
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,
)


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

_UpperCAmelCase					: Union[str, Any]      =       "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def       A       (					lowercase    ,       lowercase    ,       lowercase=8 )   ->  Tuple:
		'''simple docstring'''

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


class 				lowercase					(		lowercase__   ):



		def __init__( self				,      A_				,      A_				,      A_				,      )  ->					Dict:
				"""simple docstring"""
				super().__init__()

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



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				if latents is None:
						UpperCamelCase		=     randn_tensor(lowercase_				,      generator=lowercase_				,      device=lowercase_				,      dtype=lowercase_       )
				else:
						if latents.shape != shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
						UpperCamelCase		=     latents.to(lowercase_       )

				UpperCamelCase		=     latents * scheduler.init_noise_sigma
				return latents



		def __UpperCamelCase ( self				,      A_=0       )  ->					Dict:
				"""simple docstring"""
				if is_accelerate_available():
						from accelerate import cpu_offload
				else:
						raise ImportError('Please install accelerate via `pip install accelerate`'       )

				UpperCamelCase		=     torch.device(F'''cuda:{gpu_id}'''       )

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



		def __UpperCamelCase ( self				,      A_=0       )  ->					List[Any]:
				"""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		=     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)

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

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



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				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				,      A_				,      A_				,      A_ = 512				,      A_ = 512				,      A_ = 100				,      A_ = 4.0				,      A_ = 1				,      A_ = None				,      A_ = None				,      A_ = "pil"				,      A_ = True				,      )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._execution_device

				UpperCamelCase		=     guidance_scale > 1.0

				if isinstance(lowercase_				,      lowercase_       ):
						UpperCamelCase		=     torch.cat(lowercase_				,      dim=0       )
				UpperCamelCase		=     image_embeds.shape[0] * num_images_per_prompt
				if isinstance(lowercase_				,      lowercase_       ):
						UpperCamelCase		=     torch.cat(lowercase_				,      dim=0       )

				if do_classifier_free_guidance:
						UpperCamelCase		=     image_embeds.repeat_interleave(lowercase_				,      dim=0       )
						UpperCamelCase		=     negative_image_embeds.repeat_interleave(lowercase_				,      dim=0       )

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

				self.scheduler.set_timesteps(lowercase_				,      device=lowercase_       )
				UpperCamelCase		=     self.scheduler.timesteps

				UpperCamelCase		=     self.unet.config.in_channels

				UpperCamelCase		=     downscale_height_and_width(lowercase_				,      lowercase_				,      self.movq_scale_factor       )

				# create initial latent
				UpperCamelCase		=     self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)				,      image_embeds.dtype				,      lowercase_				,      lowercase_				,      lowercase_				,      self.scheduler				,      )

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

						UpperCamelCase		=     {"image_embeds": image_embeds}
						UpperCamelCase		=     self.unet(
						    sample=lowercase_				,      timestep=lowercase_				,      encoder_hidden_states=lowercase_				,      added_cond_kwargs=lowercase_				,      return_dict=lowercase_				,      )[0]

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

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

				if output_type == "pil":
						UpperCamelCase		=     self.numpy_to_pil(lowercase_       )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=lowercase_       )


"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    {
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


