
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class     lowercase		:




							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''')

												torch.manual_seed(0)
												lowercase						     =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''')

												torch.manual_seed(0)
												lowercase						     =						UNetaDConditionModel(
												    sample_size=3_2       ,layers_per_block=1       ,block_out_channels=[3_2, 6_4]       ,down_block_types=[
												        '''ResnetDownsampleBlock2D''',
												        '''SimpleCrossAttnDownBlock2D''',
												    ]       ,mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''       ,up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']       ,in_channels=3       ,out_channels=6       ,cross_attention_dim=3_2       ,encoder_hid_dim=3_2       ,attention_head_dim=8       ,addition_embed_type='''text'''       ,addition_embed_type_num_heads=2       ,cross_attention_norm='''group_norm'''       ,resnet_time_scale_shift='''scale_shift'''       ,act_fn='''gelu'''       ,)
												unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

												torch.manual_seed(0)
												lowercase						     =						DDPMScheduler(
												    num_train_timesteps=1_0_0_0       ,beta_schedule='''squaredcos_cap_v2'''       ,beta_start=0.0001       ,beta_end=0.02       ,thresholding=A__       ,dynamic_thresholding_ratio=0.95       ,sample_max_value=1.0       ,prediction_type='''epsilon'''       ,variance_type='''learned_range'''       ,)

												torch.manual_seed(0)
												lowercase						     =						IFWatermarker()

												return {
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "unet": unet,
												    "scheduler": scheduler,
												    "watermarker": watermarker,
												    "safety_checker": None,
												    "feature_extractor": None,
												}




							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''')

												torch.manual_seed(0)
												lowercase						     =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''')

												torch.manual_seed(0)
												lowercase						     =						UNetaDConditionModel(
												    sample_size=3_2       ,layers_per_block=[1, 2]       ,block_out_channels=[3_2, 6_4]       ,down_block_types=[
												        '''ResnetDownsampleBlock2D''',
												        '''SimpleCrossAttnDownBlock2D''',
												    ]       ,mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''       ,up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']       ,in_channels=6       ,out_channels=6       ,cross_attention_dim=3_2       ,encoder_hid_dim=3_2       ,attention_head_dim=8       ,addition_embed_type='''text'''       ,addition_embed_type_num_heads=2       ,cross_attention_norm='''group_norm'''       ,resnet_time_scale_shift='''scale_shift'''       ,act_fn='''gelu'''       ,class_embed_type='''timestep'''       ,mid_block_scale_factor=1.414       ,time_embedding_act_fn='''gelu'''       ,time_embedding_dim=3_2       ,)
												unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

												torch.manual_seed(0)
												lowercase						     =						DDPMScheduler(
												    num_train_timesteps=1_0_0_0       ,beta_schedule='''squaredcos_cap_v2'''       ,beta_start=0.0001       ,beta_end=0.02       ,thresholding=A__       ,dynamic_thresholding_ratio=0.95       ,sample_max_value=1.0       ,prediction_type='''epsilon'''       ,variance_type='''learned_range'''       ,)

												torch.manual_seed(0)
												lowercase						     =						DDPMScheduler(
												    num_train_timesteps=1_0_0_0       ,beta_schedule='''squaredcos_cap_v2'''       ,beta_start=0.0001       ,beta_end=0.02       ,)

												torch.manual_seed(0)
												lowercase						     =						IFWatermarker()

												return {
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "unet": unet,
												    "scheduler": scheduler,
												    "image_noising_scheduler": image_noising_scheduler,
												    "watermarker": watermarker,
												    "safety_checker": None,
												    "feature_extractor": None,
												}




							def 			A__				(    self):
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						self.pipeline_class(**A__)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)

												lowercase						     =						inputs['''prompt''']
												lowercase						     =						inputs['''generator''']
												lowercase						     =						inputs['''num_inference_steps''']
												lowercase						     =						inputs['''output_type''']

												if "image" in inputs:
																	lowercase						     =						inputs['''image''']
												else:
																	lowercase						     =						None

												if "mask_image" in inputs:
																	lowercase						     =						inputs['''mask_image''']
												else:
																	lowercase						     =						None

												if "original_image" in inputs:
																	lowercase						     =						inputs['''original_image''']
												else:
																	lowercase						     =						None

												lowercase     ,						lowercase						     =						pipe.encode_prompt(A__)

												# inputs with prompt converted to embeddings
												lowercase						     =						{
												    '''prompt_embeds''': prompt_embeds,
												    '''negative_prompt_embeds''': negative_prompt_embeds,
												    '''generator''': generator,
												    '''num_inference_steps''': num_inference_steps,
												    '''output_type''': output_type,
												}

												if image is not None:
																	lowercase						     =						image

												if mask_image is not None:
																	lowercase						     =						mask_image

												if original_image is not None:
																	lowercase						     =						original_image

												# set all optional components to None
												for optional_component in pipe._optional_components:
																	setattr(A__       ,A__       ,A__)

												lowercase						     =						pipe(**A__)[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(A__)
																	lowercase						     =						self.pipeline_class.from_pretrained(A__)
																	pipe_loaded.to(A__)
																	pipe_loaded.set_progress_bar_config(disable=A__)

												pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

												for optional_component in pipe._optional_components:
																	self.assertTrue(
																	    getattr(A__       ,A__) is None       ,f'`{optional_component}` did not stay set to None after loading.'       ,)

												lowercase						     =						self.get_dummy_inputs(A__)

												lowercase						     =						inputs['''generator''']
												lowercase						     =						inputs['''num_inference_steps''']
												lowercase						     =						inputs['''output_type''']

												# inputs with prompt converted to embeddings
												lowercase						     =						{
												    '''prompt_embeds''': prompt_embeds,
												    '''negative_prompt_embeds''': negative_prompt_embeds,
												    '''generator''': generator,
												    '''num_inference_steps''': num_inference_steps,
												    '''output_type''': output_type,
												}

												if image is not None:
																	lowercase						     =						image

												if mask_image is not None:
																	lowercase						     =						mask_image

												if original_image is not None:
																	lowercase						     =						original_image

												lowercase						     =						pipe_loaded(**A__)[0]

												lowercase						     =						np.abs(to_np(A__) - to_np(A__)).max()
												self.assertLess(A__       ,1E-4)





							def 			A__				(    self):
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						self.pipeline_class(**A__)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						pipe(**A__)[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(A__)
																	lowercase						     =						self.pipeline_class.from_pretrained(A__)
																	pipe_loaded.to(A__)
																	pipe_loaded.set_progress_bar_config(disable=A__)

												pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						pipe_loaded(**A__)[0]

												lowercase						     =						np.abs(to_np(A__) - to_np(A__)).max()
												self.assertLess(A__       ,1E-4)






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

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 ConditionalDetrImageProcessor







class     lowercase		( unittest.TestCase		):




							def __init__(    self       ,A__       ,A__=7       ,A__=3       ,A__=3_0       ,A__=4_0_0       ,A__=True       ,A__=None       ,A__=True       ,A__=[0.5, 0.5, 0.5]       ,A__=[0.5, 0.5, 0.5]       ,A__=True       ,A__=1 / 2_5_5       ,A__=True       ,):
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowercase						     =						size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						num_channels
												lowercase						     =						min_resolution
												lowercase						     =						max_resolution
												lowercase						     =						do_resize
												lowercase						     =						size
												lowercase						     =						do_normalize
												lowercase						     =						image_mean
												lowercase						     =						image_std
												lowercase						     =						do_rescale
												lowercase						     =						rescale_factor
												lowercase						     =						do_pad




							def 			A__				(    self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}





							def 			A__				(    self       ,A__       ,A__=False):
												if not batched:
																	lowercase						     =						image_inputs[0]
																	if isinstance(A__       ,Image.Image):
																						lowercase     ,						lowercase						     =						image.size
																	else:
																						lowercase     ,						lowercase						     =						image.shape[1], image.shape[2]
																	if w < h:
																						lowercase						     =						int(self.size['''shortest_edge'''] * h / w)
																						lowercase						     =						self.size['''shortest_edge''']
																	elif w > h:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						int(self.size['''shortest_edge'''] * w / h)
																	else:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						self.size['''shortest_edge''']

												else:
																	lowercase						     =						[]
																	for image in image_inputs:
																						lowercase     ,						lowercase						     =						self.get_expected_values([image])
																						expected_values.append((expected_height, expected_width))
																	lowercase						     =						max(A__       ,key=lambda A__: item[0])[0]
																	lowercase						     =						max(A__       ,key=lambda A__: item[1])[1]

												return expected_height, expected_width







@require_torch
@require_vision
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =ConditionalDetrImageProcessor if is_vision_available() else None




							def 			A__				(    self):
												lowercase						     =						ConditionalDetrImageProcessingTester(self)




							@property
							def 			A__				(    self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def 			A__				(    self):
												lowercase						     =						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 			A__				(    self):
												lowercase						     =						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
												self.assertEqual(image_processor.do_pad       ,A__)

												lowercase						     =						self.image_processing_class.from_dict(
												    self.image_processor_dict       ,size=4_2       ,max_size=8_4       ,pad_and_return_pixel_mask=A__)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
												self.assertEqual(image_processor.do_pad       ,A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,Image.Image)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												lowercase						     =						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,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase						     =						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
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase						     =						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
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							@slow
							def 			A__				(    self):
												# prepare image and target
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''image_id''': 3_9_7_6_9, '''annotations''': target}

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))





							@slow
							def 			A__				(    self):
												# prepare image, target and masks_path
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

												lowercase						     =						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor(format='''coco_panoptic''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,masks_path=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify masks
												lowercase						     =						8_2_2_8_7_3
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,A__)
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))




