




"""simple docstring"""







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 					SCREAMING_SNAKE_CASE__    (    UpperCamelCase__  , unittest.TestCase    ):
						_a			  =  KandinskyVaaControlnetImgaImgPipeline
						_a			  =  ['image_embeds', 'negative_image_embeds', 'image', 'hint']
						_a			  =  ['image_embeds', 'negative_image_embeds', 'image', 'hint']
						_a			  =  [
						    'generator',
						    'height',
						    'width',
						    'strength',
						    'guidance_scale',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_a			  =  False


						@property
						def      __lowercase       (							self :	Optional[Any]    ):
										return 32


						@property
						def      __lowercase       (							self :	str    ):
										return 32


						@property
						def      __lowercase       (							self :	int    ):
										return self.time_input_dim


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


						@property
						def      __lowercase       (							self :	Union[str, Any]    ):
										return 100


						@property
						def      __lowercase       (							self :	Optional[int]    ):
										torch.manual_seed(0    )

										lowerCAmelCase              = {
										    """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,
										}

										lowerCAmelCase              = UNetaDConditionModel(**lowercase_    )
										return model


						@property
						def      __lowercase       (							self :	List[str]    ):
										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      __lowercase       (							self :	Optional[Any]    ):
										torch.manual_seed(0    )
										lowerCAmelCase              = VQModel(**self.dummy_movq_kwargs    )
										return model


						def      __lowercase       (							self :	Optional[Any]    ):
										lowerCAmelCase              = self.dummy_unet
										lowerCAmelCase              = self.dummy_movq

										lowerCAmelCase              = {
										    """num_train_timesteps""": 1000,
										    """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,
										}

										lowerCAmelCase              = DDIMScheduler(**lowercase_    )

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

										return components


						def      __lowercase       (							self :	int  ,  lowerCAmelCase :	Any  ,  lowerCAmelCase :	Optional[Any]=0    ):
										lowerCAmelCase              = floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(lowercase_    )    ).to(lowercase_    )
										lowerCAmelCase              = floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(seed + 1    )    ).to(
										    lowercase_    )
										# create init_image
										lowerCAmelCase              = floats_tensor((1, 3, 64, 64)  ,  rng=random.Random(lowercase_    )    ).to(lowercase_    )
										lowerCAmelCase              = image.cpu().permute(0  ,  2  ,  3  ,  1    )[0]
										lowerCAmelCase              = Image.fromarray(np.uinta(lowercase_    )    ).convert("""RGB"""    ).resize((256, 256)    )
										# create hint
										lowerCAmelCase              = floats_tensor((1, 3, 64, 64)  ,  rng=random.Random(lowercase_    )    ).to(lowercase_    )

										if str(lowercase_    ).startswith("""mps"""    ):
														lowerCAmelCase              = torch.manual_seed(lowercase_    )
										else:
														lowerCAmelCase              = torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
										lowerCAmelCase              = {
										    """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      __lowercase       (							self :	Optional[int]    ):
										lowerCAmelCase              = """cpu"""

										lowerCAmelCase              = self.get_dummy_components()

										lowerCAmelCase              = self.pipeline_class(**lowercase_    )
										lowerCAmelCase              = pipe.to(lowercase_    )

										pipe.set_progress_bar_config(disable=lowercase_    )

										lowerCAmelCase              = pipe(**self.get_dummy_inputs(lowercase_    )    )
										lowerCAmelCase              = output.images

										lowerCAmelCase              = pipe(
										    **self.get_dummy_inputs(lowercase_    )  ,  return_dict=lowercase_  ,  )[0]

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

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

										lowerCAmelCase              = 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 					SCREAMING_SNAKE_CASE__    (    unittest.TestCase    ):


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




						def      __lowercase       (							self :	int    ):
										lowerCAmelCase              = load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""    )

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

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

										lowerCAmelCase              = """A robot, 4k photo"""

										lowerCAmelCase              = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-prior"""  ,  torch_dtype=torch.floataa    )
										pipe_prior.to(lowercase_    )

										lowerCAmelCase              = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-controlnet-depth"""  ,  torch_dtype=torch.floataa    )
										lowerCAmelCase              = pipeline.to(lowercase_    )

										pipeline.set_progress_bar_config(disable=lowercase_    )

										lowerCAmelCase              = torch.Generator(device="""cpu"""    ).manual_seed(0    )

										lowerCAmelCase       ,  lowerCAmelCase              = pipe_prior(
										    lowercase_  ,  image=lowercase_  ,  strength=0.85  ,  generator=lowercase_  ,  negative_prompt=""""""  ,  ).to_tuple()

										lowerCAmelCase              = pipeline(
										    image=lowercase_  ,  image_embeds=lowercase_  ,  negative_image_embeds=lowercase_  ,  hint=lowercase_  ,  generator=lowercase_  ,  num_inference_steps=100  ,  height=512  ,  width=512  ,  strength=0.5  ,  output_type="""np"""  ,  )

										lowerCAmelCase              = output.images[0]

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

										assert_mean_pixel_difference(lowercase_  ,  lowercase_    )












'''simple docstring'''

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
  from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
  from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
  from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__lowerCAmelCase							:	Dict			=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCAmelCase							:	List[Any]			="main"
# Default branch name
__lowerCAmelCase							:	int			="f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__lowerCAmelCase							:	List[Any]			="aaaaaaa"
# This commit does not exist, so we should 404.

__lowerCAmelCase							:	Optional[int]			="d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCAmelCase							:	Dict			="4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Welcome!"       )
    yield
    print("Bye!"       )





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Bonjour!"       )
    yield
    print("Au revoir!"       )






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     # If the spec is missing, importlib would not be able to import the module dynamically.
     assert transformers.__spec__ is not None
     assert importlib.util.find_spec("transformers"							) is not None






class    UpperCAmelCase	(       unittest.TestCase				):
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :Union[str, Any]							)->							Any:
     with ContextManagers([]							):
         print("Transformers are awesome!"							)
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()						,       "Transformers are awesome!\n"							)
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							Tuple:
     with ContextManagers([context_en()]							):
         print("Transformers are awesome!"							)
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()						,       "Welcome!\nTransformers are awesome!\nBye!\n"							)
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :int							)->							Union[str, Any]:
     with ContextManagers([context_fr(), context_en()]							):
         print("Transformers are awesome!"							)
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()						,       "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"							)
 @require_torch
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
 @require_tf
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)



 @require_flax
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # Flax models don't have labels
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       []							)



