
import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
					import jax

					from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor


class     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=100    ,				A_=13    ,				A_=30    ,				A_=2    ,				A_=3    ,				A_=True    ,				A_=True    ,				A_=32    ,				A_=5    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=10    ,				A_=0.02    ,				A_=3    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					patch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range

				# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				UpperCamelCase          =					(image_size // patch_size) ** 2
				UpperCamelCase          =					num_patches + 1



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.type_sequence_label_size	)

				UpperCamelCase          =					BeitConfig(
				    vocab_size=self.vocab_size    ,				image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				hidden_size=self.hidden_size    ,				num_hidden_layers=self.num_hidden_layers    ,				num_attention_heads=self.num_attention_heads    ,				intermediate_size=self.intermediate_size    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				is_decoder=A_    ,				initializer_range=self.initializer_range    ,				)

				return config, pixel_values, labels



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitModel(config=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitForMaskedImageModeling(config=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length - 1, self.vocab_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.type_sequence_label_size
				UpperCamelCase          =					FlaxBeitForImageClassification(config=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				UpperCamelCase          =					1
				UpperCamelCase          =					FlaxBeitForImageClassification(A_	)

				UpperCamelCase          =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
				UpperCamelCase          =					model(A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,    
				)          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_flax
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	int     					=    (
		    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
		)



		def __UpperCamelCase			(			self	)  -> None:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					inspect.signature(model.__call__	)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase          =					[*signature.parameters.keys()]

						UpperCamelCase          =					['pixel_values']
						self.assertListEqual(arg_names[:1]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__	):
								UpperCamelCase          =					self._prepare_for_class(A_    ,				A_	)
								UpperCamelCase          =					model_class(A_	)

								@jax.jit
								def model_jitted(A_    ,				**A_	):
										return model(pixel_values=A_    ,				**A_	)

								with self.subTest('JIT Enabled'	):
										UpperCamelCase          =					model_jitted(**A_	).to_tuple()

								with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
												UpperCamelCase          =					model_jitted(**A_	).to_tuple()

								self.assertEqual(len(A_	)    ,				len(A_	)	)
								for jitted_output, output in zip(A_    ,				A_	):
										self.assertEqual(jitted_output.shape    ,				output.shape	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*A_	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				for model_class_name in self.all_model_classes:
						UpperCamelCase          =					model_class_name.from_pretrained('microsoft/beit-base-patch16-224'	)
						UpperCamelCase          =					model(np.ones((1, 3, 224, 224)	)	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  Tuple:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


@require_vision
@require_flax
class     lowercase (		unittest.TestCase       ):



		@cached_property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'	) if is_vision_available() else None



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='np'	).pixel_values

				# prepare bool_masked_pos
				UpperCamelCase          =					np.ones((1, 196)    ,				dtype=A_	)

				# forward pass
				UpperCamelCase          =					model(pixel_values=A_    ,				bool_masked_pos=A_	)
				UpperCamelCase          =					outputs.logits

				# verify the logits
				UpperCamelCase          =					(1, 196, 8_192)
				self.assertEqual(logits.shape    ,				A_	)

				UpperCamelCase          =					np.array(
				    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]	)

				self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]    ,				A_    ,				atol=1e-2	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='np'	)

				# forward pass
				UpperCamelCase          =					model(**A_	)
				UpperCamelCase          =					outputs.logits

				# verify the logits
				UpperCamelCase          =					(1, 1_000)
				self.assertEqual(logits.shape    ,				A_	)

				UpperCamelCase          =					np.array([-1.2385, -1.0987, -1.0108]	)

				self.assertTrue(np.allclose(logits[0, :3]    ,				A_    ,				atol=1e-4	)	)

				UpperCamelCase          =					281
				self.assertEqual(logits.argmax(-1	).item()    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='np'	)

				# forward pass
				UpperCamelCase          =					model(**A_	)
				UpperCamelCase          =					outputs.logits

				# verify the logits
				UpperCamelCase          =					(1, 21_841)
				self.assertEqual(logits.shape    ,				A_	)

				UpperCamelCase          =					np.array([1.6881, -0.2787, 0.5901]	)

				self.assertTrue(np.allclose(logits[0, :3]    ,				A_    ,				atol=1e-4	)	)

				UpperCamelCase          =					2_396
				self.assertEqual(logits.argmax(-1	).item()    ,				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_	)
