


"""simple docstring"""







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
						import torch
class 				lowerCamelCase							(      _lowerCamelCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   ["""image_processor""", """tokenizer"""]
							SCREAMING_SNAKE_CASE              =   """OneFormerImageProcessor"""
							SCREAMING_SNAKE_CASE              =   ("""CLIPTokenizer""", """CLIPTokenizerFast""")






							def __init__(    self			:  List[str]		,			lowerCamelCase_			:  int=None		,			lowerCamelCase_			:  str=None		,			lowerCamelCase_			:  Any = 77		,			lowerCamelCase_			:  Any = 77		,			**lowerCamelCase_			:  Optional[int]					):
													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`.'''					)

													UpperCAmelCase__												=     max_seq_length
													UpperCAmelCase__												=     task_seq_length

													super().__init__(lowerCAmelCase_		,			lowerCAmelCase_					)






							def        lowerCAmelCase  (    self			:  List[Any]		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Optional[int]=77					):
													if text_list is None:
																			raise ValueError('''tokens cannot be None.'''					)

													UpperCAmelCase__												=     self.tokenizer(lowerCAmelCase_		,			padding='''max_length'''		,			max_length=lowerCAmelCase_		,			truncation=lowerCAmelCase_					)

													UpperCAmelCase__						,					UpperCAmelCase__												=     tokens['''attention_mask'''], tokens['''input_ids''']

													UpperCAmelCase__												=     []
													for attn_mask, input_id in zip(lowerCAmelCase_		,			lowerCAmelCase_					):
																			UpperCAmelCase__												=     torch.tensor(lowerCAmelCase_					) * torch.tensor(lowerCAmelCase_					)
																			token_inputs.append(token.unsqueeze(0					)					)

													UpperCAmelCase__												=     torch.cat(lowerCAmelCase_		,			dim=0					)
													return token_inputs






							def __call__(    self			:  Any		,			lowerCamelCase_			:  Optional[int]=None		,			lowerCamelCase_			:  Optional[int]=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			**lowerCamelCase_			:  Optional[int]					):

													if task_inputs is None:
																			raise ValueError('''You have to specify the task_input. Found None.'''					)
													elif images is None:
																			raise ValueError('''You have to specify the image. Found None.'''					)

													if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs					):
																			raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''					)

													UpperCAmelCase__												=     self.image_processor(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			**lowerCAmelCase_					)

													if isinstance(lowerCAmelCase_		,			lowerCAmelCase_					):
																			UpperCAmelCase__												=     [task_inputs]

													if isinstance(lowerCAmelCase_		,			lowerCAmelCase_					) and all(isinstance(lowerCAmelCase_		,			lowerCAmelCase_					) for task_input in task_inputs					):
																			UpperCAmelCase__												=     []
																			for task in task_inputs:
																									UpperCAmelCase__												=     f"""the task is {task}"""
																									task_token_inputs.append(lowerCAmelCase_					)
																			UpperCAmelCase__												=     self._preprocess_text(lowerCAmelCase_		,			max_length=self.task_seq_length					)
													else:
																			raise TypeError('''Task Inputs should be a string or a list of strings.'''					)

													if hasattr(lowerCAmelCase_		,			'''text_inputs'''					):
																			UpperCAmelCase__												=     encoded_inputs.text_inputs

																			UpperCAmelCase__												=     []
																			for texts in texts_list:
																									UpperCAmelCase__												=     self._preprocess_text(lowerCAmelCase_		,			max_length=self.max_seq_length					)
																									text_inputs.append(text_input_list.unsqueeze(0					)					)

																			UpperCAmelCase__												=     torch.cat(lowerCAmelCase_		,			dim=0					)

													return encoded_inputs






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  Optional[Any]=None		,			lowerCamelCase_			:  List[Any]=None		,			lowerCamelCase_			:  Optional[int]=None		,			**lowerCamelCase_			:  Dict					):

													if task_inputs is None:
																			raise ValueError('''You have to specify the task_input. Found None.'''					)
													elif images is None:
																			raise ValueError('''You have to specify the image. Found None.'''					)

													if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs					):
																			raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''					)

													UpperCAmelCase__												=     self.image_processor.encode_inputs(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			**lowerCAmelCase_					)

													if isinstance(lowerCAmelCase_		,			lowerCAmelCase_					):
																			UpperCAmelCase__												=     [task_inputs]

													if isinstance(lowerCAmelCase_		,			lowerCAmelCase_					) and all(isinstance(lowerCAmelCase_		,			lowerCAmelCase_					) for task_input in task_inputs					):
																			UpperCAmelCase__												=     []
																			for task in task_inputs:
																									UpperCAmelCase__												=     f"""the task is {task}"""
																									task_token_inputs.append(lowerCAmelCase_					)
																			UpperCAmelCase__												=     self._preprocess_text(lowerCAmelCase_		,			max_length=self.task_seq_length					)
													else:
																			raise TypeError('''Task Inputs should be a string or a list of strings.'''					)

													if hasattr(lowerCAmelCase_		,			'''text_inputs'''					):
																			UpperCAmelCase__												=     encoded_inputs.text_inputs

																			UpperCAmelCase__												=     []
																			for texts in texts_list:
																									UpperCAmelCase__												=     self._preprocess_text(lowerCAmelCase_		,			max_length=self.max_seq_length					)
																									text_inputs.append(text_input_list.unsqueeze(0					)					)

																			UpperCAmelCase__												=     torch.cat(lowerCAmelCase_		,			dim=0					)

													return encoded_inputs






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			*lowerCamelCase_			:  Union[str, Any]		,			**lowerCamelCase_			:  Optional[int]					):
													return self.image_processor.post_process_semantic_segmentation(*lowerCAmelCase_		,			**lowerCAmelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[int]		,			*lowerCamelCase_			:  int		,			**lowerCamelCase_			:  Optional[int]					):
													return self.image_processor.post_process_instance_segmentation(*lowerCAmelCase_		,			**lowerCAmelCase_					)






							def        lowerCAmelCase  (    self			:  List[Any]		,			*lowerCamelCase_			:  Tuple		,			**lowerCamelCase_			:  List[str]					):
													return self.image_processor.post_process_panoptic_segmentation(*lowerCAmelCase_		,			**lowerCAmelCase_					)






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_   				=  getLogger(__name__)


UpperCAmelCase_   				=  """cuda""" if torch.cuda.is_available() else """cpu"""





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							int = 8							,     UpperCamelCase__      :							str = DEFAULT_DEVICE							,     UpperCamelCase__      :							str=False							,     UpperCamelCase__      :							str="summarization"							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							int							,     )      ->			Dict:



					'''simple docstring'''




					_snake_case						=					Path(UpperCamelCase__    ).open('w'							,     encoding='utf-8'    )
					_snake_case						=					str(UpperCamelCase__    )
					_snake_case						=					AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__    ).to(UpperCamelCase__    )
					if fpaa:
										_snake_case						=					model.half()

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

					_snake_case						=					time.time()
					# update config with task specific params
					use_task_specific_params(UpperCamelCase__							,     UpperCamelCase__    )
					if prefix is None:
										_snake_case						=					prefix or getattr(model.config							,     'prefix'							,     ''    ) or ''
					for examples_chunk in tqdm(list(chunks(UpperCamelCase__							,     UpperCamelCase__    )    )    ):
										_snake_case						=					[prefix + text for text in examples_chunk]
										_snake_case						=					tokenizer(UpperCamelCase__							,     return_tensors='pt'							,     truncation=UpperCamelCase__							,     padding='longest'    ).to(UpperCamelCase__    )
										_snake_case						=					model.generate(
										    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **UpperCamelCase__							,     )
										_snake_case						=					tokenizer.batch_decode(UpperCamelCase__							,     skip_special_tokens=UpperCamelCase__							,     clean_up_tokenization_spaces=UpperCamelCase__    )
										for hypothesis in dec:
															fout.write(hypothesis + '\n'    )
															fout.flush()
					fout.close()
					_snake_case						=					int(time.time() - start_time    )  # seconds
					_snake_case						=					len(UpperCamelCase__    )
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4    )}





def     lowerCamelCase__  (  )      ->			Dict:



					'''simple docstring'''




					return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]=True    )      ->			List[str]:



					'''simple docstring'''




					_snake_case						=					argparse.ArgumentParser()
					parser.add_argument('model_name'							,     type=UpperCamelCase__							,     help='like facebook/bart-large-cnn,t5-base, etc.'    )
					parser.add_argument('input_path'							,     type=UpperCamelCase__							,     help='like cnn_dm/test.source'    )
					parser.add_argument('save_path'							,     type=UpperCamelCase__							,     help='where to save summaries'    )
					parser.add_argument('--reference_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='like cnn_dm/test.target'    )
					parser.add_argument('--score_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default='metrics.json'							,     help='where to save metrics'    )
					parser.add_argument('--device'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='cuda, cuda:1, cpu etc.'    )
					parser.add_argument(
					    '--prefix'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='will be added to the begininng of src examples'    )
					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(
					    '--n_obs'							,     type=UpperCamelCase__							,     default=-1							,     required=UpperCamelCase__							,     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=UpperCamelCase__							,     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
					_snake_case				,  _snake_case						=					parser.parse_known_args()
					_snake_case						=					parse_numeric_n_bool_cl_kwargs(UpperCamelCase__    )
					if parsed_args and verbose:
										print(F'''parsed the following generate kwargs: {parsed_args}'''    )
					with open(args.input_path    ) as f:
										_snake_case						=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										_snake_case						=					examples[: args.n_obs]
					Path(args.save_path    ).parent.mkdir(exist_ok=UpperCamelCase__    )
					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.'''    )
					_snake_case						=					generate_summaries_or_translations(
					    UpperCamelCase__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **UpperCamelCase__							,     )

					if args.reference_path is None:
										return {}

					# Compute scores
					_snake_case						=					calculate_bleu if 'translation' in args.task else calculate_rouge
					_snake_case						=					[x.rstrip() for x in open(args.save_path    ).readlines()]
					_snake_case						=					[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(UpperCamelCase__    )]
					_snake_case						=					score_fn(UpperCamelCase__							,     UpperCamelCase__    )
					scores.update(UpperCamelCase__    )

					if args.dump_args:
										scores.update(UpperCamelCase__    )
					if args.info:
										_snake_case						=					args.info

					if verbose:
										print(UpperCamelCase__    )

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



