






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=30       ,  _UpperCamelCase=2       ,  _UpperCamelCase=3       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=32       ,  _UpperCamelCase=2       ,  _UpperCamelCase=4       ,  _UpperCamelCase=37       ,  _UpperCamelCase="gelu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=10       ,  _UpperCamelCase=0.02       ,  _UpperCamelCase=3       ,  _UpperCamelCase=None       ,  _UpperCamelCase=2       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  image_size
												lowerCAmelCase__        =  patch_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  hidden_size
												lowerCAmelCase__        =  num_hidden_layers
												lowerCAmelCase__        =  num_attention_heads
												lowerCAmelCase__        =  intermediate_size
												lowerCAmelCase__        =  hidden_act
												lowerCAmelCase__        =  hidden_dropout_prob
												lowerCAmelCase__        =  attention_probs_dropout_prob
												lowerCAmelCase__        =  type_sequence_label_size
												lowerCAmelCase__        =  initializer_range
												lowerCAmelCase__        =  scope
												lowerCAmelCase__        =  encoder_stride

												# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
												lowerCAmelCase__        =  (image_size // patch_size) ** 2
												lowerCAmelCase__        =  num_patches + 2






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

												lowerCAmelCase__        =  self.get_config()

												return config, pixel_values, labels






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return DeiTConfig(
												    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=_SCREAMING_SNAKE_CASE       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFDeiTModel(config=_SCREAMING_SNAKE_CASE				)
												lowerCAmelCase__        =  model(_SCREAMING_SNAKE_CASE				)
												self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE				)
												lowerCAmelCase__        =  model(_SCREAMING_SNAKE_CASE				)
												self.parent.assertEqual(
												    result.reconstruction.shape       ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

												# test greyscale images
												lowerCAmelCase__        =  1
												lowerCAmelCase__        =  TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)

												lowerCAmelCase__        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
												lowerCAmelCase__        =  model(_SCREAMING_SNAKE_CASE				)
												self.parent.assertEqual(result.reconstruction.shape       ,  (self.batch_size, 1, self.image_size, self.image_size)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.type_sequence_label_size
												lowerCAmelCase__        =  TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)
												lowerCAmelCase__        =  model(_SCREAMING_SNAKE_CASE       ,  labels=_SCREAMING_SNAKE_CASE				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)				)

												# test greyscale images
												lowerCAmelCase__        =  1
												lowerCAmelCase__        =  TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)

												lowerCAmelCase__        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
												lowerCAmelCase__        =  model(_SCREAMING_SNAKE_CASE       ,  labels=_SCREAMING_SNAKE_CASE				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)				)







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												lowerCAmelCase__        =  config_and_inputs
												lowerCAmelCase__        =  {"pixel_values": pixel_values}
												return config, inputs_dict




@require_tf
class       __SCREAMING_SNAKE_CASE   (				__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    List[str]						=   (
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   (
						    {
						        '''feature-extraction''': TFDeiTModel,
						        '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						_SCREAMING_SNAKE_CASE		:    List[Any]						=   False
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   False
						_SCREAMING_SNAKE_CASE		:    str						=   False
						_SCREAMING_SNAKE_CASE		:    Tuple						=   False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFDeiTModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_SCREAMING_SNAKE_CASE       ,  has_text_modality=_SCREAMING_SNAKE_CASE       ,  hidden_size=37				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						@unittest.skip(reason='DeiT does not use inputs_embeds'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowerCAmelCase__        =  model_class(_SCREAMING_SNAKE_CASE				)
																		self.assertIsInstance(model.get_input_embeddings()       ,  (tf.keras.layers.Layer)				)
																		lowerCAmelCase__        =  model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,  tf.keras.layers.Dense				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCAmelCase__        =  ["pixel_values"]
																		self.assertListEqual(arg_names[:1]       ,  _SCREAMING_SNAKE_CASE				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												lowerCAmelCase__        =  super()._prepare_for_class(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE       ,  return_labels=_SCREAMING_SNAKE_CASE				)

												if return_labels:
																		if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call				).parameters:
																								del inputs_dict["labels"]

												return inputs_dict







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""

												for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__        =  TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
																		self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)







def 		_UpperCamelCase				(      )					->				Union[str, Any]:

						"""simple docstring"""



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


@require_tf
@require_vision
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return (
												    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'				)
												    if is_vision_available()
												    else None
												)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'				)

												lowerCAmelCase__        =  self.default_image_processor
												lowerCAmelCase__        =  prepare_img()
												lowerCAmelCase__        =  image_processor(images=_SCREAMING_SNAKE_CASE       ,  return_tensors='tf'				)

												# forward pass
												lowerCAmelCase__        =  model(**_SCREAMING_SNAKE_CASE				)

												# verify the logits
												lowerCAmelCase__        =  tf.TensorShape((1, 10_00)				)
												self.assertEqual(outputs.logits.shape       ,  _SCREAMING_SNAKE_CASE				)

												lowerCAmelCase__        =  tf.constant([-1.02_66, 0.19_12, -1.28_61]				)

												self.assertTrue(np.allclose(outputs.logits[0, :3]       ,  _SCREAMING_SNAKE_CASE       ,  atol=1E-4				)				)













import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   KandinskyVaaInpaintPipeline
						_SCREAMING_SNAKE_CASE		:    int						=   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 1_00






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    'in_channels': 9,
												    # 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,
												}

												lowerCAmelCase__        =  UNetaDConditionModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return {
												    "block_out_channels": [32, 64],
												    "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": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  VQModel(**self.dummy_movq_kwargs				)
												return model






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_unet
												lowerCAmelCase__        =  self.dummy_movq

												lowerCAmelCase__        =  DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  clip_sample=_UpperCamelCase       ,  set_alpha_to_one=_UpperCamelCase       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=_UpperCamelCase       ,  )

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

												return components






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=0				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1				)				).to(
												    _UpperCamelCase				)
												# create init_image
												lowerCAmelCase__        =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)
												# create mask
												lowerCAmelCase__        =  np.ones((64, 64)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												if str(_UpperCamelCase				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
												lowerCAmelCase__        =  {
												    'image': init_image,
												    'mask_image': mask,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 2,
												    'guidance_scale': 4.0,
												    'output_type': 'np',
												}
												return inputs






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

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

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

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

												print(F"image.shape {image.shape}"				)

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

												lowerCAmelCase__        =  np.array(
												    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]				)

												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()}"







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												lowerCAmelCase__        =  np.ones((7_68, 7_68)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												lowerCAmelCase__        =  'a hat'

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

												lowerCAmelCase__        =  KandinskyVaaInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,  torch_dtype=torch.floataa				)
												lowerCAmelCase__        =  pipeline.to(_UpperCamelCase				)
												pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

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

												lowerCAmelCase__        =  pipeline(
												    image=_UpperCamelCase       ,  mask_image=_UpperCamelCase       ,  image_embeds=_UpperCamelCase       ,  negative_image_embeds=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(_UpperCamelCase       ,  _UpperCamelCase				)





