



"""simple docstring"""
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
					from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image




class 			snake_case_						:






							def __init__(   self			,     a_			,     a_=3			,     a_=3_2			,     a_=3			,     a_=1_0			,     a_=[8, 1_6, 3_2, 6_4]			,     a_=[1, 1, 2, 1]			,     a_=True			,     a_=True			,     a_="relu"			,     a_=3			,     a_=None			,     a_=["stage2", "stage3", "stage4"]			,     a_=[2, 3, 4]			,     a_=1			,     ):
									a_     :      List[Any] 						=     parent
									a_     :      Optional[int] 						=     batch_size
									a_     :      Union[str, Any] 						=     image_size
									a_     :      Dict 						=     num_channels
									a_     :      Any 						=     embeddings_size
									a_     :      Any 						=     hidden_sizes
									a_     :      int 						=     depths
									a_     :      List[str] 						=     is_training
									a_     :      Any 						=     use_labels
									a_     :      Union[str, Any] 						=     hidden_act
									a_     :      Union[str, Any] 						=     num_labels
									a_     :      Union[str, Any] 						=     scope
									a_     :      Union[str, Any] 						=     len(a_      )
									a_     :      Union[str, Any] 						=     out_features
									a_     :      Any 						=     out_indices
									a_     :      Any 						=     num_groups






							def snake_case_  (   self      ):
									a_     :      int 						=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									a_     :      int 						=     None
									if self.use_labels:
											a_     :      int 						=     ids_tensor([self.batch_size]			,     self.num_labels      )

									a_     :      List[str] 						=     self.get_config()

									return config, pixel_values, labels






							def snake_case_  (   self      ):
									return BitConfig(
									    num_channels=self.num_channels			,     embeddings_size=self.embeddings_size			,     hidden_sizes=self.hidden_sizes			,     depths=self.depths			,     hidden_act=self.hidden_act			,     num_labels=self.num_labels			,     out_features=self.out_features			,     out_indices=self.out_indices			,     num_groups=self.num_groups			,     )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      List[Any] 						=     BitModel(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      List[str] 						=     model(a_      )
									self.parent.assertEqual(
									    result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)			,     )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Tuple 						=     self.num_labels
									a_     :      Any 						=     BitForImageClassification(a_      )
									model.to(a_      )
									model.eval()
									a_     :      Optional[Any] 						=     model(a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Optional[Any] 						=     BitBackbone(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      Any 						=     model(a_      )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps      )			,     len(config.out_features      )      )
									self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[1], 4, 4]      )

									# verify channels
									self.parent.assertEqual(len(model.channels      )			,     len(config.out_features      )      )
									self.parent.assertListEqual(model.channels			,     config.hidden_sizes[1:]      )

									# verify backbone works with out_features=None
									a_     :      Union[str, Any] 						=     None
									a_     :      str 						=     BitBackbone(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      int 						=     model(a_      )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps      )			,     1      )
									self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

									# verify channels
									self.parent.assertEqual(len(model.channels      )			,     1      )
									self.parent.assertListEqual(model.channels			,     [config.hidden_sizes[-1]]      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.prepare_config_and_inputs()
									a_     :      Optional[Any] 						=     config_and_inputs
									a_     :      Union[str, Any] 						=     {"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class 			snake_case_						(						a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
							__lowerCAmelCase    					=					(
							    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     BitModelTester(self      )
									a_     :      Tuple 						=     ConfigTester(self			,     config_class=a_			,     has_text_modality=a_      )






							def snake_case_  (   self      ):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()






							def snake_case_  (   self      ):
									return






							@unittest.skip(reason="Bit does not output attentions"      )
							def snake_case_  (   self      ):
									pass






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






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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      List[str] 						=     model_class(a_      )
											a_     :      Optional[Any] 						=     inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_     :      Optional[int] 						=     [*signature.parameters.keys()]

											a_     :      int 						=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     a_      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*a_      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      List[str] 						=     model_class(config=a_      )
											for name, module in model.named_modules():
													if isinstance(a_			,     (nn.BatchNormad, nn.GroupNorm)      ):
															self.assertTrue(
															    torch.all(module.weight == 1      )			,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,     )
															self.assertTrue(
															    torch.all(module.bias == 0      )			,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,     )






							def snake_case_  (   self      ):
									def check_hidden_states_output(a_			,     a_			,     a_      ):
											a_     :      Optional[int] 						=     model_class(a_      )
											model.to(a_      )
											model.eval()

											with torch.no_grad():
													a_     :      Optional[Any] 						=     model(**self._prepare_for_class(a_			,     a_      )      )

											a_     :      List[Any] 						=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											a_     :      str 						=     self.model_tester.num_stages
											self.assertEqual(len(a_      )			,     expected_num_stages + 1      )

											# Bit'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]			,     )

									a_     :      List[Any] 						=     self.model_tester.prepare_config_and_inputs_for_common()
									a_     :      Union[str, Any] 						=     ["preactivation", "bottleneck"]
									for model_class in self.all_model_classes:
											for layer_type in layers_type:
													a_     :      Tuple 						=     layer_type
													a_     :      List[Any] 						=     True
													check_hidden_states_output(a_			,     a_			,     a_      )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													a_     :      int 						=     True

													check_hidden_states_output(a_			,     a_			,     a_      )






							@unittest.skip(reason="Bit does not use feedforward chunking"      )
							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a_      )






							@slow
							def snake_case_  (   self      ):
									for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      str 						=     BitModel.from_pretrained(a_      )
											self.assertIsNotNone(a_      )



def        lowerCAmelCase_						(							)			->      Optional[Any]:
		a_     :      Optional[Any] 						=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




@require_torch
@require_vision
class 			snake_case_						(						unittest.TestCase				):






							@cached_property
							def snake_case_  (   self      ):
									return (
									    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
									)






							@slow
							def snake_case_  (   self      ):
									a_     :      Tuple 						=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(a_      )

									a_     :      Optional[Any] 						=     self.default_image_processor
									a_     :      Optional[Any] 						=     prepare_img()
									a_     :      Optional[int] 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Tuple 						=     model(**a_      )

									# verify the logits
									a_     :      str 						=     torch.Size((1, 1_0_0_0)      )
									self.assertEqual(outputs.logits.shape			,     a_      )

									a_     :      Optional[int] 						=     torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]      ).to(a_      )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     a_			,     atol=1e-4      )      )




@require_torch
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(BitBackbone,) if is_torch_available() else ()
							__lowerCAmelCase    					=					BitConfig

							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     BitModelTester(self      )




"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					KandinskyImgaImgPipeline
							__lowerCAmelCase    					=					["prompt", "image_embeds", "negative_image_embeds", "image"]
							__lowerCAmelCase    					=					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							__lowerCAmelCase    					=					[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							__lowerCAmelCase    					=					False






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim






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






							@property
							def snake_case_  (   self      ):
									return 1_0_0






							@property
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
									return tokenizer






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     MCLIPConfig(
									    numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=3_7			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1_0_0_5			,     )

									a_     :      str 						=     MultilingualCLIP(a_      )
									a_     :      Any 						=     text_encoder.eval()

									return text_encoder






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )

									a_     :      Union[str, Any] 						=     {
									    "in_channels": 4,
									    # Out channels is double in channels because predicts mean and variance
									    "out_channels": 8,
									    "addition_embed_type": "text_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": "text_image_proj",
									    "cross_attention_dim": self.cross_attention_dim,
									    "attention_head_dim": 4,
									    "resnet_time_scale_shift": "scale_shift",
									    "class_embed_type": None,
									}

									a_     :      Dict 						=     UNetaDConditionModel(**a_      )
									return model






							@property
							def snake_case_  (   self      ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     VQModel(**self.dummy_movq_kwargs      )
									return model






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.dummy_text_encoder
									a_     :      Dict 						=     self.dummy_tokenizer
									a_     :      Optional[int] 						=     self.dummy_unet
									a_     :      Dict 						=     self.dummy_movq

									a_     :      List[str] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.00_085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									a_     :      List[Any] 						=     DDIMScheduler(**a_      )

									a_     :      Union[str, Any] 						=     {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									a_     :      Optional[int] 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Tuple 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(a_      )
									# create init_image
									a_     :      Optional[int] 						=     floats_tensor((1, 3, 6_4, 6_4)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Any 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      int 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )

									if str(a_      ).startswith("mps"      ):
											a_     :      Any 						=     torch.manual_seed(a_      )
									else:
											a_     :      Any 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "cpu"

									a_     :      List[Any] 						=     self.get_dummy_components()

									a_     :      Union[str, Any] 						=     self.pipeline_class(**a_      )
									a_     :      Tuple 						=     pipe.to(a_      )

									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Union[str, Any] 						=     pipe(**self.get_dummy_inputs(a_      )      )
									a_     :      Any 						=     output.images

									a_     :      str 						=     pipe(
									    **self.get_dummy_inputs(a_      )			,     return_dict=a_			,     )[0]

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]
									a_     :      Optional[Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a_     :      Optional[int] 						=     np.array(
									    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]      )
									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 			snake_case_						(						unittest.TestCase				):






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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy"      )

									a_     :      Union[str, Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
									a_     :      Optional[Any] 						=     "A red cartoon frog, 4k"

									a_     :      int 						=     KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"			,     torch_dtype=torch.floataa      )
									pipe_prior.to(a_      )

									a_     :      int 						=     KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"			,     torch_dtype=torch.floataa      )
									a_     :      List[Any] 						=     pipeline.to(a_      )

									pipeline.set_progress_bar_config(disable=a_      )

									a_     :      int 						=     torch.Generator(device="cpu"      ).manual_seed(0      )
									a_						,				a_     :      Optional[int] 						=     pipe_prior(
									    a_			,     generator=a_			,     num_inference_steps=5			,     negative_prompt=""			,     ).to_tuple()

									a_     :      List[Any] 						=     pipeline(
									    a_			,     image=a_			,     image_embeds=a_			,     negative_image_embeds=a_			,     generator=a_			,     num_inference_steps=1_0_0			,     height=7_6_8			,     width=7_6_8			,     strength=0.2			,     output_type="np"			,     )

									a_     :      int 						=     output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_			,     a_      )