

"""simple docstring"""




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,
)


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

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
	'''simple docstring'''





	UpperCAmelCase__					:  Optional[Any]					   =					height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	UpperCAmelCase__					:  Tuple					   =					width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=512							,    __UpperCamelCase=512      ):
	'''simple docstring'''





	UpperCAmelCase__					:  Optional[Any]					   =					pil_image.resize((w, h)							,    resample=Image.BICUBIC							,    reducing_gap=1      )
	UpperCAmelCase__					:  Union[str, Any]					   =					np.array(pil_image.convert("""RGB"""      )      )
	UpperCAmelCase__					:  List[str]					   =					arr.astype(np.floataa      ) / 127.5 - 1
	UpperCAmelCase__					:  str					   =					np.transpose(__UpperCamelCase							,    [2, 0, 1]      )
	UpperCAmelCase__					:  List[str]					   =					torch.from_numpy(__UpperCamelCase      ).unsqueeze(0      )
	return image







class      __lowercase			(    A_					):

		def __init__(  self		:			List[str]					,A		:			List[Any]					,A		:			Any					,A		:			Dict					,):






			'''simple docstring'''
			super().__init__()

			self.register_modules(
			    unet=A					,scheduler=A					,movq=A					,)
			UpperCAmelCase__					:  Any					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

		def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[Any]					,A		:			Any					,A		:			Optional[Any]    ):






			'''simple docstring'''
			# get the original timestep using init_timestep
			UpperCAmelCase__					:  int					   =					min(int(num_inference_steps * strength    )					,A    )

			UpperCAmelCase__					:  List[Any]					   =					max(num_inference_steps - init_timestep					,0    )
			UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start

		def    __lowercase       (  self		:			List[Any]					,A		:			Tuple					,A		:			Optional[int]					,A		:			Any					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			List[str]					,A		:			Optional[int]=None    ):






			'''simple docstring'''
			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    )}"    )

			UpperCAmelCase__					:  List[str]					   =					image.to(device=A					,dtype=A    )

			UpperCAmelCase__					:  List[Any]					   =					batch_size * num_images_per_prompt

			if image.shape[1] == 4:
				UpperCAmelCase__					:  Optional[Any]					   =					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    ):
					UpperCAmelCase__					:  Dict					   =					[
					    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(A    )
					]
					UpperCAmelCase__					:  List[str]					   =					torch.cat(A					,dim=0    )
				else:
					UpperCAmelCase__					:  List[Any]					   =					self.movq.encode(A    ).latent_dist.sample(A    )

				UpperCAmelCase__					:  Optional[Any]					   =					self.movq.config.scaling_factor * init_latents

			UpperCAmelCase__					:  List[Any]					   =					torch.cat([init_latents]					,dim=0    )

			UpperCAmelCase__					:  Dict					   =					init_latents.shape
			UpperCAmelCase__					:  Dict					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )

			# get latents
			UpperCAmelCase__					:  str					   =					self.scheduler.add_noise(A					,A					,A    )

			UpperCAmelCase__					:  Any					   =					init_latents

			return latents

		def    __lowercase       (  self		:			int					,A		:			List[str]=0    ):






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

			UpperCAmelCase__					:  List[str]					   =					torch.device(f"cuda:{gpu_id}"    )

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

		def    __lowercase       (  self		:			List[str]					,A		:			Union[str, Any]=0    ):






			'''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."""    )

			UpperCAmelCase__					:  str					   =					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)

			UpperCAmelCase__					:  List[Any]					   =					None
			for cpu_offloaded_model in [self.unet, self.movq]:
				UpperCAmelCase__					:  int					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

			# We'll offload the last model manually.
			UpperCAmelCase__					:  Optional[int]					   =					hook

		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def    __lowercase       (  self		:			Dict    ):






			'''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		:			Optional[Any]					,A		:			Union[str, Any]					,A		:			Tuple					,A		:			Optional[int]					,A		:			List[Any] = 512					,A		:			List[str] = 512					,A		:			int = 100					,A		:			Dict = 4.0					,A		:			List[Any] = 0.3					,A		:			Any = 1					,A		:			int = None					,A		:			List[str] = "pil"					,A		:			Any = True					,):






			'''simple docstring'''
			UpperCAmelCase__					:  List[str]					   =					self._execution_device

			UpperCAmelCase__					:  List[str]					   =					guidance_scale > 1.0

			if isinstance(A					,A    ):
				UpperCAmelCase__					:  int					   =					torch.cat(A					,dim=0    )
			UpperCAmelCase__					:  Any					   =					image_embeds.shape[0]
			if isinstance(A					,A    ):
				UpperCAmelCase__					:  Tuple					   =					torch.cat(A					,dim=0    )

			if do_classifier_free_guidance:
				UpperCAmelCase__					:  Tuple					   =					image_embeds.repeat_interleave(A					,dim=0    )
				UpperCAmelCase__					:  str					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

			UpperCAmelCase__					:  Any					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(dtype=self.unet.dtype					,device=A    )

			if not isinstance(A					,A    ):
				UpperCAmelCase__					:  Optional[int]					   =					[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"    )

			UpperCAmelCase__					:  List[str]					   =					torch.cat([prepare_image(A					,A					,A    ) for i in image]					,dim=0    )
			UpperCAmelCase__					:  Union[str, Any]					   =					image.to(dtype=image_embeds.dtype					,device=A    )

			UpperCAmelCase__					:  Any					   =					self.movq.encode(A    )["""latents"""]
			UpperCAmelCase__					:  List[str]					   =					latents.repeat_interleave(A					,dim=0    )
			self.scheduler.set_timesteps(A					,device=A    )
			UpperCAmelCase__					:  str					   =					self.get_timesteps(A					,A					,A    )
			UpperCAmelCase__					:  Dict					   =					timesteps[:1].repeat(batch_size * num_images_per_prompt    )
			UpperCAmelCase__					:  Optional[Any]					   =					downscale_height_and_width(A					,A					,self.movq_scale_factor    )
			UpperCAmelCase__					:  Union[str, Any]					   =					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
				UpperCAmelCase__					:  Optional[Any]					   =					torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

				UpperCAmelCase__					:  Any					   =					{"""image_embeds""": image_embeds}
				UpperCAmelCase__					:  List[str]					   =					self.unet(
				    sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

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

				# compute the previous noisy sample x_t -> x_t-1
				UpperCAmelCase__					:  int					   =					self.scheduler.step(
				    A					,A					,A					,generator=A					,)[0]

			# post-processing
			UpperCAmelCase__					:  Union[str, Any]					   =					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"]:
				UpperCAmelCase__					:  Tuple					   =					image * 0.5 + 0.5
				UpperCAmelCase__					:  List[Any]					   =					image.clamp(0					,1    )
				UpperCAmelCase__					:  List[str]					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

			if output_type == "pil":
				UpperCAmelCase__					:  Optional[Any]					   =					self.numpy_to_pil(A    )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=A    )








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				['RobertaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





