							:  Tuple						   =		False




		@property
		def 	snake_case_     (     self):
				return 3_2




		@property
		def 	snake_case_     (     self):
				return 3_2




		@property
		def 	snake_case_     (     self):
				return self.time_input_dim




		@property
		def 	snake_case_     (     self):
				return self.time_input_dim * 4




		@property
		def 	snake_case_     (     self):
				return 1_0_0




		@property
		def 	snake_case_     (     self):
				torch.manual_seed(0)

				__SCREAMING_SNAKE_CASE            = {
				    """in_channels""": 4,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """image""",
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """encoder_hid_dim""": self.text_embedder_hidden_size,
				    """encoder_hid_dim_type""": """image_proj""",
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": None,
				}

				__SCREAMING_SNAKE_CASE            = UNetaDConditionModel(**lowerCAmelCase__)
				return model




		@property
		def 	snake_case_     (     self):
				return {
				    "block_out_channels": [3_2, 6_4],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 1_2,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}




		@property
		def 	snake_case_     (     self):
				torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = VQModel(**self.dummy_movq_kwargs)
				return model




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.dummy_unet
				__SCREAMING_SNAKE_CASE            = self.dummy_movq

				__SCREAMING_SNAKE_CASE            = {
				    """num_train_timesteps""": 1_0_0_0,
				    """beta_schedule""": """linear""",
				    """beta_start""": 0.0_00_85,
				    """beta_end""": 0.0_12,
				    """clip_sample""": False,
				    """set_alpha_to_one""": False,
				    """steps_offset""": 0,
				    """prediction_type""": """epsilon""",
				    """thresholding""": False,
				}

				__SCREAMING_SNAKE_CASE            = DDIMScheduler(**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = {
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0):
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(seed + 1)).to(
				    lowerCAmelCase__)
				# create init_image
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, 3, 6_4, 6_4)							,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = image.cpu().permute(0							,  2							,  3							,  1)[0]
				__SCREAMING_SNAKE_CASE            = Image.fromarray(np.uinta(lowerCAmelCase__)).convert("""RGB""").resize((2_5_6, 2_5_6))

				if str(lowerCAmelCase__).startswith("""mps"""):
						__SCREAMING_SNAKE_CASE            = torch.manual_seed(lowerCAmelCase__)
				else:
						__SCREAMING_SNAKE_CASE            = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = {
				    """image""": init_image,
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """generator""": generator,
				    """height""": 6_4,
				    """width""": 6_4,
				    """num_inference_steps""": 1_0,
				    """guidance_scale""": 7.0,
				    """strength""": 0.2,
				    """output_type""": """np""",
				}
				return inputs




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """cpu"""

				__SCREAMING_SNAKE_CASE            = self.get_dummy_components()

				__SCREAMING_SNAKE_CASE            = self.pipeline_class(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = pipe.to(lowerCAmelCase__)

				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = pipe(**self.get_dummy_inputs(lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = output.images

				__SCREAMING_SNAKE_CASE            = pipe(
				    **self.get_dummy_inputs(lowerCAmelCase__)							,  return_dict=lowerCAmelCase__							,  )[0]

				__SCREAMING_SNAKE_CASE            = image[0, -3:, -3:, -1]
				__SCREAMING_SNAKE_CASE            = image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)

				__SCREAMING_SNAKE_CASE            = np.array(
				    [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
				assert (
				    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
				), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
				), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



@slow
@require_torch_gpu
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		def 	snake_case_     (     self):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_img2img_frog.npy""")

				__SCREAMING_SNAKE_CASE            = load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
				__SCREAMING_SNAKE_CASE            = """A red cartoon frog, 4k"""

				__SCREAMING_SNAKE_CASE            = KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""							,  torch_dtype=torch.floataa)
				pipe_prior.to(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = KandinskyVaaImgaImgPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-decoder"""							,  torch_dtype=torch.floataa)
				__SCREAMING_SNAKE_CASE            = pipeline.to(lowerCAmelCase__)

				pipeline.set_progress_bar_config(disable=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = torch.Generator(device="""cpu""").manual_seed(0)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = pipe_prior(
				    lowerCAmelCase__							,  generator=lowerCAmelCase__							,  num_inference_steps=5							,  negative_prompt=""""""							,  ).to_tuple()

				__SCREAMING_SNAKE_CASE            = pipeline(
				    image=lowerCAmelCase__							,  image_embeds=lowerCAmelCase__							,  negative_image_embeds=lowerCAmelCase__							,  generator=lowerCAmelCase__							,  num_inference_steps=1_0_0							,  height=7_6_8							,  width=7_6_8							,  strength=0.2							,  output_type="""np"""							,  )

				__SCREAMING_SNAKE_CASE            = output.images[0]

				assert image.shape == (7_6_8, 7_6_8, 3)

				assert_mean_pixel_difference(lowerCAmelCase__							,  lowerCAmelCase__)




