





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     RePaintPipeline
					A__								=     IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
					A__								=     PipelineTesterMixin.required_optional_params - {
					    "latents",
					    "num_images_per_prompt",
					    "callback",
					    "callback_steps",
					}
					A__								=     IMAGE_INPAINTING_BATCH_PARAMS
					A__								=     False





					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										torch.manual_seed(0						)
										lowerCamelCase__      =							UNetaDModel(
										    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("DownBlock2D", "AttnDownBlock2D")						,	up_block_types=("AttnUpBlock2D", "UpBlock2D")						,	)
										lowerCamelCase__      =							RePaintScheduler()
										lowerCamelCase__      =							{"unet": unet, "scheduler": scheduler}
										return components





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=0						):
										if str(_lowerCAmelCase						).startswith("mps"						):
															lowerCamelCase__      =							torch.manual_seed(_lowerCAmelCase						)
										else:
															lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
										lowerCamelCase__      =							np.random.RandomState(_lowerCAmelCase						).standard_normal((1, 3, 32, 32)						)
										lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						,	dtype=torch.floataa						)
										lowerCamelCase__      =							(image > 0).to(device=_lowerCAmelCase						,	dtype=torch.floataa						)
										lowerCamelCase__      =							{
										    "image": image,
										    "mask_image": mask,
										    "generator": generator,
										    "num_inference_steps": 5,
										    "eta": 0.0,
										    "jump_length": 2,
										    "jump_n_sample": 2,
										    "output_type": "numpy",
										}
										return inputs





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__      =							self.get_dummy_components()
										lowerCamelCase__      =							RePaintPipeline(**_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe.to(_lowerCAmelCase						)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							self.get_dummy_inputs(_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe(**_lowerCAmelCase						).images
										lowerCamelCase__      =							image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]						)

										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3





					@skip_mps
					def        __magic_name__			(			self						):
										return super().test_save_load_local()





					@unittest.skip("non-deterministic pipeline"						)
					def        __magic_name__			(			self						):
										return super().test_inference_batch_single_identical()





					@skip_mps
					def        __magic_name__			(			self						):
										return super().test_dict_tuple_outputs_equivalent()





					@skip_mps
					def        __magic_name__			(			self						):
										return super().test_save_load_optional_components()






					@skip_mps
					def        __magic_name__			(			self						):
										return super().test_attention_slicing_forward_pass()

@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256.png"						)
										lowerCamelCase__      =							load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"						)
										lowerCamelCase__      =							load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256_result.npy"						)

										lowerCamelCase__      =							"google/ddpm-ema-celebahq-256"
										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							RePaintScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							RePaintPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						).to(_lowerCAmelCase						)
										repaint.set_progress_bar_config(disable=_lowerCAmelCase						)
										repaint.enable_attention_slicing()

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							repaint(
										    _lowerCAmelCase						,	_lowerCAmelCase						,	num_inference_steps=250						,	eta=0.0						,	jump_length=10						,	jump_n_sample=10						,	generator=_lowerCAmelCase						,	output_type="np"						,	)
										lowerCamelCase__      =							output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image						).mean() < 1E-2








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										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        __magic_name__			(			self						):
										return ConvNextConfig(
										    num_channels=self.num_channels						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	num_stages=self.num_stages						,	hidden_act=self.hidden_act						,	is_decoder=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	training=_lowerCAmelCase						)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							config_and_inputs
										lowerCamelCase__      =							{"pixel_values": pixel_values}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			self						):
										pass





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					@slow
					def        __magic_name__			(			self						):
										super().test_keras_fit()





					@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
					def        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															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]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					def        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)
															lowerCamelCase__      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	expected_num_stages + 1						)

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)						,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCamelCase__      =							True

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

															def recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						):
																				if isinstance(_lowerCAmelCase						,	(List, Tuple)						):
																									for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase						,	_lowerCAmelCase						):
																														recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)
																				elif tuple_object is None:
																									return
																				else:
																									self.assertTrue(
																									    all(tf.equal(_lowerCAmelCase						,	_lowerCAmelCase						)						)						,	msg=(
																									        "Tuple and dict output are not equal. Difference:"
																									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"
																									    )						,	)

																				recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







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





					"""simple docstring"""


					@cached_property
					def        __magic_name__			(			self						):
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

										# verify the logits
										lowerCamelCase__      =							tf.TensorShape((1, 1000)						)
										self.assertEqual(outputs.logits.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant([-0.0_260, -0.4_739, 0.1_911]						)

										tf.debugging.assert_near(outputs.logits[0, :3]						,	_lowerCAmelCase						,	atol=1E-4						)

