

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	Dict,lowercase_			:	int,lowercase_			:	Union[str, Any]=7,lowercase_			:	Dict=3,lowercase_			:	List[Any]=3_0,lowercase_			:	Tuple=4_0_0,lowercase_			:	Any=True,lowercase_			:	int=None,lowercase_			:	List[str]=True,lowercase_			:	int=[0.5, 0.5, 0.5],lowercase_			:	Dict=[0.5, 0.5, 0.5],lowercase_			:	Dict=True,lowercase_			:	Tuple=1 / 2_5_5,lowercase_			:	Optional[int]=True,)->   Union[str, Any]:





           '''simple docstring'''







           A__   				=       size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
           A__   				=       parent
           A__   				=       batch_size
           A__   				=       num_channels
           A__   				=       min_resolution
           A__   				=       max_resolution
           A__   				=       do_resize
           A__   				=       size
           A__   				=       do_normalize
           A__   				=       image_mean
           A__   				=       image_std
           A__   				=       do_rescale
           A__   				=       rescale_factor
           A__   				=       do_pad


    def 						snake_case__					(				self			:	List[str]		)->   Optional[Any]:





           '''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_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }


    def 						snake_case__					(				self			:	Any,lowercase_			:	Optional[int],lowercase_			:	Optional[Any]=False		)->   List[str]:





           '''simple docstring'''







           if not batched:
                  A__   				=       image_inputs[0]
                  if isinstance(lowercase_,Image.Image		):
                         A__				,						A__   				=       image.size
                  else:
                         A__				,						A__   				=       image.shape[1], image.shape[2]
                  if w < h:
                         A__   				=       int(self.size['shortest_edge'] * h / w		)
                         A__   				=       self.size['shortest_edge']
                  elif w > h:
                         A__   				=       self.size['shortest_edge']
                         A__   				=       int(self.size['shortest_edge'] * w / h		)
                  else:
                         A__   				=       self.size['shortest_edge']
                         A__   				=       self.size['shortest_edge']

           else:
                  A__   				=       []
                  for image in image_inputs:
                         A__				,						A__   				=       self.get_expected_values([image]		)
                         expected_values.append((expected_height, expected_width)		)
                  A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
                  A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

           return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							__snake_case   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							ConditionalDetrImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	Tuple		)->   List[str]:





           '''simple docstring'''







           A__   				=       ConditionalDetrImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Union[str, Any]		)->   int:





           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Tuple		)->   Any:





           '''simple docstring'''







           A__   				=       self.image_processing_class(**self.image_processor_dict		)
           self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
           self.assertTrue(hasattr(lowercase_,'image_std'		)		)
           self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
           self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
           self.assertTrue(hasattr(lowercase_,'size'		)		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   int:





           '''simple docstring'''







           A__   				=       self.image_processing_class.from_dict(self.image_processor_dict		)
           self.assertEqual(image_processor.size,{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}		)
           self.assertEqual(image_processor.do_pad,lowercase_		)

           A__   				=       self.image_processing_class.from_dict(
               self.image_processor_dict,size=4_2,max_size=8_4,pad_and_return_pixel_mask=lowercase_		)
           self.assertEqual(image_processor.size,{'shortest_edge': 4_2, 'longest_edge': 8_4}		)
           self.assertEqual(image_processor.do_pad,lowercase_		)


    def 						snake_case__					(				self			:	int		)->   List[str]:





           '''simple docstring'''







           pass


    def 						snake_case__					(				self			:	str		)->   Any:





           '''simple docstring'''







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

           # Test not batched input
           A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)

           self.assertEqual(
               encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

           # Test batched
           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)

           A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values
           self.assertEqual(
               encoded_images.shape,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),)


    def 						snake_case__					(				self			:	Any		)->   Tuple:





           '''simple docstring'''







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

           # Test not batched input
           A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)

           self.assertEqual(
               encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

           # Test batched
           A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)

           self.assertEqual(
               encoded_images.shape,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),)


    def 						snake_case__					(				self			:	Tuple		)->   Optional[Any]:





           '''simple docstring'''







           A__   				=       self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
           for image in image_inputs:
                  self.assertIsInstance(lowercase_,torch.Tensor		)

           # Test not batched input
           A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)

           self.assertEqual(
               encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

           # Test batched
           A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

           A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)

           self.assertEqual(
               encoded_images.shape,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),)


    @slow
    def 						snake_case__					(				self			:	Tuple		)->   Any:





           '''simple docstring'''







           A__   				=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
           with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt','r'		) as f:
                  A__   				=       json.loads(f.read()		)

           A__   				=       {'image_id': 3_9_7_6_9, 'annotations': target}

           # encode them
           A__   				=       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'		)
           A__   				=       image_processing(images=lowercase_,annotations=lowercase_,return_tensors='pt'		)

           # verify pixel values
           A__   				=       torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
           self.assertEqual(encoding['pixel_values'].shape,lowercase_		)

           A__   				=       torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],lowercase_,atol=1E-4		)		)

           # verify area
           A__   				=       torch.tensor([5_887.9_600, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],lowercase_		)		)
           # verify boxes
           A__   				=       torch.Size([6, 4]		)
           self.assertEqual(encoding['labels'][0]['boxes'].shape,lowercase_		)
           A__   				=       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],lowercase_,atol=1E-3		)		)
           # verify image_id
           A__   				=       torch.tensor([3_9_7_6_9]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],lowercase_		)		)
           # verify is_crowd
           A__   				=       torch.tensor([0, 0, 0, 0, 0, 0]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],lowercase_		)		)
           # verify class_labels
           A__   				=       torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],lowercase_		)		)
           # verify orig_size
           A__   				=       torch.tensor([4_8_0, 6_4_0]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],lowercase_		)		)
           # verify size
           A__   				=       torch.tensor([8_0_0, 1_0_6_6]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],lowercase_		)		)


    @slow
    def 						snake_case__					(				self			:	str		)->   Any:





           '''simple docstring'''







           A__   				=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
           with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt','r'		) as f:
                  A__   				=       json.loads(f.read()		)

           A__   				=       {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

           A__   				=       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

           # encode them
           A__   				=       ConditionalDetrImageProcessor(format='coco_panoptic'		)
           A__   				=       image_processing(images=lowercase_,annotations=lowercase_,masks_path=lowercase_,return_tensors='pt'		)

           # verify pixel values
           A__   				=       torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
           self.assertEqual(encoding['pixel_values'].shape,lowercase_		)

           A__   				=       torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],lowercase_,atol=1E-4		)		)

           # verify area
           A__   				=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_879.6_562, 7_634.1_147]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],lowercase_		)		)
           # verify boxes
           A__   				=       torch.Size([6, 4]		)
           self.assertEqual(encoding['labels'][0]['boxes'].shape,lowercase_		)
           A__   				=       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],lowercase_,atol=1E-3		)		)
           # verify image_id
           A__   				=       torch.tensor([3_9_7_6_9]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],lowercase_		)		)
           # verify is_crowd
           A__   				=       torch.tensor([0, 0, 0, 0, 0, 0]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],lowercase_		)		)
           # verify class_labels
           A__   				=       torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],lowercase_		)		)
           # verify masks
           A__   				=       8_2_2_8_7_3
           self.assertEqual(encoding['labels'][0]['masks'].sum().item(),lowercase_		)
           # verify orig_size
           A__   				=       torch.tensor([4_8_0, 6_4_0]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],lowercase_		)		)
           # verify size
           A__   				=       torch.tensor([8_0_0, 1_0_6_6]		)
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],lowercase_		)		)




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


lowercase_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 8   ,      SCREAMING_SNAKE_CASE__      :     str = DEFAULT_DEVICE   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






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

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





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






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

    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'				)

    A__   				=       generate_summaries_or_translations(
        SCREAMING_SNAKE_CASE__   ,      args.save_path   ,      args.model_name   ,      batch_size=args.bs   ,      device=args.device   ,      fpaa=args.fpaa   ,      task=args.task   ,      prefix=args.prefix   ,      **SCREAMING_SNAKE_CASE__   ,      )

    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(SCREAMING_SNAKE_CASE__				)]
    A__   				=       score_fn(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    scores.update(SCREAMING_SNAKE_CASE__				)

    if args.dump_args:
        scores.update(SCREAMING_SNAKE_CASE__				)
    if args.info:
        A__   				=       args.info

    if verbose:
        print(SCREAMING_SNAKE_CASE__				)

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

