




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


__lowerCAmelCase		:    Any	      =		getLogger(__name__)


__lowerCAmelCase		:    Optional[Any]	      =		'cuda' if torch.cuda.is_available() else 'cpu'




def       __magic_name__    (			A		: List[str],  A		: str,  A		: str,  A		: int = 8,  A		: str = DEFAULT_DEVICE,  A		: List[str]=False,  A		: Tuple="summarization",  A		: int=None,  **A		: Any,  ):
			a   =      Path(A       ).open("w",  encoding="utf-8"       )
			a   =      str(A       )
			a   =      AutoModelForSeqaSeqLM.from_pretrained(A       ).to(A       )
			if fpaa:
						a   =      model.half()

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

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




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




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

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(A       )
			if args.info:
						a   =      args.info

			if verbose:
						print(A       )

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












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      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__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






