from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__UpperCamelCase		:       List[Any]									=     logging.get_logger(__name__)  # pylint: disable=invalid-name





class 					lowercase__						(   UpperCamelCase_):


						def __init__(       self	:				Optional[int]    ,		UpperCamelCase__	:				CLIPSegForImageSegmentation    ,		UpperCamelCase__	:				CLIPSegProcessor    ,		UpperCamelCase__	:				AutoencoderKL    ,		UpperCamelCase__	:				CLIPTextModel    ,		UpperCamelCase__	:				CLIPTokenizer    ,		UpperCamelCase__	:				UNetaDConditionModel    ,		UpperCamelCase__	:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,		UpperCamelCase__	:				StableDiffusionSafetyChecker    ,		UpperCamelCase__	:				CLIPImageProcessor    ,		):





												'''simple docstring'''





												super().__init__()

												if hasattr(scheduler.config    ,		'''steps_offset'''						) and scheduler.config.steps_offset != 1:
																		SCREAMING_SNAKE_CASE							:    str							=							(
																		    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																		    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																		    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																		    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																		    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																		    ''' file'''
																		)
																		deprecate('''steps_offset!=1'''    ,		'''1.0.0'''    ,		UpperCamelCase__    ,		standard_warn=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							dict(scheduler.config						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							1
																		SCREAMING_SNAKE_CASE							:    Dict							=							FrozenDict(UpperCamelCase__						)

												if hasattr(scheduler.config    ,		'''skip_prk_steps'''						) and scheduler.config.skip_prk_steps is False:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							(
																		    f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
																		    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
																		    ''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
																		    ''' Hub, it would be very nice if you could open a Pull request for the'''
																		    ''' `scheduler/scheduler_config.json` file'''
																		)
																		deprecate('''skip_prk_steps not set'''    ,		'''1.0.0'''    ,		UpperCamelCase__    ,		standard_warn=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							dict(scheduler.config						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							True
																		SCREAMING_SNAKE_CASE							:    List[str]							=							FrozenDict(UpperCamelCase__						)

												if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
																		    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
																		    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
																		    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
																		    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''						)

												self.register_modules(
												    segmentation_model=UpperCamelCase__    ,		segmentation_processor=UpperCamelCase__    ,		vae=UpperCamelCase__    ,		text_encoder=UpperCamelCase__    ,		tokenizer=UpperCamelCase__    ,		unet=UpperCamelCase__    ,		scheduler=UpperCamelCase__    ,		safety_checker=UpperCamelCase__    ,		feature_extractor=UpperCamelCase__    ,		)


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Optional[Union[str, int]] = "auto"						):





												'''simple docstring'''





												if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												self.enable_attention_slicing(UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.device('''cuda'''						)

												for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																								cpu_offload(UpperCamelCase__    ,		UpperCamelCase__						)


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def        __A       (       self	:				int						):





												'''simple docstring'''





												if self.device != torch.device('''meta'''						) or not hasattr(self.unet    ,		'''_hf_hook'''						):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(UpperCamelCase__    ,		'''_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()
						def __call__(       self	:				List[str]    ,		UpperCamelCase__	:				Union[str, List[str]]    ,		UpperCamelCase__	:				Union[torch.FloatTensor, PIL.Image.Image]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				int = 512    ,		UpperCamelCase__	:				int = 512    ,		UpperCamelCase__	:				int = 50    ,		UpperCamelCase__	:				float = 7.5    ,		UpperCamelCase__	:				Optional[Union[str, List[str]]] = None    ,		UpperCamelCase__	:				Optional[int] = 1    ,		UpperCamelCase__	:				float = 0.0    ,		UpperCamelCase__	:				Optional[torch.Generator] = None    ,		UpperCamelCase__	:				Optional[torch.FloatTensor] = None    ,		UpperCamelCase__	:				Optional[str] = "pil"    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,		UpperCamelCase__	:				int = 1    ,		**UpperCamelCase__	:				List[Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.segmentation_processor(
												    text=[text]    ,		images=[image]    ,		padding='''max_length'''    ,		return_tensors='''pt'''						).to(self.device						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.segmentation_model(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
												SCREAMING_SNAKE_CASE							:    str							=							self.numpy_to_pil(UpperCamelCase__						)[0].resize(image.size						)

												# Run inpainting pipeline with the generated mask
												SCREAMING_SNAKE_CASE							:    str							=							StableDiffusionInpaintPipeline(
												    vae=self.vae    ,		text_encoder=self.text_encoder    ,		tokenizer=self.tokenizer    ,		unet=self.unet    ,		scheduler=self.scheduler    ,		safety_checker=self.safety_checker    ,		feature_extractor=self.feature_extractor    ,		)
												return inpainting_pipeline(
												    prompt=UpperCamelCase__    ,		image=UpperCamelCase__    ,		mask_image=UpperCamelCase__    ,		height=UpperCamelCase__    ,		width=UpperCamelCase__    ,		num_inference_steps=UpperCamelCase__    ,		guidance_scale=UpperCamelCase__    ,		negative_prompt=UpperCamelCase__    ,		num_images_per_prompt=UpperCamelCase__    ,		eta=UpperCamelCase__    ,		generator=UpperCamelCase__    ,		latents=UpperCamelCase__    ,		output_type=UpperCamelCase__    ,		return_dict=UpperCamelCase__    ,		callback=UpperCamelCase__    ,		callback_steps=UpperCamelCase__    ,		)






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		:       Any									=     getLogger(__name__)


__UpperCamelCase		:       int									=     'cuda' if torch.cuda.is_available() else 'cpu'
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = DEFAULT_DEVICE		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Path(_lowercase						).open('''w'''		,  encoding='''utf-8'''						)
						SCREAMING_SNAKE_CASE							:    int							=							str(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).to(_lowercase						)
						if fpaa:
												SCREAMING_SNAKE_CASE							:    Dict							=							model.half()

						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoTokenizer.from_pretrained(_lowercase						)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						# update config with task specific params
						use_task_specific_params(_lowercase		,  _lowercase						)
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						for examples_chunk in tqdm(list(chunks(_lowercase		,  _lowercase						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''						).to(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							model.generate(
												    input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.batch_decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase						)
												for hypothesis in dec:
																		fout.write(hypothesis + '''\n'''						)
																		fout.flush()
						fout.close()
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(time.time() - start_time						)  # seconds
						SCREAMING_SNAKE_CASE							:    str							=							len(_lowercase						)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4						)}
def       A	(    ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)
def       A	(    _lowercase=True						):
						SCREAMING_SNAKE_CASE							:    int							=							argparse.ArgumentParser()
						parser.add_argument('''model_name'''		,  type=_lowercase		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
						parser.add_argument('''input_path'''		,  type=_lowercase		,  help='''like cnn_dm/test.source'''						)
						parser.add_argument('''save_path'''		,  type=_lowercase		,  help='''where to save summaries'''						)
						parser.add_argument('''--reference_path'''		,  type=_lowercase		,  required=_lowercase		,  help='''like cnn_dm/test.target'''						)
						parser.add_argument('''--score_path'''		,  type=_lowercase		,  required=_lowercase		,  default='''metrics.json'''		,  help='''where to save metrics'''						)
						parser.add_argument('''--device'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''cuda, cuda:1, cpu etc.'''						)
						parser.add_argument(
						    '''--prefix'''		,  type=_lowercase		,  required=_lowercase		,  default=_lowercase		,  help='''will be added to the begininng of src examples'''						)
						parser.add_argument('''--task'''		,  type=_lowercase		,  default='''summarization'''		,  help='''used for task_specific_params + metrics'''						)
						parser.add_argument('''--bs'''		,  type=_lowercase		,  default=8		,  required=_lowercase		,  help='''batch size'''						)
						parser.add_argument(
						    '''--n_obs'''		,  type=_lowercase		,  default=-1		,  required=_lowercase		,  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=_lowercase		,  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
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
						if args.n_obs > 0:
												SCREAMING_SNAKE_CASE							:    Any							=							examples[: args.n_obs]
						Path(args.save_path						).parent.mkdir(exist_ok=_lowercase						)

						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'''						)

						SCREAMING_SNAKE_CASE							:    List[str]							=							generate_summaries_or_translations(
						    _lowercase		,  args.save_path		,  args.model_name		,  batch_size=args.bs		,  device=args.device		,  fpaa=args.fpaa		,  task=args.task		,  prefix=args.prefix		,  **_lowercase		,  )

						if args.reference_path is None:
												return {}

						# Compute scores
						SCREAMING_SNAKE_CASE							:    Dict							=							calculate_bleu if '''translation''' in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[x.rstrip() for x in open(args.save_path						).readlines()]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowercase						)]
						SCREAMING_SNAKE_CASE							:    dict							=							score_fn(_lowercase		,  _lowercase						)
						scores.update(_lowercase						)

						if args.dump_args:
												scores.update(_lowercase						)
						if args.info:
												SCREAMING_SNAKE_CASE							:    Tuple							=							args.info

						if verbose:
												print(_lowercase						)

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





