
"""simple docstring"""




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


A__ :       int				= getLogger(__name__)


A__ :       Optional[int]				= 'cuda' if torch.cuda.is_available() else 'cpu'




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = 8			,					_UpperCamelCase = DEFAULT_DEVICE			,					_UpperCamelCase=False			,					_UpperCamelCase="summarization"			,					_UpperCamelCase=None			,					**_UpperCamelCase			,					):






  """simple docstring"""



  _lowercase:      Dict   			=  Path(_UpperCamelCase    ).open('''w'''			,					encoding='''utf-8'''    )
  _lowercase:      Optional[int]   			=  str(_UpperCamelCase    )
  _lowercase:      Dict   			=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase    ).to(_UpperCamelCase    )
  if fpaa:
    _lowercase:      Tuple   			=  model.half()

  _lowercase:      int   			=  AutoTokenizer.from_pretrained(_UpperCamelCase    )
  logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''    )  # if this is wrong, check config.model_type.

  _lowercase:      int   			=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCamelCase			,					_UpperCamelCase    )
  if prefix is None:
    _lowercase:      List[str]   			=  prefix or getattr(model.config			,					'''prefix'''			,					''''''    ) or ''''''
  for examples_chunk in tqdm(list(chunks(_UpperCamelCase			,					_UpperCamelCase    )    )    ):
    _lowercase:      Union[str, Any]   			=  [prefix + text for text in examples_chunk]
    _lowercase:      Any   			=  tokenizer(_UpperCamelCase			,					return_tensors='''pt'''			,					truncation=_UpperCamelCase			,					padding='''longest'''    ).to(_UpperCamelCase    )
    _lowercase:      str   			=  model.generate(
        input_ids=batch.input_ids			,					attention_mask=batch.attention_mask			,					**_UpperCamelCase			,					)
    _lowercase:      Dict   			=  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()
  _lowercase:      Optional[Any]   			=  int(time.time() - start_time    )  # seconds
  _lowercase:      Tuple   			=  len(_UpperCamelCase    )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,					4    )}




def 			_lowerCAmelCase		(		):






  """simple docstring"""



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




def 			_lowerCAmelCase		(		_UpperCamelCase=True    ):






  """simple docstring"""



  _lowercase:      int   			=  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
  _lowercase			,					_lowercase:      Dict   			=  parser.parse_known_args()
  _lowercase:      int   			=  parse_numeric_n_bool_cl_kwargs(_UpperCamelCase    )
  if parsed_args and verbose:
    print(f'''parsed the following generate kwargs: {parsed_args}'''    )
  _lowercase:      Optional[Any]   			=  [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
  if args.n_obs > 0:
    _lowercase:      Optional[Any]   			=  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.'''    )

  if args.device == "cpu" and args.fpaa:
    # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
    raise ValueError('''Can\'t mix --fp16 and --device cpu'''    )

  _lowercase:      List[str]   			=  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
  _lowercase:      Any   			=  calculate_bleu if '''translation''' in args.task else calculate_rouge
  _lowercase:      List[str]   			=  [x.rstrip() for x in open(args.save_path    ).readlines()]
  _lowercase:      Any   			=  [x.rstrip() for x in open(args.reference_path    ).readlines()][: len(_UpperCamelCase    )]
  _lowercase:      dict   			=  score_fn(_UpperCamelCase			,					_UpperCamelCase    )
  scores.update(_UpperCamelCase    )

  if args.dump_args:
    scores.update(_UpperCamelCase    )
  if args.info:
    _lowercase:      Optional[int]   			=  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)


"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DonutImageProcessor


class       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=18			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=False			,					A_=True			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_thumbnail'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_align_long_axis'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_pad'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=42							)
         self.assertEqual(image_processor.size			,					{'''height''': 42, '''width''': 42}							)

         # Previous config had dimensions in (width, height) order
         _lowercase:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      Dict   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					Image.Image							)

         # Test not batched input
         _lowercase:      Tuple   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      List[Any]   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
       @is_flaky()
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					numpify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					np.ndarray							)

         # Test not batched input
         _lowercase:      Union[str, Any]   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      Dict   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)



       @is_flaky()
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					torchify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					torch.Tensor							)

         # Test not batched input
         _lowercase:      Union[str, Any]   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)

         # Test batched
         _lowercase:      Dict   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
