


'''simple docstring'''





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,
)


snake_case_						:			Tuple													=							getLogger(__name__)







def 						lowerCamelCase(   a__	,a__	,a__	,a__ = 8	,a__ = 1024	,a__="val"	,a__=None	,a__=False	,a__="summarization"	,a__=None	,a__=1	,a__ = None	,a__=""	,**a__	,):
				_SCREAMING_SNAKE_CASE			  =str(a__)
				assert local_rank is not None
				torch.distributed.init_process_group(backend='''nccl'''	,rank=a__)

				_SCREAMING_SNAKE_CASE			  =Path(a__)
				_SCREAMING_SNAKE_CASE			  =save_dir.joinpath(f"rank_{local_rank}_output.json")
				torch.cuda.set_device(a__)
				_SCREAMING_SNAKE_CASE			  =AutoModelForSeqaSeqLM.from_pretrained(a__).cuda()
				if fpaa:
								_SCREAMING_SNAKE_CASE			  =model.half()
				# determine if we need to increase num_beams
				use_task_specific_params(a__	,a__)  # update config with task specific params
				_SCREAMING_SNAKE_CASE			  =generate_kwargs.pop('''num_beams'''	,model.config.num_beams)  # AttributeError risk?
				if num_return_sequences > num_beams:
								_SCREAMING_SNAKE_CASE			  =num_return_sequences

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

				if max_source_length is None:
								_SCREAMING_SNAKE_CASE			  =tokenizer.model_max_length
				if prefix is None:
								_SCREAMING_SNAKE_CASE			  =prefix or getattr(model.config	,'''prefix'''	,'''''') or ''''''
				_SCREAMING_SNAKE_CASE			  =SeqaSeqDataset(
				    a__	,a__	,a__	,max_target_length=1024	,type_path=a__	,n_obs=a__	,prefix=a__	,**a__	,)
				# 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.
				_SCREAMING_SNAKE_CASE			  =ds.make_sortish_sampler(a__	,distributed=a__	,add_extra_examples=a__	,shuffle=a__)
				_SCREAMING_SNAKE_CASE			  =DataLoader(a__	,sampler=a__	,batch_size=a__	,collate_fn=ds.collate_fn)
				_SCREAMING_SNAKE_CASE			  =[]
				for batch in tqdm(a__):
								_SCREAMING_SNAKE_CASE			  =model.generate(
								    input_ids=batch['''input_ids'''].to(model.device)	,attention_mask=batch['''attention_mask'''].to(model.device)	,num_return_sequences=a__	,num_beams=a__	,**a__	,)
								_SCREAMING_SNAKE_CASE			  =tokenizer.batch_decode(a__	,skip_special_tokens=a__	,clean_up_tokenization_spaces=a__)
								_SCREAMING_SNAKE_CASE			  =batch['''ids''']
								if num_return_sequences > 1:
												_SCREAMING_SNAKE_CASE			  =chunks(a__	,a__)  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(a__):
												results.append({'''pred''': pred, '''id''': ids[i].item()})
				save_json(a__	,a__)
				return results, sampler.num_replicas







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =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=a__	,help='''like cnn_dm/test.source''')
				parser.add_argument(
				    '''--model_name'''	,type=a__	,help='''like facebook/bart-large-cnn,t5-base, etc.'''	,default='''sshleifer/distilbart-xsum-12-3'''	,)
				parser.add_argument('''--save_dir'''	,type=a__	,help='''where to save'''	,default='''tmp_gen''')
				parser.add_argument('''--max_source_length'''	,type=a__	,default=a__)
				parser.add_argument(
				    '''--type_path'''	,type=a__	,default='''test'''	,help='''which subset to evaluate typically train/val/test''')
				parser.add_argument('''--task'''	,type=a__	,default='''summarization'''	,help='''used for task_specific_params + metrics''')
				parser.add_argument('''--bs'''	,type=a__	,default=8	,required=a__	,help='''batch size''')
				parser.add_argument(
				    '''--local_rank'''	,type=a__	,default=-1	,required=a__	,help='''should be passed by distributed.launch''')

				parser.add_argument(
				    '''--n_obs'''	,type=a__	,default=a__	,required=a__	,help='''How many observations. Defaults to all.''')
				parser.add_argument(
				    '''--num_return_sequences'''	,type=a__	,default=1	,required=a__	,help='''How many sequences to return''')
				parser.add_argument(
				    '''--sync_timeout'''	,type=a__	,default=600	,required=a__	,help='''How long should master process wait for other processes to finish.'''	,)
				parser.add_argument('''--src_lang'''	,type=a__	,default=a__	,required=a__)
				parser.add_argument('''--tgt_lang'''	,type=a__	,default=a__	,required=a__)
				parser.add_argument(
				    '''--prefix'''	,type=a__	,required=a__	,default=a__	,help='''will be added to the begininng of src examples''')
				parser.add_argument('''--fp16'''	,action='''store_true''')
				parser.add_argument('''--debug'''	,action='''store_true''')
				_SCREAMING_SNAKE_CASE			  =time.time()
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =parser.parse_known_args()
				_SCREAMING_SNAKE_CASE			  =parse_numeric_n_bool_cl_kwargs(a__)
				if generate_kwargs and args.local_rank <= 0:
								print(f"parsed the following generate kwargs: {generate_kwargs}")
				_SCREAMING_SNAKE_CASE			  =Path(args.save_dir + '''_tmp''')
				Path(a__).mkdir(exist_ok=a__)  # this handles locking.
				_SCREAMING_SNAKE_CASE			  =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.
				_SCREAMING_SNAKE_CASE			  ={}
				if args.src_lang is not None:
								_SCREAMING_SNAKE_CASE			  =args.src_lang
				if args.tgt_lang is not None:
								_SCREAMING_SNAKE_CASE			  =args.tgt_lang

				Path(args.save_dir).mkdir(exist_ok=a__)
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =eval_data_dir(
				    args.data_dir	,a__	,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=a__	,**a__	,)

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

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







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =[]
				for partial_result in partial_results:
								records.extend(a__)
				_SCREAMING_SNAKE_CASE			  =sorted(a__	,key=lambda a__: x["id"])
				_SCREAMING_SNAKE_CASE			  =[x['''pred'''] for x in records]
				return preds







def 						lowerCamelCase(   a__	,a__	,a__):
				# WAIT FOR lots of .json files
				_SCREAMING_SNAKE_CASE			  =time.time()
				logger.info('''waiting for all nodes to finish''')
				_SCREAMING_SNAKE_CASE			  =None
				while (time.time() - start_wait) < timeout:
								_SCREAMING_SNAKE_CASE			  =list(save_dir.glob('''rank_*.json'''))
								if len(a__) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												_SCREAMING_SNAKE_CASE			  =lmap(a__	,a__)
												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()



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.'''				,    _a				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError('''You need to specify an `image_processor`.'''  )
										if tokenizer is None:
														raise ValueError('''You need to specify a `tokenizer`.'''  )

										super().__init__(_a				,    _a  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										if text is None and visual_prompt is None and images is None:
														raise ValueError('''You have to specify either text, visual prompt or images.'''  )

										if text is not None and visual_prompt is not None:
														raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

										if text is not None:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''pixel_values''': image_features.pixel_values,
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




						def 							__UpperCamelCase				(       self					:      Any				,    *_a					:      str				,    **_a					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										return self.tokenizer.batch_decode(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										return self.tokenizer.decode(*_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    _a				,    )
										return self.image_processor_class




						@property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    _a				,    )
										return self.image_processor