


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

UpperCAmelCase_	:     List[str]							     =	'\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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=8  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_SCREAMING_SNAKE_CASE				: List[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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=512							,   SCREAMING_SNAKE_CASE__=512  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   pil_image.resize((w, h)							,   resample=Image.BICUBIC							,   reducing_gap=1  )
				_SCREAMING_SNAKE_CASE				: Dict  	=   np.array(pil_image.convert("""RGB"""  )  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   arr.astype(np.floataa  ) / 1_2_7.5 - 1
				_SCREAMING_SNAKE_CASE				: str  	=   np.transpose(SCREAMING_SNAKE_CASE__							,   [2, 0, 1]  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   torch.from_numpy(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )
				return image







class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      ):
											super().__init__()

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






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											# get the original timestep using init_timestep
											_SCREAMING_SNAKE_CASE				: List[str]  	=   min(int(num_inference_steps * strength						)					,      __snake_case						)

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

											return timesteps, num_inference_steps - t_start






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case=None						):
											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						)}"""						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   image.to(device=__snake_case					,      dtype=__snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   batch_size * num_images_per_prompt

											if image.shape[1] == 4:
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   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						):
																			_SCREAMING_SNAKE_CASE				: int  	=   [
																			    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__snake_case						)
																			]
																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   torch.cat(__snake_case					,      dim=0						)
															else:
																			_SCREAMING_SNAKE_CASE				: Dict  	=   self.movq.encode(__snake_case						).latent_dist.sample(__snake_case						)

															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.movq.config.scaling_factor * init_latents

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

											_SCREAMING_SNAKE_CASE				: Any  	=   init_latents.shape
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   randn_tensor(__snake_case					,      generator=__snake_case					,      device=__snake_case					,      dtype=__snake_case						)

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

											_SCREAMING_SNAKE_CASE				: Any  	=   init_latents

											return latents






							def UpperCAmelCase_			(					self					,      __snake_case=0						):
											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

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

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






							def UpperCAmelCase_			(					self					,      __snake_case=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."""						)

											_SCREAMING_SNAKE_CASE				: str  	=   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)

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   None
											for cpu_offloaded_model in [self.unet, self.movq]:
															_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Any  	=   cpu_offload_with_hook(__snake_case					,      __snake_case					,      prev_module_hook=__snake_case						)

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






							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def UpperCAmelCase_			(					self						):
											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					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case = 512					,      __snake_case = 512					,      __snake_case = 100					,      __snake_case = 4.0					,      __snake_case = 0.3					,      __snake_case = 1					,      __snake_case = None					,      __snake_case = "pil"					,      __snake_case = True					,      ):
											_SCREAMING_SNAKE_CASE				: Dict  	=   self._execution_device

											_SCREAMING_SNAKE_CASE				: int  	=   guidance_scale > 1.0

											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   torch.cat(__snake_case					,      dim=0						)
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   torch.cat(__snake_case					,      dim=0						)
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Tuple  	=   torch.cat(__snake_case					,      dim=0						)

											_SCREAMING_SNAKE_CASE				: Dict  	=   image_embeds.shape[0]

											if do_classifier_free_guidance:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   image_embeds.repeat_interleave(__snake_case					,      dim=0						)
															_SCREAMING_SNAKE_CASE				: str  	=   negative_image_embeds.repeat_interleave(__snake_case					,      dim=0						)
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   hint.repeat_interleave(__snake_case					,      dim=0						)

											_SCREAMING_SNAKE_CASE				: Any  	=   torch.cat([negative_image_embeds, image_embeds]					,      dim=0						).to(dtype=self.unet.dtype					,      device=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   torch.cat([hint, hint]					,      dim=0						).to(dtype=self.unet.dtype					,      device=__snake_case						)

											if not isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   [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"""						)

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

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

															_SCREAMING_SNAKE_CASE				: int  	=   {"""image_embeds""": image_embeds, """hint""": hint}
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   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:
																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: str  	=   noise_pred.split(latents.shape[1]					,      dim=1						)
																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Any  	=   noise_pred.chunk(2						)
																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Tuple  	=   variance_pred.chunk(2						)
																			_SCREAMING_SNAKE_CASE				: str  	=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			_SCREAMING_SNAKE_CASE				: 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"]
															):
																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Tuple  	=   noise_pred.split(latents.shape[1]					,      dim=1						)

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

															_SCREAMING_SNAKE_CASE				: Tuple  	=   self.scheduler.step(
															    __snake_case					,      __snake_case					,      __snake_case					,      generator=__snake_case					,      )[0]

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

											if output_type == "pil":
															_SCREAMING_SNAKE_CASE				: int  	=   self.numpy_to_pil(__snake_case						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=__snake_case						)








'''simple docstring'''


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





				return field(default_factory=lambda: default							,   metadata=SCREAMING_SNAKE_CASE__  )





@dataclass
class 					lowercase__						:





							'''simple docstring'''


							A_							:   str   			=						field(
							    metadata={"""help""": """The csv file to plot."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Disable logarithmic scale when plotting"""}  ,  )
							A_							:   bool   			=						field(
							    default=_snake_case  ,  metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }  ,  )
							A_							:   Optional[str]   			=						field(
							    default=_snake_case  ,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}  ,  )
							A_							:   Optional[List[str]]   			=						list_field(
							    default=_snake_case  ,  metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}      )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								int(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				try:
								float(SCREAMING_SNAKE_CASE__  )
								return True
				except ValueError:
								return False





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

											with open(self.args.csv_file					,      newline=""""""						) as csv_file:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   csv.DictReader(__snake_case						)
															for row in reader:
																			_SCREAMING_SNAKE_CASE				: int  	=   row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]						)						)
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]						)						)
																			if can_convert_to_int(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Any  	=   int(row["""result"""]						)
																			elif can_convert_to_float(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   float(row["""result"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   plt.subplots()
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE				: int  	=   title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""						)
															ax.set_yscale("""log"""						)

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()						)

											for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
															_SCREAMING_SNAKE_CASE				: str  	=   sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: Any  	=   (
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							_SCREAMING_SNAKE_CASE				: Any  	=   np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,      dtype=__snake_case					,      )
																			else:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,      dtype=np.floataa					,      )

																			((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   np.asarray(__snake_case					,      __snake_case						)[: len(__snake_case						)]
																			plt.scatter(
																			    __snake_case					,      __snake_case					,      label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
																			plt.plot(__snake_case					,      __snake_case					,      """--"""						)

															title_str += f""" {label_model_name} vs."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__snake_case						)
											plt.xlabel(__snake_case						)
											plt.ylabel(__snake_case						)
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file						)
											else:
															plt.show()







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




