





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


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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Any       , UpperCAmelCase_				:   CLIPSegForImageSegmentation       , UpperCAmelCase_				:   CLIPSegProcessor       , UpperCAmelCase_				:   AutoencoderKL       , UpperCAmelCase_				:   CLIPTextModel       , UpperCAmelCase_				:   CLIPTokenizer       , UpperCAmelCase_				:   UNetaDConditionModel       , UpperCAmelCase_				:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       , UpperCAmelCase_				:   StableDiffusionSafetyChecker       , UpperCAmelCase_				:   CLIPImageProcessor       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__()

   if hasattr(scheduler.config       , "steps_offset") and scheduler.config.steps_offset != 1:
     lowerCamelCase__:      List[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         F"""The configuration file of this scheduler: {scheduler} is outdated.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_)
     lowerCamelCase__:      Tuple			  =dict(scheduler.config)
     lowerCamelCase__:      List[str]			  =1
     lowerCamelCase__:      Optional[int]			  =FrozenDict(UpperCAmelCase_)

   if hasattr(scheduler.config       , "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
     lowerCamelCase__:      Tuple			  =(
         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_)
     lowerCamelCase__:      Optional[int]			  =dict(scheduler.config)
     lowerCamelCase__:      str			  =True
     lowerCamelCase__:      Optional[Any]			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   Optional[Union[str, int]] = "auto")       ->List[Any]:



   '''simple docstring'''



   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     lowerCamelCase__:      Dict			  =self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   self.enable_attention_slicing(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->List[Any]:



   '''simple docstring'''



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

   lowerCamelCase__:      Dict			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''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				:   int       , 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_				:   str       , )       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.segmentation_processor(
       text=[text]       , images=[image]       , padding="max_length"       , return_tensors="pt").to(self.device)
   lowerCamelCase__:      Dict			  =self.segmentation_model(**UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
   lowerCamelCase__:      List[Any]			  =self.numpy_to_pil(UpperCAmelCase_)[0].resize(image.size)

   # Run inpainting pipeline with the generated mask
   lowerCamelCase__:      int			  =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 shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   			=getLogger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					int = 10_24 ,   _UpperCAmelCase							:					Any="val" ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Union[str, Any]="summarization" ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Union[str, Any]=1 ,   _UpperCAmelCase							:					Dict = None ,   _UpperCAmelCase							:					Dict="" ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	str(_UpperCAmelCase						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl''' ,   rank=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	Path(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	save_dir.joinpath(f'rank_{local_rank}_output.json'						)
		torch.cuda.set_device(_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).cuda()
		if fpaa:
				__UpperCAmelCase    :     Any													=	model.half()
		# determine if we need to increase num_beams
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)  # update config with task specific params
		__UpperCAmelCase    :     List[str]													=	generate_kwargs.pop('''num_beams''' ,   model.config.num_beams						)  # AttributeError risk?
		if num_return_sequences > num_beams:
				__UpperCAmelCase    :     Any													=	num_return_sequences

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

		if max_source_length is None:
				__UpperCAmelCase    :     Optional[Any]													=	tokenizer.model_max_length
		if prefix is None:
				__UpperCAmelCase    :     str													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		__UpperCAmelCase    :     Union[str, Any]													=	SeqaSeqDataset(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   max_target_length=10_24 ,   type_path=_UpperCAmelCase ,   n_obs=_UpperCAmelCase ,   prefix=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
		# I set shuffle=True for a more accurate progress bar.
		# If all the longest samples are first, the prog bar estimate is too high at the beginning.
		__UpperCAmelCase    :     str													=	ds.make_sortish_sampler(_UpperCAmelCase ,   distributed=_UpperCAmelCase ,   add_extra_examples=_UpperCAmelCase ,   shuffle=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	DataLoader(_UpperCAmelCase ,   sampler=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   collate_fn=ds.collate_fn						)
		__UpperCAmelCase    :     List[Any]													=	[]
		for batch in tqdm(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	model.generate(
				    input_ids=batch['''input_ids'''].to(model.device						) ,   attention_mask=batch['''attention_mask'''].to(model.device						) ,   num_return_sequences=_UpperCAmelCase ,   num_beams=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     Optional[int]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	batch['''ids''']
				if num_return_sequences > 1:
						__UpperCAmelCase    :     Any													=	chunks(_UpperCAmelCase ,   _UpperCAmelCase						)  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(_UpperCAmelCase						):
						results.append({'''pred''': pred, '''id''': ids[i].item()}						)
		save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
		return results, sampler.num_replicas

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	argparse.ArgumentParser(
		    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
		parser.add_argument('''--data_dir''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument(
		    '''--model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.''' ,   default='''sshleifer/distilbart-xsum-12-3''' ,   )
		parser.add_argument('''--save_dir''' ,   type=_UpperCAmelCase ,   help='''where to save''' ,   default='''tmp_gen'''						)
		parser.add_argument('''--max_source_length''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase						)
		parser.add_argument(
		    '''--type_path''' ,   type=_UpperCAmelCase ,   default='''test''' ,   help='''which subset to evaluate typically train/val/test'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--local_rank''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''should be passed by distributed.launch'''						)

		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument(
		    '''--num_return_sequences''' ,   type=_UpperCAmelCase ,   default=1 ,   required=_UpperCAmelCase ,   help='''How many sequences to return'''						)
		parser.add_argument(
		    '''--sync_timeout''' ,   type=_UpperCAmelCase ,   default=6_00 ,   required=_UpperCAmelCase ,   help='''How long should master process wait for other processes to finish.''' ,   )
		parser.add_argument('''--src_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument('''--tgt_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--debug''' ,   action='''store_true'''						)
		__UpperCAmelCase    :     Any													=	time.time()
		__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	parser.parse_known_args()
		__UpperCAmelCase    :     List[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if generate_kwargs and args.local_rank <= 0:
				print(f'parsed the following generate kwargs: {generate_kwargs}'						)
		__UpperCAmelCase    :     Union[str, Any]													=	Path(args.save_dir + '''_tmp'''						)
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)  # this handles locking.
		__UpperCAmelCase    :     int													=	list(json_save_dir.glob('''rank_*.json'''						)						)
		if intermediate_files:
				raise ValueError(f'Found files at {json_save_dir} please move or remove them.'						)
				# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
		__UpperCAmelCase    :     List[Any]													=	{}
		if args.src_lang is not None:
				__UpperCAmelCase    :     List[str]													=	args.src_lang
		if args.tgt_lang is not None:
				__UpperCAmelCase    :     List[Any]													=	args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	eval_data_dir(
		    args.data_dir ,   _UpperCAmelCase ,   args.model_name ,   type_path=args.type_path ,   bs=args.bs ,   fpaa=args.fpaa ,   task=args.task ,   local_rank=args.local_rank ,   n_obs=args.n_obs ,   max_source_length=args.max_source_length ,   num_return_sequences=args.num_return_sequences ,   prefix=args.prefix ,   dataset_kwargs=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

		if args.local_rank <= 0:
				__UpperCAmelCase    :     int													=	Path(args.save_dir						)
				save_dir.mkdir(exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	gather_results_from_each_node(_UpperCAmelCase ,   _UpperCAmelCase ,   args.sync_timeout						)
				__UpperCAmelCase    :     List[Any]													=	combine_partial_results(_UpperCAmelCase						)
				if args.num_return_sequences > 1:
						__UpperCAmelCase    :     int													=	save_dir.joinpath('''pseudolabel_results.json'''						)
						print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'						)
						save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
						return
				__UpperCAmelCase    :     str													=	Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     int													=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase						)]

				# Calculate metrics, save metrics,  and save _generations.txt
				__UpperCAmelCase    :     Optional[Any]													=	'''translation''' in args.task
				__UpperCAmelCase    :     Optional[int]													=	calculate_bleu if calc_bleu else calculate_rouge
				__UpperCAmelCase    :     Union[str, Any]													=	'''bleu''' if calc_bleu else '''rouge'''
				__UpperCAmelCase    :     Dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	time.time() - start_time
				__UpperCAmelCase    :     List[str]													=	round(runtime / metrics['''n_obs'''] ,   4						)
				__UpperCAmelCase    :     List[str]													=	num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				__UpperCAmelCase    :     List[Any]													=	save_dir.joinpath(f'{args.type_path}_{metric_name}.json'						)
				save_json(_UpperCAmelCase ,   _UpperCAmelCase ,   indent=_UpperCAmelCase						)
				print(_UpperCAmelCase						)
				write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}_generations.txt'						)						)
				if args.debug:
						write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}.target'						)						)
				else:
						shutil.rmtree(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[]
		for partial_result in partial_results:
				records.extend(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x["id"]						)
		__UpperCAmelCase    :     Union[str, Any]													=	[x['''pred'''] for x in records]
		return preds

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	time.time()
		logger.info('''waiting for all nodes to finish'''						)
		__UpperCAmelCase    :     Any													=	None
		while (time.time() - start_wait) < timeout:
				__UpperCAmelCase    :     List[Any]													=	list(save_dir.glob('''rank_*.json'''						)						)
				if len(_UpperCAmelCase						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						__UpperCAmelCase    :     Union[str, Any]													=	lmap(_UpperCAmelCase ,   _UpperCAmelCase						)
						return json_data
				except JSONDecodeError:
						continue
		else:
				raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
		# Unreachable


if __name__ == "__main__":
				# Usage for MT:
				run_generate()





