


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

__UpperCamelCase					:	Tuple          =				'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase=8							):
   UpperCAmelCase_				:   Dict							= height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   UpperCAmelCase_				:   str							= width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor
def   snake_case_    (			__lowercase      ,							__lowercase=5_1_2      ,							__lowercase=5_1_2							):
   UpperCAmelCase_				:   Union[str, Any]							= pil_image.resize((w, h)      ,							resample=Image.BICUBIC      ,							reducing_gap=1							)
   UpperCAmelCase_				:   str							= np.array(pil_image.convert('''RGB'''							)							)
   UpperCAmelCase_				:   Optional[int]							= arr.astype(np.floataa							) / 1_2_7.5 - 1
   UpperCAmelCase_				:   int							= np.transpose(__lowercase      ,							[2, 0, 1]							)
   UpperCAmelCase_				:   Dict							= torch.from_numpy(__lowercase							).unsqueeze(0							)
   return image





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    def __init__(						self					: Optional[Any]							, __snake_case					: UNetaDConditionModel							, __snake_case					: DDPMScheduler							, __snake_case					: VQModel							, ):





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

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





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: Tuple							, __snake_case					: List[str]							, __snake_case					: Optional[Any]						):





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

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

       return timesteps, num_inference_steps - t_start





    def        _lowerCamelCase			(						self					: str							, __snake_case					: List[Any]							, __snake_case					: int							, __snake_case					: Tuple							, __snake_case					: Tuple							, __snake_case					: Dict							, __snake_case					: Any							, __snake_case					: Dict=None						):





       '''simple docstring'''
       if not isinstance(__snake_case							, (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(__snake_case						)}'''						)

       UpperCAmelCase_				:   int							= image.to(device=__snake_case							, dtype=__snake_case						)

       UpperCAmelCase_				:   Tuple							= batch_size * num_images_per_prompt

       if image.shape[1] == 4:
          UpperCAmelCase_				:   Optional[Any]							= image

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

          elif isinstance(__snake_case							, __snake_case						):
             UpperCAmelCase_				:   Optional[Any]							= [
                 self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__snake_case						)
             ]
             UpperCAmelCase_				:   Dict							= torch.cat(__snake_case							, dim=0						)
          else:
             UpperCAmelCase_				:   Tuple							= self.movq.encode(__snake_case						).latent_dist.sample(__snake_case						)

          UpperCAmelCase_				:   Union[str, Any]							= self.movq.config.scaling_factor * init_latents

       UpperCAmelCase_				:   Optional[int]							= torch.cat([init_latents]							, dim=0						)

       UpperCAmelCase_				:   Dict							= init_latents.shape
       UpperCAmelCase_				:   List[str]							= randn_tensor(__snake_case							, generator=__snake_case							, device=__snake_case							, dtype=__snake_case						)

       # get latents
       UpperCAmelCase_				:   Dict							= self.scheduler.add_noise(__snake_case							, __snake_case							, __snake_case						)

       UpperCAmelCase_				:   List[str]							= init_latents

       return latents





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: 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_				:   Optional[int]							= [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
             cpu_offload(__snake_case							, __snake_case						)





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: List[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_				:   Tuple							= torch.device(f'''cuda:{gpu_id}'''						)

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

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

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





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





       '''simple docstring'''
       if not hasattr(self.unet							, '''_hf_hook'''						):
          return self.device
       for module in self.unet.modules():
          if (
              hasattr(__snake_case							, '''_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(__snake_case						)
    def __call__(						self					: Union[str, Any]							, __snake_case					: Union[torch.FloatTensor, List[torch.FloatTensor]]							, __snake_case					: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							, __snake_case					: Union[torch.FloatTensor, List[torch.FloatTensor]]							, __snake_case					: torch.FloatTensor							, __snake_case					: int = 512							, __snake_case					: int = 512							, __snake_case					: int = 100							, __snake_case					: float = 4.0							, __snake_case					: float = 0.3							, __snake_case					: int = 1							, __snake_case					: Optional[Union[torch.Generator, List[torch.Generator]]] = None							, __snake_case					: Optional[str] = "pil"							, __snake_case					: bool = True							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= self._execution_device

       UpperCAmelCase_				:   Optional[Any]							= guidance_scale > 1.0

       if isinstance(__snake_case							, __snake_case						):
          UpperCAmelCase_				:   List[Any]							= torch.cat(__snake_case							, dim=0						)
       if isinstance(__snake_case							, __snake_case						):
          UpperCAmelCase_				:   str							= torch.cat(__snake_case							, dim=0						)
       if isinstance(__snake_case							, __snake_case						):
          UpperCAmelCase_				:   Tuple							= torch.cat(__snake_case							, dim=0						)

       UpperCAmelCase_				:   int							= image_embeds.shape[0]

       if do_classifier_free_guidance:
          UpperCAmelCase_				:   Dict							= image_embeds.repeat_interleave(__snake_case							, dim=0						)
          UpperCAmelCase_				:   Tuple							= negative_image_embeds.repeat_interleave(__snake_case							, dim=0						)
          UpperCAmelCase_				:   Dict							= hint.repeat_interleave(__snake_case							, dim=0						)

       UpperCAmelCase_				:   List[str]							= torch.cat([negative_image_embeds, image_embeds]							, dim=0						).to(dtype=self.unet.dtype							, device=__snake_case						)
       UpperCAmelCase_				:   Tuple							= torch.cat([hint, hint]							, dim=0						).to(dtype=self.unet.dtype							, device=__snake_case						)

       if not isinstance(__snake_case							, __snake_case						):
          UpperCAmelCase_				:   List[Any]							= [image]
       if not all(isinstance(__snake_case							, (PIL.Image.Image, torch.Tensor)						) for i in image						):
          raise ValueError(
              f'''Input is in incorrect format: {[type(__snake_case						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       UpperCAmelCase_				:   Optional[int]							= torch.cat([prepare_image(__snake_case							, __snake_case							, __snake_case						) for i in image]							, dim=0						)
       UpperCAmelCase_				:   Optional[Any]							= image.to(dtype=image_embeds.dtype							, device=__snake_case						)

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

          UpperCAmelCase_				:   List[str]							= {'''image_embeds''': image_embeds, '''hint''': hint}
          UpperCAmelCase_				:   int							= self.unet(
              sample=__snake_case							, timestep=__snake_case							, encoder_hidden_states=__snake_case							, added_cond_kwargs=__snake_case							, return_dict=__snake_case							, )[0]

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

          # compute the previous noisy sample x_t -> x_t-1

          UpperCAmelCase_				:   Optional[int]							= self.scheduler.step(
              __snake_case							, __snake_case							, __snake_case							, generator=__snake_case							, )[0]

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

       if output_type == "pil":
          UpperCAmelCase_				:   Dict							= self.numpy_to_pil(__snake_case						)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=__snake_case						)



import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )





class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def __init__(						self					: Any							, __snake_case					: Tuple							, __snake_case					: Union[str, Any]=13							, __snake_case					: Tuple=7							, __snake_case					: str=True							, __snake_case					: List[Any]=True							, __snake_case					: Optional[int]=True							, __snake_case					: List[str]=True							, __snake_case					: Tuple=99							, __snake_case					: int=24							, __snake_case					: int=32							, __snake_case					: Tuple=5							, __snake_case					: int=4							, __snake_case					: Any=37							, __snake_case					: int="gelu"							, __snake_case					: Union[str, Any]=0.1							, __snake_case					: Optional[Any]=0.1							, __snake_case					: str=512							, __snake_case					: List[Any]=16							, __snake_case					: Tuple=2							, __snake_case					: List[str]=0.02							, __snake_case					: Any=4							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= parent
       UpperCAmelCase_				:   Dict							= batch_size
       UpperCAmelCase_				:   Union[str, Any]							= seq_length
       UpperCAmelCase_				:   Any							= is_training
       UpperCAmelCase_				:   Tuple							= use_attention_mask
       UpperCAmelCase_				:   Dict							= use_token_type_ids
       UpperCAmelCase_				:   str							= use_labels
       UpperCAmelCase_				:   Union[str, Any]							= vocab_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_size
       UpperCAmelCase_				:   Union[str, Any]							= embedding_size
       UpperCAmelCase_				:   Optional[int]							= num_hidden_layers
       UpperCAmelCase_				:   Tuple							= num_attention_heads
       UpperCAmelCase_				:   List[Any]							= intermediate_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_act
       UpperCAmelCase_				:   Any							= hidden_dropout_prob
       UpperCAmelCase_				:   List[str]							= attention_probs_dropout_prob
       UpperCAmelCase_				:   List[str]							= max_position_embeddings
       UpperCAmelCase_				:   Dict							= type_vocab_size
       UpperCAmelCase_				:   Tuple							= type_sequence_label_size
       UpperCAmelCase_				:   Dict							= initializer_range
       UpperCAmelCase_				:   Any							= num_choices





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

       UpperCAmelCase_				:   Any							= None
       if self.use_attention_mask:
          UpperCAmelCase_				:   List[str]							= random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_				:   List[str]							= None
       if self.use_token_type_ids:
          UpperCAmelCase_				:   str							= ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size						)

       UpperCAmelCase_				:   int							= ElectraConfig(
           vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, embedding_size=self.embedding_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, )

       return config, input_ids, token_type_ids, attention_mask





    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= self.prepare_config_and_inputs()
       UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_				:   Union[str, Any]							= config_and_inputs
       UpperCAmelCase_				:   Optional[Any]							= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





@require_flax
class 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Any								=			True

    A_	:  int								=			(
        (
            FlaxElectraModel,
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForPreTraining,
            FlaxElectraForTokenClassification,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForMultipleChoice,
            FlaxElectraForSequenceClassification,
        )
        if is_flax_available()
        else ()
    )





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= FlaxElectraModelTester(self						)





    @slow
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       for model_class_name in self.all_model_classes:
          if model_class_name == FlaxElectraForMaskedLM:
             UpperCAmelCase_				:   List[str]							= model_class_name.from_pretrained('''google/electra-small-generator'''						)
          else:
             UpperCAmelCase_				:   Optional[Any]							= model_class_name.from_pretrained('''google/electra-small-discriminator'''						)
          UpperCAmelCase_				:   Tuple							= model(np.ones((1, 1)						)						)
          self.assertIsNotNone(__snake_case						)