




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


__SCREAMING_SNAKE_CASE     : List[str]      =getLogger(__name__)


__SCREAMING_SNAKE_CASE     : int      ="""cuda""" if torch.cuda.is_available() else """cpu"""

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							str  ,					lowerCamelCase:							str  ,					lowerCamelCase:							int = 8  ,					lowerCamelCase:							str = DEFAULT_DEVICE  ,					lowerCamelCase:							Dict=False  ,					lowerCamelCase:							Tuple="summarization"  ,					lowerCamelCase:							Any=None  ,					**lowerCamelCase:							List[str]  ,					)       ->      Dict:


      '''simple docstring'''




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

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

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

def       lowerCAmelCase_      (	)       ->      List[str]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							Any=True    )       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					argparse.ArgumentParser()
      parser.add_argument('model_name'  ,					type=lowerCamelCase  ,					help='like facebook/bart-large-cnn,t5-base, etc.'    )
      parser.add_argument('input_path'  ,					type=lowerCamelCase  ,					help='like cnn_dm/test.source'    )
      parser.add_argument('save_path'  ,					type=lowerCamelCase  ,					help='where to save summaries'    )
      parser.add_argument('--reference_path'  ,					type=lowerCamelCase  ,					required=lowerCamelCase  ,					help='like cnn_dm/test.target'    )
      parser.add_argument('--score_path'  ,					type=lowerCamelCase  ,					required=lowerCamelCase  ,					default='metrics.json'  ,					help='where to save metrics'    )
      parser.add_argument('--device'  ,					type=lowerCamelCase  ,					required=lowerCamelCase  ,					default=lowerCamelCase  ,					help='cuda, cuda:1, cpu etc.'    )
      parser.add_argument(
          '--prefix'  ,					type=lowerCamelCase  ,					required=lowerCamelCase  ,					default=lowerCamelCase  ,					help='will be added to the begininng of src examples'    )
      parser.add_argument('--task'  ,					type=lowerCamelCase  ,					default='summarization'  ,					help='used for task_specific_params + metrics'    )
      parser.add_argument('--bs'  ,					type=lowerCamelCase  ,					default=8  ,					required=lowerCamelCase  ,					help='batch size'    )
      parser.add_argument(
          '--n_obs'  ,					type=lowerCamelCase  ,					default=-1  ,					required=lowerCamelCase  ,					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=lowerCamelCase  ,					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
      lowerCamelCase__	,		lowerCamelCase__   	=					parser.parse_known_args()
      lowerCamelCase__   	=					parse_numeric_n_bool_cl_kwargs(lowerCamelCase    )
      if parsed_args and verbose:
            print(f"""parsed the following generate kwargs: {parsed_args}"""    )
      with open(args.input_path    ) as f:
            lowerCamelCase__   	=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
            lowerCamelCase__   	=					examples[: args.n_obs]
      Path(args.save_path    ).parent.mkdir(exist_ok=lowerCamelCase    )
      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."""    )
      lowerCamelCase__   	=					generate_summaries_or_translations(
          lowerCamelCase  ,					args.save_path  ,					args.model_name  ,					batch_size=args.bs  ,					device=args.device  ,					fpaa=args.fpaa  ,					task=args.task  ,					prefix=args.prefix  ,					**lowerCamelCase  ,					)

      if args.reference_path is None:
            return {}

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

      if args.dump_args:
            scores.update(lowerCamelCase    )
      if args.info:
            lowerCamelCase__   	=					args.info

      if verbose:
            print(lowerCamelCase    )

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





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE     : Dict      =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						Any  =["pixel_values"]




     def __init__(     self	: Union[str, Any]						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: bool = True						,	A__	: Union[int, float] = 1 / 2_5_5						,	A__	: bool = True						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = True						,	**A__	: Optional[int]						,	)					->							None:
           super().__init__(**A__      )
           lowerCamelCase__   	=					size if size is not None else {'shortest_edge': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__      )

           lowerCamelCase__   	=					do_resize
           lowerCamelCase__   	=					size
           lowerCamelCase__   	=					resample
           lowerCamelCase__   	=					do_center_crop
           lowerCamelCase__   	=					crop_size
           lowerCamelCase__   	=					do_rescale
           lowerCamelCase__   	=					rescale_factor
           lowerCamelCase__   	=					do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCamelCase__   	=					image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCamelCase__   	=					do_convert_rgb




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Optional[int]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					get_resize_output_image_size(
               A__						,	size=(size['height'], size['width'])						,	default_to_square=A__      )
           return resize(A__						,	size=A__						,	resample=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__      )
           return center_crop(A__						,	size=(size['height'], size['width'])						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Dict						,	A__	: np.ndarray						,	A__	: Union[int, float]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							Tuple:
           return rescale(A__						,	scale=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Union[float, List[float]]						,	A__	: Union[float, List[float]]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Tuple						,	)					->							np.ndarray:
           return normalize(A__						,	mean=A__						,	std=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Tuple						,	A__	: ImageInput						,	A__	: bool = None						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = None						,	A__	: bool = None						,	A__	: int = None						,	A__	: bool = None						,	A__	: float = None						,	A__	: bool = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = None						,	A__	: Optional[Union[str, TensorType]] = None						,	A__	: Optional[ChannelDimension] = ChannelDimension.FIRST						,	**A__	: List[str]						,	)					->							PIL.Image.Image:
           lowerCamelCase__   	=					do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__   	=					size if size is not None else self.size
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					resample if resample is not None else self.resample
           lowerCamelCase__   	=					do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase__   	=					crop_size if crop_size is not None else self.crop_size
           lowerCamelCase__   	=					get_size_dict(A__      )
           lowerCamelCase__   	=					do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__   	=					rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__   	=					do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__   	=					image_std if image_std is not None else self.image_std
           lowerCamelCase__   	=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCamelCase__   	=					make_list_of_images(A__      )

           if not valid_images(A__      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

           if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'      )

           if do_center_crop and crop_size is None:
                 raise ValueError('Crop size must be specified if do_center_crop is True.'      )

           if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 lowerCamelCase__   	=					[convert_to_rgb(A__      ) for image in images]

           # All transformations expect numpy arrays.
           lowerCamelCase__   	=					[to_numpy_array(A__      ) for image in images]

           if do_resize:
                 lowerCamelCase__   	=					[self.resize(image=A__						,	size=A__						,	resample=A__      ) for image in images]

           if do_center_crop:
                 lowerCamelCase__   	=					[self.center_crop(image=A__						,	size=A__      ) for image in images]

           if do_rescale:
                 lowerCamelCase__   	=					[self.rescale(image=A__						,	scale=A__      ) for image in images]

           if do_normalize:
                 lowerCamelCase__   	=					[self.normalize(image=A__						,	mean=A__						,	std=A__      ) for image in images]

           lowerCamelCase__   	=					[to_channel_dimension_format(A__						,	A__      ) for image in images]

           lowerCamelCase__   	=					{'pixel_values': images}
           return BatchFeature(data=A__						,	tensor_type=A__      )