
"""simple docstring"""

import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


SCREAMING_SNAKE_CASE_            =    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def      lowercase__    (       lowerCAmelCase					:       Dict			)			->   Optional[Any]:






    """simple docstring"""




    if "token" in model_name_or_path:
        return "rag_token"
    if "sequence" in model_name_or_path:
        return "rag_sequence"
    if "bart" in model_name_or_path:
        return "bart"
    return None




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       Optional[Any]			)			->   List[Any]:






    """simple docstring"""




    return max(metric_fn(lowerCAmelCase			,					lowerCAmelCase			) for gt in ground_truths			)




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       [line.strip() for line in open(lowerCAmelCase			,					'r'			).readlines()]
    UpperCAmelCase          =       []

    if args.gold_data_mode == "qa":
        UpperCAmelCase          =       pd.read_csv(lowerCAmelCase			,					sep='\t'			,					header=lowerCAmelCase			)
        for answer_list in data[1]:
            UpperCAmelCase          =       ast.literal_eval(lowerCAmelCase			)
            answers.append(lowerCAmelCase			)
    else:
        UpperCAmelCase          =       [line.strip() for line in open(lowerCAmelCase			,					'r'			).readlines()]
        UpperCAmelCase          =       [[reference] for reference in references]

    UpperCAmelCase          =       UpperCAmelCase          =       UpperCAmelCase          =       0
    for prediction, ground_truths in zip(lowerCAmelCase			,					lowerCAmelCase			):
        total += 1
        em += metric_max_over_ground_truths(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
        fa += metric_max_over_ground_truths(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)

    UpperCAmelCase          =       100.0 * em / total
    UpperCAmelCase          =       100.0 * fa / total

    logger.info(F"F1: {fa:.2f}"			)
    logger.info(F"EM: {em:.2f}"			)




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       Optional[Any]			,					lowerCAmelCase					:       str			)			->   Optional[Any]:






    """simple docstring"""




    UpperCAmelCase          =       args.k
    UpperCAmelCase          =       [line.strip() for line in open(lowerCAmelCase			,					'r'			).readlines()]
    UpperCAmelCase          =       [line.strip() for line in open(lowerCAmelCase			,					'r'			).readlines()]

    UpperCAmelCase          =       UpperCAmelCase          =       0
    for hypo, reference in zip(lowerCAmelCase			,					lowerCAmelCase			):
        UpperCAmelCase          =       set(hypo.split('\t'			)[:k]			)
        UpperCAmelCase          =       set(reference.split('\t'			)			)
        total += 1
        em += len(hypo_provenance & ref_provenance			) / k

    UpperCAmelCase          =       100.0 * em / total
    logger.info(F"Precision@{k}: {em: .2f}"			)




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       List[Any]			)			->   Optional[Any]:






    """simple docstring"""




    def strip_title(lowerCAmelCase					:       Dict			):
        if title.startswith('"'			):
            UpperCAmelCase          =       title[1:]
        if title.endswith('"'			):
            UpperCAmelCase          =       title[:-1]
        return title

    UpperCAmelCase          =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowerCAmelCase			,					return_tensors='pt'			,					padding=lowerCAmelCase			,					truncation=lowerCAmelCase			,					)['input_ids'].to(args.device			)

    UpperCAmelCase          =       rag_model.rag.question_encoder(lowerCAmelCase			)
    UpperCAmelCase          =       question_enc_outputs[0]

    UpperCAmelCase          =       rag_model.retriever(
        lowerCAmelCase			,					question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy()			,					prefix=rag_model.rag.generator.config.prefix			,					n_docs=rag_model.config.n_docs			,					return_tensors='pt'			,					)
    UpperCAmelCase          =       rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
    UpperCAmelCase          =       []
    for docs in all_docs:
        UpperCAmelCase          =       [strip_title(lowerCAmelCase			) for title in docs['title']]
        provenance_strings.append('\t'.join(lowerCAmelCase			)			)
    return provenance_strings




def      lowercase__    (       lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       List[Any]			)			->   List[Any]:






    """simple docstring"""




    with torch.no_grad():
        UpperCAmelCase          =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            lowerCAmelCase			,					return_tensors='pt'			,					padding=lowerCAmelCase			,					truncation=lowerCAmelCase			)

        UpperCAmelCase          =       inputs_dict.input_ids.to(args.device			)
        UpperCAmelCase          =       inputs_dict.attention_mask.to(args.device			)
        UpperCAmelCase          =       rag_model.generate(  # rag_model overwrites generate
            lowerCAmelCase			,					attention_mask=lowerCAmelCase			,					num_beams=args.num_beams			,					min_length=args.min_length			,					max_length=args.max_length			,					early_stopping=lowerCAmelCase			,					num_return_sequences=1			,					bad_words_ids=[[0, 0]]			,					)
        UpperCAmelCase          =       rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase			,					skip_special_tokens=lowerCAmelCase			)

        if args.print_predictions:
            for q, a in zip(lowerCAmelCase			,					lowerCAmelCase			):
                logger.info('Q: {} - A: {}'.format(lowerCAmelCase			,					lowerCAmelCase			)			)

        return answers




def      lowercase__    (       )			->   Dict:






    """simple docstring"""




    UpperCAmelCase          =       argparse.ArgumentParser()
    parser.add_argument(
        '--model_type'			,					choices=['rag_sequence', 'rag_token', 'bart']			,					type=lowerCAmelCase			,					help=(
            'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
            ' model_name_or_path'
        )			,					)
    parser.add_argument(
        '--index_name'			,					default=lowerCAmelCase			,					choices=['exact', 'compressed', 'legacy']			,					type=lowerCAmelCase			,					help='RAG model retriever type'			,					)
    parser.add_argument(
        '--index_path'			,					default=lowerCAmelCase			,					type=lowerCAmelCase			,					help='Path to the retrieval index'			,					)
    parser.add_argument('--n_docs'			,					default=5			,					type=lowerCAmelCase			,					help='Number of retrieved docs'			)
    parser.add_argument(
        '--model_name_or_path'			,					default=lowerCAmelCase			,					type=lowerCAmelCase			,					required=lowerCAmelCase			,					help='Path to pretrained checkpoints or model identifier from huggingface.co/models'			,					)
    parser.add_argument(
        '--eval_mode'			,					choices=['e2e', 'retrieval']			,					default='e2e'			,					type=lowerCAmelCase			,					help=(
            'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
            ' precision@k.'
        )			,					)
    parser.add_argument('--k'			,					default=1			,					type=lowerCAmelCase			,					help='k for the precision@k calculation'			)
    parser.add_argument(
        '--evaluation_set'			,					default=lowerCAmelCase			,					type=lowerCAmelCase			,					required=lowerCAmelCase			,					help='Path to a file containing evaluation samples'			,					)
    parser.add_argument(
        '--gold_data_path'			,					default=lowerCAmelCase			,					type=lowerCAmelCase			,					required=lowerCAmelCase			,					help='Path to a tab-separated file with gold samples'			,					)
    parser.add_argument(
        '--gold_data_mode'			,					default='qa'			,					type=lowerCAmelCase			,					choices=['qa', 'ans']			,					help=(
            'Format of the gold data file'
            'qa - a single line in the following format: question [tab] answer_list'
            'ans - a single line of the gold file contains the expected answer string'
        )			,					)
    parser.add_argument(
        '--predictions_path'			,					type=lowerCAmelCase			,					default='predictions.txt'			,					help='Name of the predictions file, to be stored in the checkpoints directory'			,					)
    parser.add_argument(
        '--eval_all_checkpoints'			,					action='store_true'			,					help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'			,					)
    parser.add_argument(
        '--eval_batch_size'			,					default=8			,					type=lowerCAmelCase			,					help='Batch size per GPU/CPU for evaluation.'			,					)
    parser.add_argument(
        '--recalculate'			,					help='Recalculate predictions even if the prediction file exists'			,					action='store_true'			,					)
    parser.add_argument(
        '--num_beams'			,					default=4			,					type=lowerCAmelCase			,					help='Number of beams to be used when generating answers'			,					)
    parser.add_argument('--min_length'			,					default=1			,					type=lowerCAmelCase			,					help='Min length of the generated answers'			)
    parser.add_argument('--max_length'			,					default=50			,					type=lowerCAmelCase			,					help='Max length of the generated answers'			)

    parser.add_argument(
        '--print_predictions'			,					action='store_true'			,					help='If True, prints predictions while evaluating.'			,					)
    parser.add_argument(
        '--print_docs'			,					action='store_true'			,					help='If True, prints docs retried while generating.'			,					)
    UpperCAmelCase          =       parser.parse_args()
    UpperCAmelCase          =       torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
    return args




def      lowercase__    (       lowerCAmelCase					:       List[str]			)			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       {}
    if args.model_type is None:
        UpperCAmelCase          =       infer_model_type(args.model_name_or_path			)
        assert args.model_type is not None
    if args.model_type.startswith('rag'			):
        UpperCAmelCase          =       RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
        UpperCAmelCase          =       args.n_docs
        if args.index_name is not None:
            UpperCAmelCase          =       args.index_name
        if args.index_path is not None:
            UpperCAmelCase          =       args.index_path
    else:
        UpperCAmelCase          =       BartForConditionalGeneration

    UpperCAmelCase          =       (
        [f.path for f in os.scandir(args.model_name_or_path			) if f.is_dir()]
        if args.eval_all_checkpoints
        else [args.model_name_or_path]
    )

    logger.info('Evaluate the following checkpoints: %s'			,					lowerCAmelCase			)

    UpperCAmelCase          =       get_scores if args.eval_mode == 'e2e' else get_precision_at_k
    UpperCAmelCase          =       evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

    for checkpoint in checkpoints:
        if os.path.exists(args.predictions_path			) and (not args.recalculate):
            logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path			)			)
            score_fn(lowerCAmelCase			,					args.predictions_path			,					args.gold_data_path			)
            continue

        logger.info('***** Running evaluation for {} *****'.format(lowerCAmelCase			)			)
        logger.info('  Batch size = %d'			,					args.eval_batch_size			)
        logger.info('  Predictions will be stored under {}'.format(args.predictions_path			)			)

        if args.model_type.startswith('rag'			):
            UpperCAmelCase          =       RagRetriever.from_pretrained(lowerCAmelCase			,					**lowerCAmelCase			)
            UpperCAmelCase          =       model_class.from_pretrained(lowerCAmelCase			,					retriever=lowerCAmelCase			,					**lowerCAmelCase			)
            model.retriever.init_retrieval()
        else:
            UpperCAmelCase          =       model_class.from_pretrained(lowerCAmelCase			,					**lowerCAmelCase			)
        model.to(args.device			)

        with open(args.evaluation_set			,					'r'			) as eval_file, open(args.predictions_path			,					'w'			) as preds_file:
            UpperCAmelCase          =       []
            for line in tqdm(lowerCAmelCase			):
                questions.append(line.strip()			)
                if len(lowerCAmelCase			) == args.eval_batch_size:
                    UpperCAmelCase          =       evaluate_batch_fn(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
                    preds_file.write('\n'.join(lowerCAmelCase			) + '\n'			)
                    preds_file.flush()
                    UpperCAmelCase          =       []
            if len(lowerCAmelCase			) > 0:
                UpperCAmelCase          =       evaluate_batch_fn(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
                preds_file.write('\n'.join(lowerCAmelCase			)			)
                preds_file.flush()

            score_fn(lowerCAmelCase			,					args.predictions_path			,					args.gold_data_path			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    get_args()
   main(args)





"""simple docstring"""

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 DetaImageProcessor
class   _UpperCAmelCase				(		unittest.TestCase				):




      def __init__(				self ,							lowercase_ ,							lowercase_=7 ,							lowercase_=3 ,							lowercase_=3_0 ,							lowercase_=4_0_0 ,							lowercase_=True ,							lowercase_=None ,							lowercase_=True ,							lowercase_=[0.5, 0.5, 0.5] ,							lowercase_=[0.5, 0.5, 0.5] ,							lowercase_=True ,							lowercase_=1 / 2_5_5 ,							lowercase_=True ,							)    ->       Union[str, Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          UpperCAmelCase          =       size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       num_channels
          UpperCAmelCase          =       min_resolution
          UpperCAmelCase          =       max_resolution
          UpperCAmelCase          =       do_resize
          UpperCAmelCase          =       size
          UpperCAmelCase          =       do_normalize
          UpperCAmelCase          =       image_mean
          UpperCAmelCase          =       image_std
          UpperCAmelCase          =       do_rescale
          UpperCAmelCase          =       rescale_factor
          UpperCAmelCase          =       do_pad




      def      a_   (				self				)    ->       int:
          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      a_   (				self ,							lowercase_ ,							lowercase_=False				)    ->       Union[str, Any]:
          if not batched:
              UpperCAmelCase          =       image_inputs[0]
              if isinstance(lowercase_ ,							Image.Image				):
                  UpperCAmelCase      ,				UpperCAmelCase          =       image.size
              else:
                  UpperCAmelCase      ,				UpperCAmelCase          =       image.shape[1], image.shape[2]
              if w < h:
                  UpperCAmelCase          =       int(self.size['shortest_edge'] * h / w				)
                  UpperCAmelCase          =       self.size['shortest_edge']
              elif w > h:
                  UpperCAmelCase          =       self.size['shortest_edge']
                  UpperCAmelCase          =       int(self.size['shortest_edge'] * w / h				)
              else:
                  UpperCAmelCase          =       self.size['shortest_edge']
                  UpperCAmelCase          =       self.size['shortest_edge']

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

          return expected_height, expected_width

@require_torch
@require_vision
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						DetaImageProcessor if is_vision_available() else None




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       DetaImageProcessingTester(self				)




      @property
      def      a_   (				self				)    ->       Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       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_ ,							'do_rescale'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_pad'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'size'				)				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       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_				)




      def      a_   (				self				)    ->       int:
          pass




      def      a_   (				self				)    ->       Union[str, Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							Image.Image				)

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

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

          UpperCAmelCase          =       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      a_   (				self				)    ->       Tuple:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(image_inputs[0] ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(lowercase_ ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       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      a_   (				self				)    ->       Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(image_inputs[0] ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(lowercase_ ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       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      a_   (				self				)    ->       str:
          # prepare image and target
          UpperCAmelCase          =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
          with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,							'r'				) as f:
              UpperCAmelCase          =       json.loads(f.read()				)

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

          # encode them
          UpperCAmelCase          =       DetaImageProcessor()
          UpperCAmelCase          =       image_processing(images=lowercase_ ,							annotations=lowercase_ ,							return_tensors='pt'				)

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

          UpperCAmelCase          =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,							lowercase_ ,							atol=1E-4				)				)

          # verify area
          UpperCAmelCase          =       torch.tensor([5_8_8_7.9_6_0_0, 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
          UpperCAmelCase          =       torch.Size([6, 4]				)
          self.assertEqual(encoding['labels'][0]['boxes'].shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,							lowercase_ ,							atol=1E-3				)				)
          # verify image_id
          UpperCAmelCase          =       torch.tensor([3_9_7_6_9]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,							lowercase_				)				)
          # verify is_crowd
          UpperCAmelCase          =       torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,							lowercase_				)				)
          # verify class_labels
          UpperCAmelCase          =       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
          UpperCAmelCase          =       torch.tensor([4_8_0, 6_4_0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,							lowercase_				)				)
          # verify size
          UpperCAmelCase          =       torch.tensor([8_0_0, 1_0_6_6]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,							lowercase_				)				)




      @slow
      def      a_   (				self				)    ->       Any:
          # prepare image, target and masks_path
          UpperCAmelCase          =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
          with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,							'r'				) as f:
              UpperCAmelCase          =       json.loads(f.read()				)

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

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

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

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

          UpperCAmelCase          =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,							lowercase_ ,							atol=1E-4				)				)

          # verify area
          UpperCAmelCase          =       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_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,							lowercase_				)				)
          # verify boxes
          UpperCAmelCase          =       torch.Size([6, 4]				)
          self.assertEqual(encoding['labels'][0]['boxes'].shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,							lowercase_ ,							atol=1E-3				)				)
          # verify image_id
          UpperCAmelCase          =       torch.tensor([3_9_7_6_9]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,							lowercase_				)				)
          # verify is_crowd
          UpperCAmelCase          =       torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,							lowercase_				)				)
          # verify class_labels
          UpperCAmelCase          =       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
          UpperCAmelCase          =       8_2_2_8_7_3
          self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,							lowercase_				)
          # verify orig_size
          UpperCAmelCase          =       torch.tensor([4_8_0, 6_4_0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,							lowercase_				)				)
          # verify size
          UpperCAmelCase          =       torch.tensor([8_0_0, 1_0_6_6]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,							lowercase_				)				)



