




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

lowercase_      :							Optional[int]    							=					'\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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=8		):

						'''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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_=512      ,       snake_case_=512		):

						'''simple docstring'''


						_UpperCAmelCase						=	pil_image.resize((w, h)      ,       resample=Image.BICUBIC      ,       reducing_gap=1		)
						_UpperCAmelCase						=	np.array(pil_image.convert("RGB"		)		)
						_UpperCAmelCase						=	arr.astype(np.floataa		) / 127.5 - 1
						_UpperCAmelCase						=	np.transpose(snake_case_      ,       [2, 0, 1]		)
						_UpperCAmelCase						=	torch.from_numpy(snake_case_		).unsqueeze(0		)
						return image






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     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						=	2 ** (len(self.movq.config.block_out_channels ) - 1)







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Any       ,       snake_case__       :     Tuple       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	min(int(num_inference_steps * strength )       ,       snake_case__ )

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

									return timesteps, num_inference_steps - t_start







			def UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     int       ,       snake_case__       :     List[str]       ,       snake_case__       :     Tuple       ,       snake_case__       :     Tuple       ,       snake_case__       :     List[Any]       ,       snake_case__       :     str       ,       snake_case__       :     List[str]=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						=	image.to(device=snake_case__       ,       dtype=snake_case__ )

									_UpperCAmelCase						=	batch_size * num_images_per_prompt

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

															_UpperCAmelCase						=	self.movq.config.scaling_factor * init_latents

									_UpperCAmelCase						=	torch.cat([init_latents]       ,       dim=0 )

									_UpperCAmelCase						=	init_latents.shape
									_UpperCAmelCase						=	randn_tensor(snake_case__       ,       generator=snake_case__       ,       device=snake_case__       ,       dtype=snake_case__ )

									# get latents
									_UpperCAmelCase						=	self.scheduler.add_noise(snake_case__       ,       snake_case__       ,       snake_case__ )

									_UpperCAmelCase						=	init_latents

									return latents







			def UpperCamelCase     (			self       :     int       ,       snake_case__       :     int=0 ):




									"""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(snake_case__       ,       snake_case__ )







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

									# 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       :     List[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       :     Dict       ,       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						=	self._execution_device

									_UpperCAmelCase						=	guidance_scale > 1.0

									if isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	torch.cat(snake_case__       ,       dim=0 )
									if isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	torch.cat(snake_case__       ,       dim=0 )
									if isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	torch.cat(snake_case__       ,       dim=0 )

									_UpperCAmelCase						=	image_embeds.shape[0]

									if do_classifier_free_guidance:
															_UpperCAmelCase						=	image_embeds.repeat_interleave(snake_case__       ,       dim=0 )
															_UpperCAmelCase						=	negative_image_embeds.repeat_interleave(snake_case__       ,       dim=0 )
															_UpperCAmelCase						=	hint.repeat_interleave(snake_case__       ,       dim=0 )

									_UpperCAmelCase						=	torch.cat([negative_image_embeds, image_embeds]       ,       dim=0 ).to(dtype=self.unet.dtype       ,       device=snake_case__ )
									_UpperCAmelCase						=	torch.cat([hint, hint]       ,       dim=0 ).to(dtype=self.unet.dtype       ,       device=snake_case__ )

									if not isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	[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						=	torch.cat([prepare_image(snake_case__       ,       snake_case__       ,       snake_case__ ) for i in image]       ,       dim=0 )
									_UpperCAmelCase						=	image.to(dtype=image_embeds.dtype       ,       device=snake_case__ )

									_UpperCAmelCase						=	self.movq.encode(snake_case__ )["latents"]
									_UpperCAmelCase						=	latents.repeat_interleave(snake_case__       ,       dim=0 )
									self.scheduler.set_timesteps(snake_case__       ,       device=snake_case__ )
									_UpperCAmelCase				,					_UpperCAmelCase						=	self.get_timesteps(snake_case__       ,       snake_case__       ,       snake_case__ )
									_UpperCAmelCase						=	timesteps[:1].repeat(batch_size * num_images_per_prompt )
									_UpperCAmelCase				,					_UpperCAmelCase						=	downscale_height_and_width(snake_case__       ,       snake_case__       ,       self.movq_scale_factor )
									_UpperCAmelCase						=	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						=	torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

															_UpperCAmelCase						=	{"image_embeds": image_embeds, "hint": hint}
															_UpperCAmelCase						=	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						=	noise_pred.split(latents.shape[1]       ,       dim=1 )
																					_UpperCAmelCase				,					_UpperCAmelCase						=	noise_pred.chunk(2 )
																					_UpperCAmelCase				,					_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				,					_UpperCAmelCase						=	noise_pred.split(latents.shape[1]       ,       dim=1 )

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

															_UpperCAmelCase						=	self.scheduler.step(
															    snake_case__       ,       snake_case__       ,       snake_case__       ,       generator=snake_case__       ,       )[0]

									# post-processing
									_UpperCAmelCase						=	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						=	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(snake_case__ )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=snake_case__ )










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase_      :							int    							=					getLogger(__name__)


lowercase_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	Path(snake_case_		).open("w"      ,       encoding="utf-8"		)
						_UpperCAmelCase						=	str(snake_case_		)
						_UpperCAmelCase						=	AutoModelForSeqaSeqLM.from_pretrained(snake_case_		).to(snake_case_		)
						if fpaa:
												_UpperCAmelCase						=	model.half()

						_UpperCAmelCase						=	AutoTokenizer.from_pretrained(snake_case_		)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

						_UpperCAmelCase						=	time.time()
						# update config with task specific params
						use_task_specific_params(snake_case_      ,       snake_case_		)
						if prefix is None:
												_UpperCAmelCase						=	prefix or getattr(model.config      ,       "prefix"      ,       ""		) or ""
						for examples_chunk in tqdm(list(chunks(snake_case_      ,       snake_case_		)		)		):
												_UpperCAmelCase						=	[prefix + text for text in examples_chunk]
												_UpperCAmelCase						=	tokenizer(snake_case_      ,       return_tensors="pt"      ,       truncation=snake_case_      ,       padding="longest"		).to(snake_case_		)
												_UpperCAmelCase						=	model.generate(
												    input_ids=batch.input_ids      ,       attention_mask=batch.attention_mask      ,       **snake_case_      ,       )
												_UpperCAmelCase						=	tokenizer.batch_decode(snake_case_      ,       skip_special_tokens=snake_case_      ,       clean_up_tokenization_spaces=snake_case_		)
												for hypothesis in dec:
																		fout.write(hypothesis + "\n"		)
																		fout.flush()
						fout.close()
						_UpperCAmelCase						=	int(time.time() - start_time		)  # seconds
						_UpperCAmelCase						=	len(snake_case_		)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,       4		)}


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser()
						parser.add_argument("model_name"      ,       type=snake_case_      ,       help="like facebook/bart-large-cnn,t5-base, etc."		)
						parser.add_argument("input_path"      ,       type=snake_case_      ,       help="like cnn_dm/test.source"		)
						parser.add_argument("save_path"      ,       type=snake_case_      ,       help="where to save summaries"		)
						parser.add_argument("--reference_path"      ,       type=snake_case_      ,       required=snake_case_      ,       help="like cnn_dm/test.target"		)
						parser.add_argument("--score_path"      ,       type=snake_case_      ,       required=snake_case_      ,       default="metrics.json"      ,       help="where to save metrics"		)
						parser.add_argument("--device"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="cuda, cuda:1, cpu etc."		)
						parser.add_argument(
						    "--prefix"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="will be added to the begininng of src examples"		)
						parser.add_argument("--task"      ,       type=snake_case_      ,       default="summarization"      ,       help="used for task_specific_params + metrics"		)
						parser.add_argument("--bs"      ,       type=snake_case_      ,       default=8      ,       required=snake_case_      ,       help="batch size"		)
						parser.add_argument(
						    "--n_obs"      ,       type=snake_case_      ,       default=-1      ,       required=snake_case_      ,       help="How many observations. Defaults to all."		)
						parser.add_argument("--fp16"      ,       action="store_true"		)
						parser.add_argument("--dump-args"      ,       action="store_true"      ,       help="print the custom hparams with the results"		)
						parser.add_argument(
						    "--info"      ,       nargs="?"      ,       type=snake_case_      ,       const=datetime_now()      ,       help=(
						        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
						        " lang=en-ru. If no value is passed, the current datetime string will be used."
						    )      ,       )
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						_UpperCAmelCase				,					_UpperCAmelCase						=	parser.parse_known_args()
						_UpperCAmelCase						=	parse_numeric_n_bool_cl_kwargs(snake_case_		)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""		)
						with open(args.input_path		) as f:
												_UpperCAmelCase						=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												_UpperCAmelCase						=	examples[: args.n_obs]
						Path(args.save_path		).parent.mkdir(exist_ok=snake_case_		)
						if args.reference_path is None and Path(args.score_path		).exists():
												warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""		)
						_UpperCAmelCase						=	generate_summaries_or_translations(
						    snake_case_      ,       args.save_path      ,       args.model_name      ,       batch_size=args.bs      ,       device=args.device      ,       fpaa=args.fpaa      ,       task=args.task      ,       prefix=args.prefix      ,       **snake_case_      ,       )

						if args.reference_path is None:
												return {}

						# Compute scores
						_UpperCAmelCase						=	calculate_bleu if "translation" in args.task else calculate_rouge
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.save_path		).readlines()]
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	args.info

						if verbose:
												print(snake_case_		)

						if args.score_path is not None:
												json.dump(snake_case_      ,       open(args.score_path      ,       "w"		)		)

						return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)




