


'''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				 =	getLogger(__name__)


def      a (     __a					,   __a					,   __a					,   __a = 8					,   __a = 1024					,   __a="val"					,   __a=None					,   __a=False					,   __a="summarization"					,   __a=None					,   __a=1					,   __a = None					,   __a=""					,   **__a					,   )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					str(__a  )
					assert local_rank is not None
					torch.distributed.init_process_group(backend='''nccl'''					,   rank=__a  )

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

					UpperCamelCase__  :Union[str, Any]				   =					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:
										UpperCamelCase__  :List[str]				   =					tokenizer.model_max_length
					if prefix is None:
										UpperCamelCase__  :str				   =					prefix or getattr(model.config					,   '''prefix'''					,   ''''''  ) or ''''''
					UpperCamelCase__  :Optional[Any]				   =					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.
					UpperCamelCase__  :Optional[int]				   =					ds.make_sortish_sampler(__a					,   distributed=__a					,   add_extra_examples=__a					,   shuffle=__a  )
					UpperCamelCase__  :int				   =					DataLoader(__a					,   sampler=__a					,   batch_size=__a					,   collate_fn=ds.collate_fn  )
					UpperCamelCase__  :Union[str, Any]				   =					[]
					for batch in tqdm(__a  ):
										UpperCamelCase__  :str				   =					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					,   )
										UpperCamelCase__  :int				   =					tokenizer.batch_decode(__a					,   skip_special_tokens=__a					,   clean_up_tokenization_spaces=__a  )
										UpperCamelCase__  :int				   =					batch['''ids''']
										if num_return_sequences > 1:
															UpperCamelCase__  :Optional[int]				   =					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      a (     )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					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'''  )
					UpperCamelCase__  :Optional[int]				   =					time.time()
					UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					parser.parse_known_args()
					UpperCamelCase__  :Any				   =					parse_numeric_n_bool_cl_kwargs(__a  )
					if generate_kwargs and args.local_rank <= 0:
										print(f'''parsed the following generate kwargs: {generate_kwargs}'''  )
					UpperCamelCase__  :List[str]				   =					Path(args.save_dir + '''_tmp'''  )
					Path(__a  ).mkdir(exist_ok=__a  )  # this handles locking.
					UpperCamelCase__  :Any				   =					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__  :Union[str, Any]				   =					{}
					if args.src_lang is not None:
										UpperCamelCase__  :Optional[Any]				   =					args.src_lang
					if args.tgt_lang is not None:
										UpperCamelCase__  :List[Any]				   =					args.tgt_lang

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

										# Calculate metrics, save metrics,  and save _generations.txt
										UpperCamelCase__  :Optional[Any]				   =					'''translation''' in args.task
										UpperCamelCase__  :str				   =					calculate_bleu if calc_bleu else calculate_rouge
										UpperCamelCase__  :Tuple				   =					'''bleu''' if calc_bleu else '''rouge'''
										UpperCamelCase__  :Dict				   =					score_fn(__a					,   __a  )
										UpperCamelCase__  :str				   =					len(__a  )
										UpperCamelCase__  :Tuple				   =					time.time() - start_time
										UpperCamelCase__  :List[str]				   =					round(runtime / metrics['''n_obs''']					,   4  )
										UpperCamelCase__  :Union[str, Any]				   =					num_replicas
										# TODO(@stas00): add whatever metadata to metrics
										UpperCamelCase__  :int				   =					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      a (     __a  )    ->      List:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					[]
					for partial_result in partial_results:
										records.extend(__a  )
					UpperCamelCase__  :Optional[int]				   =					sorted(__a					,   key=lambda __a  : x["id"]  )
					UpperCamelCase__  :str				   =					[x['''pred'''] for x in records]
					return preds


def      a (     __a					,   __a					,   __a  )    ->      List[Dict[str, List]]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					time.time()
					logger.info('''waiting for all nodes to finish'''  )
					UpperCamelCase__  :Dict				   =					None
					while (time.time() - start_wait) < timeout:
										UpperCamelCase__  :Dict				   =					list(save_dir.glob('''rank_*.json'''  )  )
										if len(__a  ) < num_replicas:
															continue
										try:
															# make sure all json files are fully saved
															UpperCamelCase__  :Optional[int]				   =					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()



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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