'''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,
)


_UpperCAmelCase    :     str         =       getLogger(__name__)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple   ,		__snake_case							:			str   ,		__snake_case							:			str   ,		__snake_case							:			int = 8   ,		__snake_case							:			int = 1_0_2_4   ,		__snake_case							:			List[str]="val"   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			Dict=False   ,		__snake_case							:			List[str]="summarization"   ,		__snake_case							:			int=None   ,		__snake_case							:			int=1   ,		__snake_case							:			Dict = None   ,		__snake_case							:			str=""   ,		**__snake_case							:			Tuple   ,		):
     _A    		=  str(__snake_case				)
     assert local_rank is not None
     torch.distributed.init_process_group(backend='nccl'   ,		rank=__snake_case				)

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

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

     if max_source_length is None:
          _A    		=  tokenizer.model_max_length
     if prefix is None:
          _A    		=  prefix or getattr(model.config   ,		'prefix'   ,		''				) or ''
     _A    		=  SeqaSeqDataset(
         __snake_case   ,		__snake_case   ,		__snake_case   ,		max_target_length=1_0_2_4   ,		type_path=__snake_case   ,		n_obs=__snake_case   ,		prefix=__snake_case   ,		**__snake_case   ,		)
     # 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.
     _A    		=  ds.make_sortish_sampler(__snake_case   ,		distributed=__snake_case   ,		add_extra_examples=__snake_case   ,		shuffle=__snake_case				)
     _A    		=  DataLoader(__snake_case   ,		sampler=__snake_case   ,		batch_size=__snake_case   ,		collate_fn=ds.collate_fn				)
     _A    		=  []
     for batch in tqdm(__snake_case				):
          _A    		=  model.generate(
              input_ids=batch['input_ids'].to(model.device				)   ,		attention_mask=batch['attention_mask'].to(model.device				)   ,		num_return_sequences=__snake_case   ,		num_beams=__snake_case   ,		**__snake_case   ,		)
          _A    		=  tokenizer.batch_decode(__snake_case   ,		skip_special_tokens=__snake_case   ,		clean_up_tokenization_spaces=__snake_case				)
          _A    		=  batch['ids']
          if num_return_sequences > 1:
               _A    		=  chunks(__snake_case   ,		__snake_case				)  # batch size chunks, each of size num_return_seq
          for i, pred in enumerate(__snake_case				):
               results.append({'pred': pred, 'id': ids[i].item()}				)
     save_json(__snake_case   ,		__snake_case				)
     return results, sampler.num_replicas





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  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=__snake_case   ,		help='like cnn_dm/test.source'				)
     parser.add_argument(
         '--model_name'   ,		type=__snake_case   ,		help='like facebook/bart-large-cnn,t5-base, etc.'   ,		default='sshleifer/distilbart-xsum-12-3'   ,		)
     parser.add_argument('--save_dir'   ,		type=__snake_case   ,		help='where to save'   ,		default='tmp_gen'				)
     parser.add_argument('--max_source_length'   ,		type=__snake_case   ,		default=__snake_case				)
     parser.add_argument(
         '--type_path'   ,		type=__snake_case   ,		default='test'   ,		help='which subset to evaluate typically train/val/test'				)
     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(
         '--local_rank'   ,		type=__snake_case   ,		default=-1   ,		required=__snake_case   ,		help='should be passed by distributed.launch'				)

     parser.add_argument(
         '--n_obs'   ,		type=__snake_case   ,		default=__snake_case   ,		required=__snake_case   ,		help='How many observations. Defaults to all.'				)
     parser.add_argument(
         '--num_return_sequences'   ,		type=__snake_case   ,		default=1   ,		required=__snake_case   ,		help='How many sequences to return'				)
     parser.add_argument(
         '--sync_timeout'   ,		type=__snake_case   ,		default=6_0_0   ,		required=__snake_case   ,		help='How long should master process wait for other processes to finish.'   ,		)
     parser.add_argument('--src_lang'   ,		type=__snake_case   ,		default=__snake_case   ,		required=__snake_case				)
     parser.add_argument('--tgt_lang'   ,		type=__snake_case   ,		default=__snake_case   ,		required=__snake_case				)
     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('--fp16'   ,		action='store_true'				)
     parser.add_argument('--debug'   ,		action='store_true'				)
     _A    		=  time.time()
     _A		,       _A    		=  parser.parse_known_args()
     _A    		=  parse_numeric_n_bool_cl_kwargs(__snake_case				)
     if generate_kwargs and args.local_rank <= 0:
          print(F'parsed the following generate kwargs: {generate_kwargs}'				)
     _A    		=  Path(args.save_dir + '_tmp'				)
     Path(__snake_case				).mkdir(exist_ok=__snake_case				)  # this handles locking.
     _A    		=  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.
     _A    		=  {}
     if args.src_lang is not None:
          _A    		=  args.src_lang
     if args.tgt_lang is not None:
          _A    		=  args.tgt_lang

     Path(args.save_dir				).mkdir(exist_ok=__snake_case				)
     _A		,       _A    		=  eval_data_dir(
         args.data_dir   ,		__snake_case   ,		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=__snake_case   ,		**__snake_case   ,		)

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

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





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





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









from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE:     Optional[int]									= logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Any					,							**snake_case__	:   str  ):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								requires_backends(self					,							'vision'  )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )



						def __call__(			self	:   str					,							snake_case__	:   Union[str, List[str], "Image", List["Image"]]					,							**snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								return super().__call__(snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   str					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if "candidate_labels" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['hypothesis_template']

								return preprocess_params, {}, {}



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]="This is a photo of {}."  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor(images=[image]					,							return_tensors=self.framework  )
								SCREAMING_SNAKE_CASE_					      =							candidate_labels
								SCREAMING_SNAKE_CASE_					      =							[hypothesis_template.format(snake_case__  ) for x in candidate_labels]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							return_tensors=self.framework					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[text_inputs]
								return inputs



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('text_inputs'  )
								if isinstance(text_inputs[0]					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0]
								else:
										# Batching case.
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0][0]

								SCREAMING_SNAKE_CASE_					      =							self.model(**snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_outputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_outputs['logits'][0]
								if self.framework == "pt":
										SCREAMING_SNAKE_CASE_					      =							logits.softmax(dim=-1  ).squeeze(-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.tolist()
										if not isinstance(snake_case__					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							[scores]
								elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_					      =							stable_softmax(snake_case__					,							axis=-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.numpy().tolist()
								else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(snake_case__					,							snake_case__  )					,							key=lambda snake_case__  : -x[0]  )
								]
								return result