



"""simple docstring"""







from typing import List, Optional, Union

import torch

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_   						=   "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=8	)							->      str:
		lowercase__   :    Tuple				=			height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase__   :    str				=			width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor





class        __A       (	a_    ):







		'''simple docstring'''





		def __init__(	self						:  List[Any]			,_snake_case						:  str			,_snake_case						:  List[str]			,_snake_case						:  int			,)					->		List[Any]:






				"""simple docstring"""



				super().__init__()

				self.register_modules(
				    unet=_A			,scheduler=_A			,movq=_A			,)
				lowercase__   :    Union[str, Any]				=			2 ** (len(self.movq.config.block_out_channels						) - 1)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  str			,_snake_case						:  List[str]			,_snake_case						:  Dict			,_snake_case						:  Optional[int]			,_snake_case						:  Any			,_snake_case						:  Union[str, Any]						)					->		List[Any]:






				"""simple docstring"""



				if latents is None:
						lowercase__   :    Dict				=			randn_tensor(_A			,generator=_A			,device=_A			,dtype=_A						)
				else:
						if latents.shape != shape:
								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""						)
						lowercase__   :    Union[str, Any]				=			latents.to(_A						)

				lowercase__   :    Tuple				=			latents * scheduler.init_noise_sigma
				return latents







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  List[Any]=0						)					->		Optional[Any]:






				"""simple docstring"""



				if is_accelerate_available():
						from accelerate import cpu_offload
				else:
						raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

				lowercase__   :    int				=			torch.device(f"""cuda:{gpu_id}"""						)

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







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  List[Any]=0						)					->		List[str]:






				"""simple docstring"""



				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.'''						)

				lowercase__   :    Optional[Any]				=			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)

				lowercase__   :    Optional[Any]				=			None
				for cpu_offloaded_model in [self.unet, self.movq]:
						lowercase__		,			lowercase__   :    str				=			cpu_offload_with_hook(_A			,_A			,prev_module_hook=_A						)

				# We'll offload the last model manually.
				lowercase__   :    int				=			hook







		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Any:






				"""simple docstring"""



				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						:  Dict			,_snake_case						:  Any			,_snake_case						:  List[str]			,_snake_case						:  Any			,_snake_case						:  Dict = 512			,_snake_case						:  Tuple = 512			,_snake_case						:  Union[str, Any] = 100			,_snake_case						:  Optional[int] = 4.0			,_snake_case						:  int = 1			,_snake_case						:  Any = None			,_snake_case						:  Any = None			,_snake_case						:  List[str] = "pil"			,_snake_case						:  List[str] = True			,)					->		Any:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self._execution_device

				lowercase__   :    Optional[int]				=			guidance_scale > 1.0

				if isinstance(_A			,_A						):
						lowercase__   :    Union[str, Any]				=			torch.cat(_A			,dim=0						)
				if isinstance(_A			,_A						):
						lowercase__   :    Tuple				=			torch.cat(_A			,dim=0						)
				if isinstance(_A			,_A						):
						lowercase__   :    Optional[Any]				=			torch.cat(_A			,dim=0						)

				lowercase__   :    Tuple				=			image_embeds.shape[0] * num_images_per_prompt

				if do_classifier_free_guidance:
						lowercase__   :    Union[str, Any]				=			image_embeds.repeat_interleave(_A			,dim=0						)
						lowercase__   :    Tuple				=			negative_image_embeds.repeat_interleave(_A			,dim=0						)
						lowercase__   :    str				=			hint.repeat_interleave(_A			,dim=0						)

				lowercase__   :    int				=			torch.cat([negative_image_embeds, image_embeds]			,dim=0						).to(dtype=self.unet.dtype			,device=_A						)
				lowercase__   :    Dict				=			torch.cat([hint, hint]			,dim=0						).to(dtype=self.unet.dtype			,device=_A						)
				self.scheduler.set_timesteps(_A			,device=_A						)
				lowercase__   :    str				=			self.scheduler.timesteps

				lowercase__   :    str				=			self.movq.config.latent_channels

				lowercase__		,			lowercase__   :    Tuple				=			downscale_height_and_width(_A			,_A			,self.movq_scale_factor						)

				# create initial latent
				lowercase__   :    str				=			self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)			,image_embeds.dtype			,_A			,_A			,_A			,self.scheduler			,)

				for i, t in enumerate(self.progress_bar(_A						)						):
						# expand the latents if we are doing classifier free guidance
						lowercase__   :    List[str]				=			torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

						lowercase__   :    Tuple				=			{'''image_embeds''': image_embeds, '''hint''': hint}
						lowercase__   :    Union[str, Any]				=			self.unet(
						    sample=_A			,timestep=_A			,encoder_hidden_states=_A			,added_cond_kwargs=_A			,return_dict=_A			,)[0]

						if do_classifier_free_guidance:
								lowercase__		,			lowercase__   :    int				=			noise_pred.split(latents.shape[1]			,dim=1						)
								lowercase__		,			lowercase__   :    Optional[int]				=			noise_pred.chunk(2						)
								lowercase__		,			lowercase__   :    str				=			variance_pred.chunk(2						)
								lowercase__   :    Any				=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
								lowercase__   :    List[Any]				=			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"]
						):
								lowercase__		,			lowercase__   :    str				=			noise_pred.split(latents.shape[1]			,dim=1						)

						# compute the previous noisy sample x_t -> x_t-1
						lowercase__   :    List[Any]				=			self.scheduler.step(
						    _A			,_A			,_A			,generator=_A			,)[0]
				# post-processing
				lowercase__   :    str				=			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"]:
						lowercase__   :    List[str]				=			image * 0.5 + 0.5
						lowercase__   :    Optional[int]				=			image.clamp(0			,1						)
						lowercase__   :    int				=			image.cpu().permute(0			,2			,3			,1						).float().numpy()

				if output_type == "pil":
						lowercase__   :    List[str]				=			self.numpy_to_pil(_A						)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=_A						)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
				import torch

				from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	A_       (unittest.TestCase			):
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)



