
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()

				# fmt: off
				UpperCamelCase          =					['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
				# fmt: on
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)

				UpperCamelCase          =					{
				    'do_resize': True,
				    'size': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				A_	)
				with open(self.image_processor_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						json.dump(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Any:
				"""simple docstring"""
				return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Dict:
				"""simple docstring"""
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[str]:
				"""simple docstring"""
				return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					[np.random.randint(255    ,				size=(3, 30, 400)    ,				dtype=np.uinta	)]

				UpperCamelCase          =					[Image.fromarray(np.moveaxis(A_    ,				0    ,				-1	)	) for x in image_inputs]

				return image_inputs



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					self.get_rust_tokenizer()
				UpperCamelCase          =					self.get_image_processor()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_slow.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname    ,				use_fast=A_	)

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_fast.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor_slow.tokenizer.get_vocab()    ,				tokenizer_slow.get_vocab()	)
				self.assertEqual(processor_fast.tokenizer.get_vocab()    ,				tokenizer_fast.get_vocab()	)
				self.assertEqual(tokenizer_slow.get_vocab()    ,				tokenizer_fast.get_vocab()	)
				self.assertIsInstance(processor_slow.tokenizer    ,				A_	)
				self.assertIsInstance(processor_fast.tokenizer    ,				A_	)

				self.assertEqual(processor_slow.image_processor.to_json_string()    ,				image_processor.to_json_string()	)
				self.assertEqual(processor_fast.image_processor.to_json_string()    ,				image_processor.to_json_string()	)
				self.assertIsInstance(processor_slow.image_processor    ,				A_	)
				self.assertIsInstance(processor_fast.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,				image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				UpperCamelCase          =					self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)'	)
				UpperCamelCase          =					self.get_image_processor(do_normalize=A_	)

				UpperCamelCase          =					OwlViTProcessor.from_pretrained(
				    self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=A_	)

				self.assertEqual(processor.tokenizer.get_vocab()    ,				tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer    ,				A_	)

				self.assertEqual(processor.image_processor.to_json_string()    ,				image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					image_processor(A_    ,				return_tensors='np'	)
				UpperCamelCase          =					processor(images=A_    ,				return_tensors='np'	)

				for key in input_image_proc.keys():
						self.assertAlmostEqual(input_image_proc[key].sum()    ,				input_processor[key].sum()    ,				delta=1e-2	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'

				UpperCamelCase          =					processor(text=A_    ,				return_tensors='np'	)

				UpperCamelCase          =					tokenizer(A_    ,				return_tensors='np'	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key][0].tolist()    ,				encoded_processor[key][0].tolist()	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(text=A_    ,				images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask', 'pixel_values']	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					[['cat', 'nasa badge'], ['person']]
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					len(A_	)
				UpperCamelCase          =					max([len(A_	) for texts in input_texts]	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(batch_size * num_max_text_queries, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					inputs['input_ids']
				UpperCamelCase          =					[
				    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				]

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)
				self.assertListEqual(list(input_ids[0]	)    ,				predicted_ids[0]	)
				self.assertListEqual(list(input_ids[1]	)    ,				predicted_ids[1]	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(images=A_    ,				query_images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['query_pixel_values', 'pixel_values']	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				UpperCamelCase          =					processor.batch_decode(A_	)
				UpperCamelCase          =					tokenizer.batch_decode(A_	)

				self.assertListEqual(A_    ,				A_	)

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    'in_channels': 8,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'image_hint',
				    '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,
				}

				UpperCamelCase          =					UNetaDConditionModel(**A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "AttnDownEncoderBlock2D",
				    ],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1	)	).to(
				    A_	)
				# create init_image
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
				UpperCamelCase          =					Image.fromarray(np.uinta(A_	)	).convert('RGB'	).resize((256, 256)	)
				# create hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.images

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

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

				assert image.shape == (1, 64, 64, 3)

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				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     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				torch_dtype=torch.floataa	)
				UpperCamelCase          =					pipeline.to(A_	)

				pipeline.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					torch.Generator(device='cpu'	).manual_seed(0	)

				UpperCamelCase     ,  UpperCamelCase          =					pipe_prior(
				    A_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(A_    ,				A_	)
