

from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase      				=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase      				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






					__lowerCAmelCase					=							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase					=							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=512		,					UpperCAmelCase__=512      ):

					"""simple docstring"""






					__lowerCAmelCase					=							pil_image.resize((w, h)		,					resample=Image.BICUBIC		,					reducing_gap=1      )
					__lowerCAmelCase					=							np.array(pil_image.convert('RGB'      )      )
					__lowerCAmelCase					=							arr.astype(np.floataa      ) / 127.5 - 1
					__lowerCAmelCase					=							np.transpose(UpperCAmelCase__		,					[2, 0, 1]      )
					__lowerCAmelCase					=							torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
					return image





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						):
												super().__init__()

												self.register_modules(
												    unet=_A					,						scheduler=_A					,						movq=_A					,						)
												__lowerCAmelCase					=							2 ** (len(self.movq.config.block_out_channels							) - 1)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												# get the original timestep using init_timestep
												__lowerCAmelCase					=							min(int(num_inference_steps * strength							)					,						_A							)

												__lowerCAmelCase					=							max(num_inference_steps - init_timestep					,						0							)
												__lowerCAmelCase					=							self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=None							):
												if not isinstance(_A					,						(torch.Tensor, PIL.Image.Image, list)							):
																	raise ValueError(
																	    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A							)}"""							)

												__lowerCAmelCase					=							image.to(device=_A					,						dtype=_A							)

												__lowerCAmelCase					=							batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCAmelCase					=							image

												else:
																	if isinstance(_A					,						_A							) and len(_A							) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(_A							)}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

																	elif isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[
																						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_A							)
																						]
																						__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
																	else:
																						__lowerCAmelCase					=							self.movq.encode(_A							).latent_dist.sample(_A							)

																	__lowerCAmelCase					=							self.movq.config.scaling_factor * init_latents

												__lowerCAmelCase					=							torch.cat([init_latents]					,						dim=0							)

												__lowerCAmelCase					=							init_latents.shape
												__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)

												# get latents
												__lowerCAmelCase					=							self.scheduler.add_noise(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							init_latents

												return latents




							def        A__  ( self					,						_A=0							):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('Please install accelerate via `pip install accelerate`'							)

												__lowerCAmelCase					=							torch.device(F"""cuda:{gpu_id}"""							)

												__lowerCAmelCase					=							[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(_A					,						_A							)




							def        A__  ( self					,						_A=0							):
												if is_accelerate_available() and is_accelerate_version('>='					,						'0.17.0.dev0'							):
																	from accelerate import cpu_offload_with_hook
												else:
																	raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)

												__lowerCAmelCase					=							torch.device(F"""cuda:{gpu_id}"""							)

												if self.device.type != "cpu":
																	self.to('cpu'					,						silence_dtype_warnings=_A							)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

												__lowerCAmelCase					=							None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	__lowerCAmelCase,	__lowerCAmelCase					=							cpu_offload_with_hook(_A					,						_A					,						prev_module_hook=_A							)

												# We'll offload the last model manually.
												__lowerCAmelCase					=							hook




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_A					,						'_hf_hook'							)
																	    and hasattr(module._hf_hook					,						'execution_device'							)
																	    and module._hf_hook.execution_device is not None
																	):
																						return torch.device(module._hf_hook.execution_device							)
												return self.device




							@torch.no_grad()
							@replace_example_docstring(_A							)
							def __call__( self					,						_A					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 0.3					,						_A = 1					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0]
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[image]
												if not all(isinstance(_A					,						(PIL.Image.Image, torch.Tensor)							) for i in image							):
																	raise ValueError(
																	    F"""Input is in incorrect format: {[type(_A							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

												__lowerCAmelCase					=							torch.cat([prepare_image(_A					,						_A					,						_A							) for i in image]					,						dim=0							)
												__lowerCAmelCase					=							image.to(dtype=image_embeds.dtype					,						device=_A							)

												__lowerCAmelCase					=							self.movq.encode(_A							)['latents']
												__lowerCAmelCase					=							latents.repeat_interleave(_A					,						dim=0							)
												self.scheduler.set_timesteps(_A					,						device=_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_timesteps(_A					,						_A					,						_A							)
												__lowerCAmelCase					=							timesteps[:1].repeat(batch_size * num_images_per_prompt							)
												__lowerCAmelCase,	__lowerCAmelCase					=							downscale_height_and_width(_A					,						_A					,						self.movq_scale_factor							)
												__lowerCAmelCase					=							self.prepare_latents(
												    _A					,						_A					,						_A					,						_A					,						image_embeds.dtype					,						_A					,						_A							)
												for i, t in enumerate(self.progress_bar(_A							)							):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase					=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																	__lowerCAmelCase					=							{'image_embeds': image_embeds}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[0]

																	if do_classifier_free_guidance:
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.chunk(2							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							variance_pred.chunk(2							)
																						__lowerCAmelCase					=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						__lowerCAmelCase					=							torch.cat([noise_pred, variance_pred_text]					,						dim=1							)

																	if not (
																	    hasattr(self.scheduler.config					,						'variance_type'							)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]

												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['sample']

												if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""							)

												if output_type in ["np", "pil"]:
																	__lowerCAmelCase					=							image * 0.5 + 0.5
																	__lowerCAmelCase					=							image.clamp(0					,						1							)
																	__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)









import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												__lowerCAmelCase,	__lowerCAmelCase					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												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        A__  ( self							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						len(config.out_indices							)							)
																	self.assertEqual(len(model.channels							)					,						len(config.out_indices							)							)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						1							)
																	self.assertEqual(len(model.channels							)					,						1							)

																	# Check backbone can be initialized with fresh weights
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							False
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)






