





"""simple docstring"""




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor

class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=1_8 ,				snake_case_=3_0 ,				snake_case_=4_0_0 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=False ,				snake_case_=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,				snake_case_=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,				snake_case_=True ,				):

        """simple docstring"""

        A_  :	List[Any]    						=  size if size is not None else {'height': 2_0, 'width': 2_0}
        A_  :	Tuple    						=  parent
        A_  :	Tuple    						=  batch_size
        A_  :	List[Any]    						=  num_channels
        A_  :	List[str]    						=  image_size
        A_  :	Tuple    						=  min_resolution
        A_  :	Dict    						=  max_resolution
        A_  :	str    						=  do_resize
        A_  :	Dict    						=  size
        A_  :	Any    						=  do_normalize
        A_  :	List[Any]    						=  image_mean
        A_  :	Tuple    						=  image_std
        A_  :	Any    						=  do_pad
        A_  :	Dict    						=  do_convert_rgb

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }

      def  lowerCamelCase_      (					self ,				snake_case_=False ,				snake_case_=False ,				snake_case_=False  ):

        """simple docstring"""

        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          A_  :	int    						=  []
          for i in range(self.batch_size  ):
            image_inputs.append(
                np.random.randint(
                    2_5_5 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta  )  )
        else:
          A_  :	Optional[Any]    						=  []
          for i in range(self.batch_size  ):
            A_						,	A_  :	Tuple    						=  np.random.choice(np.arange(self.min_resolution ,				self.max_resolution  ) ,				2  )
            image_inputs.append(np.random.randint(2_5_5 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta  )  )

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          A_  :	Optional[Any]    						=  [Image.fromarray(np.moveaxis(snake_case_ ,				0 ,				-1  )  ) for x in image_inputs]

        if torchify:
          A_  :	Any    						=  [torch.from_numpy(snake_case_  ) for x in image_inputs]

        return image_inputs


@require_torch
@require_vision
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			str        =       BlipImageProcessor if is_vision_available() else None

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[str]    						=  BlipImageProcessingTester(self  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case_ ,				'do_resize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'size'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_normalize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_mean'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_std'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_convert_rgb'  )  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_  :	Union[str, Any]    						=  self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				Image.Image  )

        # Test not batched input
        A_  :	Tuple    						=  image_processor(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
        A_  :	Optional[Any]    						=  image_processor(snake_case_ ,				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'],
            ) ,				)

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_  :	Tuple    						=  self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_ ,				numpify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				np.ndarray  )

        # Test not batched input
        A_  :	str    						=  image_processor(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
        A_  :	str    						=  image_processor(snake_case_ ,				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'],
            ) ,				)

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        # Test not batched input
        A_  :	int    						=  image_processor(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
        A_  :	List[str]    						=  image_processor(snake_case_ ,				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'],
            ) ,				)


@require_torch
@require_vision
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			List[str]        =       BlipImageProcessor if is_vision_available() else None

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  BlipImageProcessingTester(self ,				num_channels=4  )
        A_  :	Union[str, Any]    						=  3

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case_ ,				'do_resize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'size'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_normalize'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_mean'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'image_std'  )  )
        self.assertTrue(hasattr(snake_case_ ,				'do_convert_rgb'  )  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_  :	Optional[Any]    						=  self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_ ,				Image.Image  )

        # Test not batched input
        A_  :	Dict    						=  image_processor(image_inputs[0] ,				return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)

        # Test batched
        A_  :	List[str]    						=  image_processor(snake_case_ ,				return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)






"""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


lowerCamelCase_					:						List[str]      = getLogger(__name__)


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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

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

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





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	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
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  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:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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.""" )
  A_  :	Optional[Any]    						=  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
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  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)