


'''simple docstring'''

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


_UpperCAmelCase				: Tuple							    =				getLogger(__name__)


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

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

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

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

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

def 							__magic_name__(						lowerCamelCase=True):
						__lowerCAmelCase							       =				argparse.ArgumentParser()
						parser.add_argument('''model_name''',				type=lowerCamelCase,				help='''like facebook/bart-large-cnn,t5-base, etc.''')
						parser.add_argument('''input_path''',				type=lowerCamelCase,				help='''like cnn_dm/test.source''')
						parser.add_argument('''save_path''',				type=lowerCamelCase,				help='''where to save summaries''')
						parser.add_argument('''--reference_path''',				type=lowerCamelCase,				required=lowerCamelCase,				help='''like cnn_dm/test.target''')
						parser.add_argument('''--score_path''',				type=lowerCamelCase,				required=lowerCamelCase,				default='''metrics.json''',				help='''where to save metrics''')
						parser.add_argument('''--device''',				type=lowerCamelCase,				required=lowerCamelCase,				default=lowerCamelCase,				help='''cuda, cuda:1, cpu etc.''')
						parser.add_argument(
						    '''--prefix''',				type=lowerCamelCase,				required=lowerCamelCase,				default=lowerCamelCase,				help='''will be added to the begininng of src examples''')
						parser.add_argument('''--task''',				type=lowerCamelCase,				default='''summarization''',				help='''used for task_specific_params + metrics''')
						parser.add_argument('''--bs''',				type=lowerCamelCase,				default=8,				required=lowerCamelCase,				help='''batch size''')
						parser.add_argument(
						    '''--n_obs''',				type=lowerCamelCase,				default=-1,				required=lowerCamelCase,				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=lowerCamelCase,				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
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				parser.parse_known_args()
						__lowerCAmelCase							       =				parse_numeric_n_bool_cl_kwargs(lowerCamelCase)
						if parsed_args and verbose:
												print(F"""parsed the following generate kwargs: {parsed_args}""")
						__lowerCAmelCase							       =				[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
						if args.n_obs > 0:
												__lowerCAmelCase							       =				examples[: args.n_obs]
						Path(args.save_path).parent.mkdir(exist_ok=lowerCamelCase)

						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.""")

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError('''Can\'t mix --fp16 and --device cpu''')

						__lowerCAmelCase							       =				generate_summaries_or_translations(
						    lowerCamelCase,				args.save_path,				args.model_name,				batch_size=args.bs,				device=args.device,				fpaa=args.fpaa,				task=args.task,				prefix=args.prefix,				**lowerCamelCase,				)

						if args.reference_path is None:
												return {}

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

						if args.dump_args:
												scores.update(lowerCamelCase)
						if args.info:
												__lowerCAmelCase							       =				args.info

						if verbose:
												print(lowerCamelCase)

						if args.score_path is not None:
												json.dump(lowerCamelCase,				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)





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

