



"""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,
)


lowercase__					:						List[str]												=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__					:						Optional[Any]												=			'''
    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    (							_a	,    _a	,    _a=8					):
    snake_case_				:  List[str]       =			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    snake_case_				:  Optional[Any]       =			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor




def __lowercase    (							_a	,    _a=512	,    _a=512					):
    snake_case_				:  List[Any]       =			pil_image.resize((w, h)	,    resample=Image.BICUBIC	,    reducing_gap=1					)
    snake_case_				:  Dict       =			np.array(pil_image.convert('''RGB'''					)					)
    snake_case_				:  List[str]       =			arr.astype(np.floataa					) / 127.5 - 1
    snake_case_				:  Tuple       =			np.transpose(_a	,    [2, 0, 1]					)
    snake_case_				:  Any       =			torch.from_numpy(_a					).unsqueeze(0					)
    return image






class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: Dict						,  lowercase_	: UNetaDConditionModel						,  lowercase_	: DDPMScheduler						,  lowercase_	: VQModel						,  ):
     super().__init__()

     self.register_modules(
         unet=lowercase_						,  scheduler=lowercase_						,  movq=lowercase_						,  )
     snake_case_				:  str       =			2 ** (len(self.movq.config.block_out_channels       ) - 1)

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: List[str]						,  lowercase_	: Tuple						,  lowercase_	: List[Any]       ):
     # get the original timestep using init_timestep
     snake_case_				:  List[str]       =			min(int(num_inference_steps * strength       )						,  lowercase_       )

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

     return timesteps, num_inference_steps - t_start

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: str						,  lowercase_	: str						,  lowercase_	: List[str]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Dict						,  lowercase_	: Optional[int]						,  lowercase_	: Union[str, Any]=None       ):
     if not isinstance(lowercase_						,  (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(lowercase_       )}"       )

     snake_case_				:  Dict       =			image.to(device=lowercase_						,  dtype=lowercase_       )

     snake_case_				:  Dict       =			batch_size * num_images_per_prompt

     if image.shape[1] == 4:
         snake_case_				:  Tuple       =			image

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

         elif isinstance(lowercase_						,  lowercase_       ):
             snake_case_				:  Any       =			[
                 self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(lowercase_       )
             ]
             snake_case_				:  Union[str, Any]       =			torch.cat(lowercase_						,  dim=0       )
         else:
             snake_case_				:  List[str]       =			self.movq.encode(lowercase_       ).latent_dist.sample(lowercase_       )

         snake_case_				:  Tuple       =			self.movq.config.scaling_factor * init_latents

     snake_case_				:  List[str]       =			torch.cat([init_latents]						,  dim=0       )

     snake_case_				:  List[str]       =			init_latents.shape
     snake_case_				:  List[str]       =			randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_						,  dtype=lowercase_       )

     # get latents
     snake_case_				:  int       =			self.scheduler.add_noise(lowercase_						,  lowercase_						,  lowercase_       )

     snake_case_				:  Dict       =			init_latents

     return latents

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Union[str, Any]=0       ):
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

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

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

 def 		_snake_case      (  self	: Any						,  lowercase_	: Dict=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.'''       )

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

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

     snake_case_				:  Optional[Any]       =			None
     for cpu_offloaded_model in [self.unet, self.movq]:
         snake_case_,			snake_case_				:  Dict       =			cpu_offload_with_hook(lowercase_						,  lowercase_						,  prev_module_hook=lowercase_       )

     # We'll offload the last model manually.
     snake_case_				:  Tuple       =			hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		_snake_case      (  self	: Tuple       ):
     if not hasattr(self.unet						,  '''_hf_hook'''       ):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(lowercase_						,  '''_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(lowercase_       )
 def __call__(  self	: List[Any]						,  lowercase_	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowercase_	: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,  lowercase_	: Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowercase_	: int = 512						,  lowercase_	: int = 512						,  lowercase_	: int = 100						,  lowercase_	: float = 4.0						,  lowercase_	: float = 0.3						,  lowercase_	: int = 1						,  lowercase_	: Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  lowercase_	: Optional[str] = "pil"						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Optional[Any]       =			self._execution_device

     snake_case_				:  Union[str, Any]       =			guidance_scale > 1.0

     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  Optional[Any]       =			torch.cat(lowercase_						,  dim=0       )
     snake_case_				:  List[Any]       =			image_embeds.shape[0]
     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  List[str]       =			torch.cat(lowercase_						,  dim=0       )

     if do_classifier_free_guidance:
         snake_case_				:  Any       =			image_embeds.repeat_interleave(lowercase_						,  dim=0       )
         snake_case_				:  List[str]       =			negative_image_embeds.repeat_interleave(lowercase_						,  dim=0       )

     snake_case_				:  Dict       =			torch.cat([negative_image_embeds, image_embeds]						,  dim=0       ).to(dtype=self.unet.dtype						,  device=lowercase_       )

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

     snake_case_				:  int       =			torch.cat([prepare_image(lowercase_						,  lowercase_						,  lowercase_       ) for i in image]						,  dim=0       )
     snake_case_				:  List[str]       =			image.to(dtype=image_embeds.dtype						,  device=lowercase_       )

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

         snake_case_				:  Tuple       =			{'''image_embeds''': image_embeds}
         snake_case_				:  Any       =			self.unet(
             sample=lowercase_						,  timestep=lowercase_						,  encoder_hidden_states=lowercase_						,  added_cond_kwargs=lowercase_						,  return_dict=lowercase_						,  )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         snake_case_				:  Optional[Any]       =			self.scheduler.step(
             lowercase_						,  lowercase_						,  lowercase_						,  generator=lowercase_						,  )[0]

     # post-processing
     snake_case_				:  List[str]       =			self.movq.decode(lowercase_						,  force_not_quantize=lowercase_       )['''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"]:
         snake_case_				:  List[str]       =			image * 0.5 + 0.5
         snake_case_				:  str       =			image.clamp(0						,  1       )
         snake_case_				:  int       =			image.cpu().permute(0						,  2						,  3						,  1       ).float().numpy()

     if output_type == "pil":
         snake_case_				:  Optional[int]       =			self.numpy_to_pil(lowercase_       )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_       )










"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





