

import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
			from cookiecutter.main import cookiecutter

			a_						     =    True
except ImportError:
			a_						     =    False

a_						     =    logging.get_logger(__name__)  # pylint: disable=invalid-name

def  lowerCamelCase__ (					_a):
	return AddNewModelCommand(args.testing							,			args.testing_file							,			path=args.path)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			@staticmethod
			def 							__UpperCamelCase (			a							: ArgumentParser	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       parser.add_parser("add-new-model"	)
				add_new_model_parser.add_argument("--testing"      ,      action="store_true"      ,      help="If in testing mode."	)
				add_new_model_parser.add_argument("--testing_file"      ,      type=a      ,      help="Configuration file on which to run."	)
				add_new_model_parser.add_argument(
				    "--path"      ,      type=a      ,      help="Path to cookiecutter. Should only be used for testing purposes."	)
				add_new_model_parser.set_defaults(func=a	)
			def __init__(			self							: List[Any]      ,      a							: bool      ,      a							: str      ,      a							: int=None      ,      *a							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       testing
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       testing_file
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       path






			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				warnings.warn(
				    "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
				    "It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
				    "checks, you should use `transformers-cli add-new-model-like` instead."	)
				if not _has_cookiecutter:
					raise ImportError(
					    "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
					    "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"	)
				# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
				if len(a	) > 0:
					raise ValueError(
					    "Several directories starting with `cookiecutter-template-` in current working directory. "
					    "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
					    "change your working directory."	)

				SCREAMING_SNAKE_CASE   :      Any				     =       (
				    Path(a	).parent.parent.parent.parent if self._path is None else Path(self._path	).parent.parent
				)
				SCREAMING_SNAKE_CASE   :      Any				     =       path_to_transformer_root / "templates" / "adding_a_new_model"

				# Execute cookiecutter
				if not self._testing:
					cookiecutter(str(a	)	)
				else:
					with open(self._testing_file      ,      "r"	) as configuration_file:
						SCREAMING_SNAKE_CASE   :      int				     =       json.load(a	)

					cookiecutter(
					    str(path_to_cookiecutter if self._path is None else self._path	)      ,      no_input=a      ,      extra_context=a      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

				# Retrieve configuration
				with open(directory + "/configuration.json"      ,      "r"	) as configuration_file:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       json.load(a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       configuration["lowercase_modelname"]
				SCREAMING_SNAKE_CASE   :      Dict				     =       configuration["generate_tensorflow_pytorch_and_flax"]
				os.remove(F"{directory}/configuration.json"	)

				SCREAMING_SNAKE_CASE   :      Any				     =       "PyTorch" in generate_tensorflow_pytorch_and_flax
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "TensorFlow" in generate_tensorflow_pytorch_and_flax
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "Flax" in generate_tensorflow_pytorch_and_flax

				SCREAMING_SNAKE_CASE   :      str				     =       F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
				os.makedirs(a      ,      exist_ok=a	)
				os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"      ,      exist_ok=a	)

				# Tests require submodules as they have parent imports
				with open(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"      ,      "w"	):
					pass

				shutil.move(
				    F"{directory}/__init__.py"      ,      F"{model_dir}/__init__.py"      ,      )
				shutil.move(
				    F"{directory}/configuration_{lowercase_model_name}.py"      ,      F"{model_dir}/configuration_{lowercase_model_name}.py"      ,      )

				def remove_copy_lines(a							: Tuple	):
					with open(a      ,      "r"	) as f:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       f.readlines()
					with open(a      ,      "w"	) as f:
						for line in lines:
							if "# Copied from transformers." not in line:
								f.write(a	)

				if output_pytorch:
					if not self._testing:
						remove_copy_lines(F"{directory}/modeling_{lowercase_model_name}.py"	)

					shutil.move(
					    F"{directory}/modeling_{lowercase_model_name}.py"      ,      F"{model_dir}/modeling_{lowercase_model_name}.py"      ,      )

					shutil.move(
					    F"{directory}/test_modeling_{lowercase_model_name}.py"      ,      F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"      ,      )
				else:
					os.remove(F"{directory}/modeling_{lowercase_model_name}.py"	)
					os.remove(F"{directory}/test_modeling_{lowercase_model_name}.py"	)

				if output_tensorflow:
					if not self._testing:
						remove_copy_lines(F"{directory}/modeling_tf_{lowercase_model_name}.py"	)

					shutil.move(
					    F"{directory}/modeling_tf_{lowercase_model_name}.py"      ,      F"{model_dir}/modeling_tf_{lowercase_model_name}.py"      ,      )

					shutil.move(
					    F"{directory}/test_modeling_tf_{lowercase_model_name}.py"      ,      F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"      ,      )
				else:
					os.remove(F"{directory}/modeling_tf_{lowercase_model_name}.py"	)
					os.remove(F"{directory}/test_modeling_tf_{lowercase_model_name}.py"	)

				if output_flax:
					if not self._testing:
						remove_copy_lines(F"{directory}/modeling_flax_{lowercase_model_name}.py"	)

					shutil.move(
					    F"{directory}/modeling_flax_{lowercase_model_name}.py"      ,      F"{model_dir}/modeling_flax_{lowercase_model_name}.py"      ,      )

					shutil.move(
					    F"{directory}/test_modeling_flax_{lowercase_model_name}.py"      ,      F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"      ,      )
				else:
					os.remove(F"{directory}/modeling_flax_{lowercase_model_name}.py"	)
					os.remove(F"{directory}/test_modeling_flax_{lowercase_model_name}.py"	)

				shutil.move(
				    F"{directory}/{lowercase_model_name}.md"      ,      F"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"      ,      )

				shutil.move(
				    F"{directory}/tokenization_{lowercase_model_name}.py"      ,      F"{model_dir}/tokenization_{lowercase_model_name}.py"      ,      )

				shutil.move(
				    F"{directory}/tokenization_fast_{lowercase_model_name}.py"      ,      F"{model_dir}/tokenization_{lowercase_model_name}_fast.py"      ,      )

				from os import fdopen, remove
				from shutil import copymode, move
				from tempfile import mkstemp

				def replace(a							: str      ,      a							: str      ,      a							: List[str]	):
					# Create temp file
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       mkstemp()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       False
					with fdopen(a      ,      "w"	) as new_file:
						with open(a	) as old_file:
							for line in old_file:
								new_file.write(a	)
								if line_to_copy_below in line:
									SCREAMING_SNAKE_CASE   :      List[Any]				     =       True
									for line_to_copy in lines_to_copy:
										new_file.write(a	)

					if not line_found:
						raise ValueError(F"Line {line_to_copy_below} was not found in file."	)

					# Copy the file permissions from the old file to the new file
					copymode(a      ,      a	)
					# Remove original file
					remove(a	)
					# Move new file
					move(a      ,      a	)

				def skip_units(a							: Tuple	):
					return (
					    ("generating PyTorch" in line and not output_pytorch)
					    or ("generating TensorFlow" in line and not output_tensorflow)
					    or ("generating Flax" in line and not output_flax)
					)

				def replace_in_files(a							: List[str]	):
					with open(a	) as datafile:
						SCREAMING_SNAKE_CASE   :      Dict				     =       []
						SCREAMING_SNAKE_CASE   :      Any				     =       False
						SCREAMING_SNAKE_CASE   :      str				     =       False
						for line in datafile:
							if "# To replace in: " in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      Any				     =       line.split("\""	)[1]
								SCREAMING_SNAKE_CASE   :      str				     =       skip_units(a	)
							elif "# Below: " in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      str				     =       line.split("\""	)[1]
								SCREAMING_SNAKE_CASE   :      int				     =       skip_units(a	)
							elif "# End." in line and "##" not in line:
								if not skip_file and not skip_snippet:
									replace(a      ,      a      ,      a	)

								SCREAMING_SNAKE_CASE   :      List[str]				     =       []
							elif "# Replace with" in line and "##" not in line:
								SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
							elif "##" not in line:
								lines_to_copy.append(a	)

					remove(a	)

				replace_in_files(F"{directory}/to_replace_{lowercase_model_name}.py"	)
				os.rmdir(a	)


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   ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				SCREAMING_SNAKE_CASE   :      Any				     =       parent
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      str				     =       do_resize
				SCREAMING_SNAKE_CASE   :      Any				     =       size
				SCREAMING_SNAKE_CASE   :      str				     =       do_normalize
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_mean
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_std
				SCREAMING_SNAKE_CASE   :      Any				     =       do_rescale
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rescale_factor
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       do_pad
			def 							__UpperCamelCase (			self							: str	)							->       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 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, Any]      ,      a							: int=False	)							->       int:

				"""simple docstring"""

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

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

				return expected_height, expected_width






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =DetaImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       DetaImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(a      ,      "image_mean"	)	)
				self.assertTrue(hasattr(a      ,      "image_std"	)	)
				self.assertTrue(hasattr(a      ,      "do_normalize"	)	)
				self.assertTrue(hasattr(a      ,      "do_resize"	)	)
				self.assertTrue(hasattr(a      ,      "do_rescale"	)	)
				self.assertTrue(hasattr(a      ,      "do_pad"	)	)
				self.assertTrue(hasattr(a      ,      "size"	)	)
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, 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      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self.image_processor_tester.get_expected_values(a	)

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

				# Test batched
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,      )
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      str				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a      ,      numpify=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      np.ndarray	)

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

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.image_processor_tester.get_expected_values(a	)

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

				# Test batched
				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,      )
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_processor_tester.get_expected_values(a	)

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

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

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,      )
			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      List[str]				     =       {"image_id": 3_9769, "annotations": target}

				# encode them
				SCREAMING_SNAKE_CASE   :      int				     =       DetaImageProcessor()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_processing(images=a      ,      annotations=a      ,      return_tensors="pt"	)

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

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor([0.2796, 0.3138, 0.3481]	)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      a      ,      atol=1e-4	)	)

				# verify area
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      str				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([75, 75, 63, 65, 17, 17]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)






			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      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:
					SCREAMING_SNAKE_CASE   :      Any				     =       json.loads(f.read()	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

				# encode them
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       DetaImageProcessor(format="coco_panoptic"	)
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(images=a      ,      annotations=a      ,      masks_path=a      ,      return_tensors="pt"	)

				# verify pixel values
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.Size([1, 3, 800, 1066]	)
				self.assertEqual(encoding["pixel_values"].shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor([0.2796, 0.3138, 0.3481]	)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      a      ,      atol=1e-4	)	)

				# verify area
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      int				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([17, 17, 63, 75, 75, 93]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      a	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)