

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


lowerCAmelCase_     :   int 			=			getLogger(__name__)


lowerCAmelCase_     :   str 			=			"cuda" if torch.cuda.is_available() else "cpu"


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 8						,	SCREAMING_SNAKE_CASE__ = DEFAULT_DEVICE						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__="summarization"						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	):
						snake_case_     =			Path(snake_case_ ).open('''w'''						,	encoding='''utf-8''' )
						snake_case_     =			str(snake_case_ )
						snake_case_     =			AutoModelForSeqaSeqLM.from_pretrained(snake_case_ ).to(snake_case_ )
						if fpaa:
												snake_case_     =			model.half()

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

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


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


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__=True ):
						snake_case_     =			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
						snake_case_     =			parser.parse_known_args()
						snake_case_     =			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:
												snake_case_     =			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												snake_case_     =			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.''' )
						snake_case_     =			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
						snake_case_     =			calculate_bleu if '''translation''' in args.task else calculate_rouge
						snake_case_     =			[x.rstrip() for x in open(args.save_path ).readlines()]
						snake_case_     =			[x.rstrip() for x in open(args.reference_path ).readlines()][: len(snake_case_ )]
						snake_case_     =			score_fn(snake_case_						,	snake_case_ )
						scores.update(snake_case_ )

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


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class 			snake_case_     (	nn.Module      ):







			'''simple docstring'''

			def __init__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       List[str]=1.0							)   ->Tuple:
									super().__init__()

									snake_case_     =			cut_size
									snake_case_     =			cut_power







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Dict							)   ->Union[str, Any]:
									snake_case_,					snake_case_     =			pixel_values.shape[2:4]
									snake_case_     =			min(_UpperCamelCase							,     _UpperCamelCase							)
									snake_case_     =			min(_UpperCamelCase							,     _UpperCamelCase							,     self.cut_size							)
									snake_case_     =			[]
									for _ in range(_UpperCamelCase							):
															snake_case_     =			int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
															snake_case_     =			torch.randint(0							,     sideX - size + 1							,     ()							)
															snake_case_     =			torch.randint(0							,     sideY - size + 1							,     ()							)
															snake_case_     =			pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(_UpperCamelCase							,     self.cut_size							)							)
									return torch.cat(_UpperCamelCase							)






def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						for param in model.parameters():
												snake_case_     =			value





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			def __init__(  self				:       List[Any]							,     _UpperCamelCase				:       AutoencoderKL							,     _UpperCamelCase				:       CLIPTextModel							,     _UpperCamelCase				:       CLIPModel							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       UNetaDConditionModel							,     _UpperCamelCase				:       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,     _UpperCamelCase				:       CLIPImageProcessor							,     )   ->Union[str, Any]:
									super().__init__()
									self.register_modules(
									    vae=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     clip_model=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							,     )

									snake_case_     =			transforms.Normalize(mean=feature_extractor.image_mean							,     std=feature_extractor.image_std							)
									snake_case_     =			(
									    feature_extractor.size
									    if isinstance(feature_extractor.size							,     _UpperCamelCase							)
									    else feature_extractor.size['''shortest_edge''']
									)
									snake_case_     =			MakeCutouts(self.cut_out_size							)

									set_requires_grad(self.text_encoder							,     _UpperCamelCase							)
									set_requires_grad(self.clip_model							,     _UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       Optional[Union[str, int]] = "auto"							)   ->int:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_     =			self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_UpperCamelCase							)







			def        snake_case__(  self				:       str							)   ->Optional[Any]:
									self.enable_attention_slicing(_UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Union[str, Any]:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Dict:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Dict							)   ->Optional[int]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							)   ->List[Any]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			@torch.enable_grad()
			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Optional[Any]							,     _UpperCamelCase				:       Optional[int]							,     _UpperCamelCase				:       Tuple=True							,     )   ->Union[str, Any]:
									snake_case_     =			latents.detach().requires_grad_()

									snake_case_     =			self.scheduler.scale_model_input(_UpperCamelCase							,     _UpperCamelCase							)

									# predict the noise residual
									snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

									if isinstance(self.scheduler							,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
															snake_case_     =			self.scheduler.alphas_cumprod[timestep]
															snake_case_     =			1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															snake_case_     =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															snake_case_     =			torch.sqrt(_UpperCamelCase							)
															snake_case_     =			pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler							,     _UpperCamelCase							):
															snake_case_     =			self.scheduler.sigmas[index]
															snake_case_     =			latents - sigma * noise_pred
									else:
															raise ValueError(f'''scheduler type {type(self.scheduler							)} not supported'''							)

									snake_case_     =			1 / self.vae.config.scaling_factor * sample
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample
									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)

									if use_cutouts:
															snake_case_     =			self.make_cutouts(_UpperCamelCase							,     _UpperCamelCase							)
									else:
															snake_case_     =			transforms.Resize(self.cut_out_size							)(_UpperCamelCase							)
									snake_case_     =			self.normalize(_UpperCamelCase							).to(latents.dtype							)

									snake_case_     =			self.clip_model.get_image_features(_UpperCamelCase							)
									snake_case_     =			image_embeddings_clip / image_embeddings_clip.norm(p=2							,     dim=-1							,     keepdim=_UpperCamelCase							)

									if use_cutouts:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							)
															snake_case_     =			dists.view([num_cutouts, sample.shape[0], -1]							)
															snake_case_     =			dists.sum(2							).mean(0							).sum() * clip_guidance_scale
									else:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							).mean() * clip_guidance_scale

									snake_case_     =			-torch.autograd.grad(_UpperCamelCase							,     _UpperCamelCase							)[0]

									if isinstance(self.scheduler							,     _UpperCamelCase							):
															snake_case_     =			latents.detach() + grads * (sigma**2)
															snake_case_     =			noise_pred_original
									else:
															snake_case_     =			noise_pred_original - torch.sqrt(_UpperCamelCase							) * grads
									return noise_pred, latents







			@torch.no_grad()
			def __call__(  self				:       Optional[int]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[float] = 1_0_0							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 4							,     _UpperCamelCase				:       Optional[bool] = True							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     )   ->str:
									if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			1
									elif isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			len(_UpperCamelCase							)
									else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase							)}'''							)

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

									# get prompt text embeddings
									snake_case_     =			self.tokenizer(
									    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     )
									snake_case_     =			self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
									# duplicate text embeddings for each generation per prompt
									snake_case_     =			text_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					snake_case_     =			self.tokenizer(
																					    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     ).input_ids.to(self.device							)
															else:
																					snake_case_     =			text_input.input_ids.to(self.device							)
															snake_case_     =			self.clip_model.get_text_features(_UpperCamelCase							)
															snake_case_     =			text_embeddings_clip / text_embeddings_clip.norm(p=2							,     dim=-1							,     keepdim=_UpperCamelCase							)
															# duplicate text embeddings clip for each generation per prompt
															snake_case_     =			text_embeddings_clip.repeat_interleave(_UpperCamelCase							,     dim=0							)

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									snake_case_     =			guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															snake_case_     =			text_input.input_ids.shape[-1]
															snake_case_     =			self.tokenizer(['''''']							,     padding='''max_length'''							,     max_length=_UpperCamelCase							,     return_tensors='''pt'''							)
															snake_case_     =			self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
															# duplicate unconditional embeddings for each generation per prompt
															snake_case_     =			uncond_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															snake_case_     =			torch.cat([uncond_embeddings, text_embeddings]							)

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									snake_case_     =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									snake_case_     =			text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					snake_case_     =			torch.randn(_UpperCamelCase							,     generator=_UpperCamelCase							,     device='''cpu'''							,     dtype=_UpperCamelCase							).to(
																					    self.device							)
															else:
																					snake_case_     =			torch.randn(_UpperCamelCase							,     generator=_UpperCamelCase							,     device=self.device							,     dtype=_UpperCamelCase							)
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
															snake_case_     =			latents.to(self.device							)

									# set timesteps
									snake_case_     =			'''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_offset:
															snake_case_     =			1

									self.scheduler.set_timesteps(_UpperCamelCase							,     **_UpperCamelCase							)

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									snake_case_     =			self.scheduler.timesteps.to(self.device							)

									# scale the initial noise by the standard deviation required by the scheduler
									snake_case_     =			latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									snake_case_     =			'''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_eta:
															snake_case_     =			eta

									# check if the scheduler accepts generator
									snake_case_     =			'''generator''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									if accepts_generator:
															snake_case_     =			generator

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

															# predict the noise residual
															snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																					snake_case_,					snake_case_     =			noise_pred.chunk(2							)
																					snake_case_     =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																					snake_case_     =			(
																					    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
																					)
																					snake_case_,					snake_case_     =			self.cond_fn(
																					    _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     )

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_     =			self.scheduler.step(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     **_UpperCamelCase							).prev_sample

									# scale and decode the image latents with vae
									snake_case_     =			1 / self.vae.config.scaling_factor * latents
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample

									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)
									snake_case_     =			image.cpu().permute(0							,     2							,     3							,     1							).numpy()

									if output_type == "pil":
															snake_case_     =			self.numpy_to_pil(_UpperCamelCase							)

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=_UpperCamelCase							,     nsfw_content_detected=_UpperCamelCase							)