



'''simple docstring'''


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ).json()
   UpperCamelCase					=  {}

   try:
      job_links.update({job['name']: job['html_url'] for job in result['jobs']}    )
      UpperCamelCase					=  math.ceil((result['total_count'] - 100) / 100    )

      for i in range(A__    ):
         UpperCamelCase					=  requests.get(url + F"""&page={i + 2}"""    ,						headers=A__    ).json()
         job_links.update({job['name']: job['html_url'] for job in result['jobs']}    )

      return job_links
   except Exception:
      print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""    )

   return {}







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ).json()
   UpperCamelCase					=  {}

   try:
      artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}    )
      UpperCamelCase					=  math.ceil((result['total_count'] - 100) / 100    )

      for i in range(A__    ):
         UpperCamelCase					=  requests.get(url + F"""&page={i + 2}"""    ,						headers=A__    ).json()
         artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}    )

      return artifacts
   except Exception:
      print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""    )

   return {}







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ,						allow_redirects=A__    )
   UpperCamelCase					=  result.headers['Location']
   UpperCamelCase					=  requests.get(A__    ,						allow_redirects=A__    )
   UpperCamelCase					=  os.path.join(A__    ,						F"""{artifact_name}.zip"""    )
   with open(A__    ,						'wb'    ) as fp:
      fp.write(response.content    )







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  []
   UpperCamelCase					=  []
   UpperCamelCase					=  None

   with zipfile.ZipFile(A__    ) as z:
      for filename in z.namelist():
         if not os.path.isdir(A__    ):
            # read the file
            if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
               with z.open(A__    ) as f:
                  for line in f:
                     UpperCamelCase					=  line.decode('UTF-8'    ).strip()
                     if filename == "failures_line.txt":
                        try:
                           # `error_line` is the place where `error` occurs
                           UpperCamelCase					=  line[: line.index(': '    )]
                           UpperCamelCase					=  line[line.index(': '    ) + len(': '    ) :]
                           errors.append([error_line, error]    )
                        except Exception:
                           # skip un-related lines
                           pass
                     elif filename == "summary_short.txt" and line.startswith('FAILED '    ):
                        # `test` is the test method that failed
                        UpperCamelCase					=  line[len('FAILED '    ) :]
                        failed_tests.append(A__    )
                     elif filename == "job_name.txt":
                        UpperCamelCase					=  line

   if len(A__    ) != len(A__    ):
      raise ValueError(
          F"""`errors` and `failed_tests` should have the same number of elements. Got {len(A__    )} for `errors` """
          F"""and {len(A__    )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
          ' problem.'    )

   UpperCamelCase					=  None
   if job_name and job_links:
      UpperCamelCase					=  job_links.get(A__    ,						A__    )

   # A list with elements of the form (line of error, error, failed test)
   UpperCamelCase					=  [x + [y] + [job_link] for x, y in zip(A__    ,						A__    )]

   return result







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  []

   UpperCamelCase					=  [os.path.join(A__    ,						A__    ) for p in os.listdir(A__    ) if p.endswith('.zip'    )]
   for p in paths:
      errors.extend(get_errors_from_single_artifact(A__    ,						job_links=A__    )    )

   return errors







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  Counter()
   counter.update([x[1] for x in logs]    )
   UpperCamelCase					=  counter.most_common()
   UpperCamelCase					=  {}
   for error, count in counts:
      if error_filter is None or error not in error_filter:
         UpperCamelCase					=  {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

   UpperCamelCase					=  dict(sorted(r.items()    ,						key=lambda A__    : item[1]["count"]    ,						reverse=A__    )    )
   return r







def 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  test.split('::'    )[0]
   if test.startswith('tests/models/'    ):
      UpperCamelCase					=  test.split('/'    )[2]
   else:
      UpperCamelCase					=  None

   return test







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  [(x[0], x[1], get_model(x[2]    )) for x in logs]
   UpperCamelCase					=  [x for x in logs if x[2] is not None]
   UpperCamelCase					=  {x[2] for x in logs}

   UpperCamelCase					=  {}
   for test in tests:
      UpperCamelCase					=  Counter()
      # count by errors in `test`
      counter.update([x[1] for x in logs if x[2] == test]    )
      UpperCamelCase					=  counter.most_common()
      UpperCamelCase					=  {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
      UpperCamelCase					=  sum(error_counts.values()    )
      if n_errors > 0:
         UpperCamelCase					=  {'count': n_errors, 'errors': error_counts}

   UpperCamelCase					=  dict(sorted(r.items()    ,						key=lambda A__    : item[1]["count"]    ,						reverse=A__    )    )
   return r







def 				__lowerCamelCase				(   A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  '| no. | error | status |'
   UpperCamelCase					=  '|-:|:-|:-|'
   UpperCamelCase					=  [header, sep]
   for error in reduced_by_error:
      UpperCamelCase					=  reduced_by_error[error]['count']
      UpperCamelCase					=  F"""| {count} | {error[:100]} |  |"""
      lines.append(A__    )

   return "\n".join(A__    )







def 				__lowerCamelCase				(   A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  '| model | no. of errors | major error | count |'
   UpperCamelCase					=  '|-:|-:|-:|-:|'
   UpperCamelCase					=  [header, sep]
   for model in reduced_by_model:
      UpperCamelCase					=  reduced_by_model[model]['count']
      UpperCamelCase				,      UpperCamelCase					=  list(reduced_by_model[model]['errors'].items()    )[0]
      UpperCamelCase					=  F"""| {model} | {count} | {error[:60]} | {_count} |"""
      lines.append(A__    )

   return "\n".join(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       Any      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
   parser.add_argument(
       "--output_dir",
       type=str,
       required=True,
       help="Where to store the downloaded artifacts and other result files.",
   )
   parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
   _lowerCamelCase	:       List[str]      	=	parser.parse_args()

   os.makedirs(args.output_dir, exist_ok=True)

   _lowerCamelCase	:       int      	=	get_job_links(args.workflow_run_id, token=args.token)
   _lowerCamelCase	:       int      	=	{}
   # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
   # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
   if _job_links:
      for k, v in _job_links.items():
         # This is how GitHub actions combine job names.
         if " / " in k:
            _lowerCamelCase	:       Optional[int]      	=	k.find(" / ")
            _lowerCamelCase	:       int      	=	k[index + len(" / ") :]
         _lowerCamelCase	:       int      	=	v
   with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
      json.dump(job_links, fp, ensure_ascii=False, indent=4)

   _lowerCamelCase	:       str      	=	get_artifacts_links(args.workflow_run_id, token=args.token)
   with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
      json.dump(artifacts, fp, ensure_ascii=False, indent=4)

   for idx, (name, url) in enumerate(artifacts.items()):
      download_artifact(name, url, args.output_dir, args.token)
      # Be gentle to GitHub
      time.sleep(1)

   _lowerCamelCase	:       Union[str, Any]      	=	get_all_errors(args.output_dir, job_links=job_links)

   # `e[1]` is the error
   _lowerCamelCase	:       Optional[Any]      	=	Counter()
   counter.update([e[1] for e in errors])

   # print the top 30 most common test errors
   _lowerCamelCase	:       Optional[Any]      	=	counter.most_common(30)
   for item in most_common:
      print(item)

   with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
      json.dump(errors, fp, ensure_ascii=False, indent=4)

   _lowerCamelCase	:       Optional[int]      	=	reduce_by_error(errors)
   _lowerCamelCase	:       Tuple      	=	reduce_by_model(errors)

   _lowerCamelCase	:       Optional[int]      	=	make_github_table(reduced_by_error)
   _lowerCamelCase	:       Union[str, Any]      	=	make_github_table_per_model(reduced_by_model)

   with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
      fp.write(sa)
   with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
      fp.write(sa)





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 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]=7					,      __lowerCamelCase							:			Dict=3					,      __lowerCamelCase							:			Union[str, Any]=30					,      __lowerCamelCase							:			Union[str, Any]=400					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			Tuple=None					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			List[str]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			str=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			Any=True					,      __lowerCamelCase							:			Union[str, Any]=1 / 255					,      __lowerCamelCase							:			Optional[Any]=True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
             lowerCamelCase__		:				Dict    =  parent
             lowerCamelCase__		:				Optional[int]    =  batch_size
             lowerCamelCase__		:				str    =  num_channels
             lowerCamelCase__		:				Optional[Any]    =  min_resolution
             lowerCamelCase__		:				List[Any]    =  max_resolution
             lowerCamelCase__		:				int    =  do_resize
             lowerCamelCase__		:				Union[str, Any]    =  size
             lowerCamelCase__		:				Union[str, Any]    =  do_normalize
             lowerCamelCase__		:				int    =  image_mean
             lowerCamelCase__		:				Optional[int]    =  image_std
             lowerCamelCase__		:				List[Any]    =  do_rescale
             lowerCamelCase__		:				Optional[Any]    =  rescale_factor
             lowerCamelCase__		:				Any    =  do_pad





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''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 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]=False       ):


             '''simple docstring'''



             if not batched:
                   lowerCamelCase__		:				Tuple    =  image_inputs[0]
                   if isinstance(__lowerCamelCase					,      Image.Image       ):
                         lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  image.size
                   else:
                         lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.shape[1], image.shape[2]
                   if w < h:
                         lowerCamelCase__		:				List[Any]    =  int(self.size["shortest_edge"] * h / w       )
                         lowerCamelCase__		:				Optional[Any]    =  self.size["shortest_edge"]
                   elif w > h:
                         lowerCamelCase__		:				List[Any]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				List[str]    =  int(self.size["shortest_edge"] * w / h       )
                   else:
                         lowerCamelCase__		:				Optional[int]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				Union[str, Any]    =  self.size["shortest_edge"]

             else:
                   lowerCamelCase__		:				Dict    =  []
                   for image in image_inputs:
                         lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.get_expected_values([image]       )
                         expected_values.append((expected_height, expected_width)       )
                   lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[0]       )[0]
                   lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[1]       )[1]

             return expected_height, expected_width







@require_torch
@require_vision
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			DetaImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  DetaImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_mean"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "image_std"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_resize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_rescale"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_pad"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "size"       )       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size					,      {"shortest_edge": 18, "longest_edge": 1333}       )
             self.assertEqual(image_processor.do_pad					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				List[str]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      Image.Image       )

             # Test not batched input
             lowerCamelCase__		:				Optional[int]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

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

             # Test batched
             lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  image_processing(__lowerCamelCase					,      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 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             lowerCamelCase__		:				Optional[int]    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      np.ndarray       )

             # Test not batched input
             lowerCamelCase__		:				Any    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

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

             # Test batched
             lowerCamelCase__		:				Optional[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

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





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				Dict    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      torch.Tensor       )

             # Test not batched input
             lowerCamelCase__		:				Dict    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.image_processor_tester.get_expected_values(__lowerCamelCase       )

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

             # Test batched
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

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





       @slow
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Any    =  {"image_id": 39769, "annotations": target}

             # encode them
             lowerCamelCase__		:				Union[str, Any]    =  DetaImageProcessor()
             lowerCamelCase__		:				List[str]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[str]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  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]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				Tuple    =  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"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Union[str, Any]    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  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]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				int    =  torch.tensor([75, 75, 63, 65, 17, 17]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify orig_size
             lowerCamelCase__		:				int    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )





       @slow
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Tuple    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             lowerCamelCase__		:				Union[str, Any]    =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

             # encode them
             lowerCamelCase__		:				Tuple    =  DetaImageProcessor(format="coco_panoptic"       )
             lowerCamelCase__		:				Dict    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      masks_path=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[Any]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  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]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				List[Any]    =  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"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Any    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				int    =  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]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				Tuple    =  torch.tensor([17, 17, 63, 75, 75, 93]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify masks
             lowerCamelCase__		:				Union[str, Any]    =  822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,      __lowerCamelCase       )
             # verify orig_size
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )


