


"""simple docstring"""




import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


__magic_name__						: List[str]    =				get_logger(__name__)


__magic_name__						: Tuple    =				Path(__file__).parent / 'model_card_template.md'
__magic_name__						: Any    =				uuida().hex
__magic_name__						: List[Any]    =				os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__magic_name__						: int    =				os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__magic_name__						: Any    =				HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'





def       a_ (	lowercase__ :Union[Dict, str, None] = None     ):
			__lowerCamelCase          =      f'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += f'; torch/{_torch_version}'
			if is_flax_available():
						ua += f'; jax/{_jax_version}'
						ua += f'; flax/{_flax_version}'
			if is_onnx_available():
						ua += f'; onnxruntime/{_onnxruntime_version}'
			# CI will set this value to True
			if os.environ.get("""DIFFUSERS_IS_CI""",			""""""     ).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(lowercase__,			lowercase__     ):
						ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()     )
			elif isinstance(lowercase__,			lowercase__     ):
						ua += "; " + user_agent
			return ua





def       a_ (	lowercase__ :str,			lowercase__ :Optional[str] = None,			lowercase__ :Optional[str] = None     ):
			if token is None:
						__lowerCamelCase          =      HfFolder.get_token()
			if organization is None:
						__lowerCamelCase          =      whoami(lowercase__     )["""name"""]
						return f'{username}/{model_id}'
			else:
						return f'{organization}/{model_id}'





def       a_ (	lowercase__ :Optional[int],			lowercase__ :List[str]     ):
			if not is_jinja_available():
						raise ValueError(
						    """Modelcard rendering is based on Jinja templates."""
						    """ Please make sure to have `jinja` installed before using `create_model_card`."""
						    """ To install it, please run `pip install Jinja2`."""     )

			if hasattr(lowercase__,			"""local_rank"""     ) and args.local_rank not in [-1, 0]:
						return

			__lowerCamelCase          =      args.hub_token if hasattr(lowercase__,			"""hub_token"""     ) else None
			__lowerCamelCase          =      get_full_repo_name(lowercase__,			token=lowercase__     )

			__lowerCamelCase          =      ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language="""en""",			license="""apache-2.0""",			library_name="""diffusers""",			tags=[],			datasets=args.dataset_name,			metrics=[],			),			template_path=lowercase__,			model_name=lowercase__,			repo_name=lowercase__,			dataset_name=args.dataset_name if hasattr(lowercase__,			"""dataset_name"""     ) else None,			learning_rate=args.learning_rate,			train_batch_size=args.train_batch_size,			eval_batch_size=args.eval_batch_size,			gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(lowercase__,			"""gradient_accumulation_steps"""     ) else None
			    ),			adam_betaa=args.adam_betaa if hasattr(lowercase__,			"""adam_beta1"""     ) else None,			adam_betaa=args.adam_betaa if hasattr(lowercase__,			"""adam_beta2"""     ) else None,			adam_weight_decay=args.adam_weight_decay if hasattr(lowercase__,			"""adam_weight_decay"""     ) else None,			adam_epsilon=args.adam_epsilon if hasattr(lowercase__,			"""adam_epsilon"""     ) else None,			lr_scheduler=args.lr_scheduler if hasattr(lowercase__,			"""lr_scheduler"""     ) else None,			lr_warmup_steps=args.lr_warmup_steps if hasattr(lowercase__,			"""lr_warmup_steps"""     ) else None,			ema_inv_gamma=args.ema_inv_gamma if hasattr(lowercase__,			"""ema_inv_gamma"""     ) else None,			ema_power=args.ema_power if hasattr(lowercase__,			"""ema_power"""     ) else None,			ema_max_decay=args.ema_max_decay if hasattr(lowercase__,			"""ema_max_decay"""     ) else None,			mixed_precision=args.mixed_precision,			)

			__lowerCamelCase          =      os.path.join(args.output_dir,			"""README.md"""     )
			model_card.save(lowercase__     )





def       a_ (	lowercase__ :Optional[str],			lowercase__ :Optional[str] = None     ):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			__lowerCamelCase          =      str(Path(lowercase__     ).as_posix()     )
			__lowerCamelCase          =      re.search(r"""snapshots/([^/]+)/""",			lowercase__     )
			if search is None:
						return None
			__lowerCamelCase          =      search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(lowercase__     ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__magic_name__						: Union[str, Any]    =				os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__magic_name__						: List[str]    =				os.path.join(hf_cache_home, 'diffusers')





def       a_ (	lowercase__ :Optional[str] = None,			lowercase__ :Optional[str] = None     ):
			if new_cache_dir is None:
						__lowerCamelCase          =      DIFFUSERS_CACHE
			if old_cache_dir is None:
						__lowerCamelCase          =      old_diffusers_cache

			__lowerCamelCase          =      Path(lowercase__     ).expanduser()
			__lowerCamelCase          =      Path(lowercase__     ).expanduser()
			for old_blob_path in old_cache_dir.glob("""**/blobs/*"""     ):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									__lowerCamelCase          =      new_cache_dir / old_blob_path.relative_to(lowercase__     )
									new_blob_path.parent.mkdir(parents=lowercase__,			exist_ok=lowercase__     )
									os.replace(lowercase__,			lowercase__     )
									try:
												os.symlink(lowercase__,			lowercase__     )
									except OSError:
												logger.warning(
												    """Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."""     )
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


__magic_name__						: Optional[Any]    =				os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
					__magic_name__						: str    =				0
else:
					with open(cache_version_file) as f:
										try:
															__magic_name__						: Tuple    =				int(f.read())
										except ValueError:
															__magic_name__						: int    =				0

if cache_version < 1:
					__magic_name__						: str    =				os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
					if old_cache_is_not_empty:
										logger.warning(
										    'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
										    'existing cached models. This is a one-time operation, you can interrupt it or run it '
										    'later by calling `diffusers.utils.hub_utils.move_cache()`.'
										)
										try:
															move_cache()
										except Exception as e:
															__magic_name__						: str    =				'\n'.join(traceback.format_tb(e.__traceback__))
															logger.error(
															    f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
															    'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
															    'message and we will do our best to help.'
															)

if cache_version < 1:
					try:
										os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
										with open(cache_version_file, 'w') as f:
															f.write('1')
					except Exception:
										logger.warning(
										    f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
										    'the directory exists and can be written to.'
										)





def       a_ (	lowercase__ :str,			lowercase__ :Optional[str] = None     ):
			if variant is not None:
						__lowerCamelCase          =      weights_name.split("""."""     )
						__lowerCamelCase          =      splits[:-1] + [variant] + splits[-1:]
						__lowerCamelCase          =      """.""".join(lowercase__     )

			return weights_name





def       a_ (	lowercase__ :Dict,			*,
    lowercase__ :str,			lowercase__ :Union[str, Any],			lowercase__ :Tuple,			lowercase__ :Optional[int],			lowercase__ :Dict,			lowercase__ :Optional[Any],			lowercase__ :str,			lowercase__ :Tuple,			lowercase__ :Any,			lowercase__ :str,			lowercase__ :Optional[int]=None,			):
			__lowerCamelCase          =      str(lowercase__     )
			if os.path.isfile(lowercase__     ):
						return pretrained_model_name_or_path
			elif os.path.isdir(lowercase__     ):
						if os.path.isfile(os.path.join(lowercase__,			lowercase__     )     ):
									# Load from a PyTorch checkpoint
									__lowerCamelCase          =      os.path.join(lowercase__,			lowercase__     )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(lowercase__,			lowercase__,			lowercase__     )     ):
									__lowerCamelCase          =      os.path.join(lowercase__,			lowercase__,			lowercase__     )
									return model_file
						else:
									raise EnvironmentError(
									    f'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'     )
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(lowercase__     ).base_version     ) >= version.parse("""0.20.0"""     )
						):
									try:
												__lowerCamelCase          =      hf_hub_download(
												    lowercase__,			filename=_add_variant(lowercase__,			lowercase__     ),			cache_dir=lowercase__,			force_download=lowercase__,			proxies=lowercase__,			resume_download=lowercase__,			local_files_only=lowercase__,			use_auth_token=lowercase__,			user_agent=lowercase__,			subfolder=lowercase__,			revision=revision or commit_hash,			)
												warnings.warn(
												    f'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.',			lowercase__,			)
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    f'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowercase__,			lowercase__     )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowercase__,			lowercase__     )}\' so that the correct variant file can be added.',			lowercase__,			)
						try:
									# 2. Load model file as usual
									__lowerCamelCase          =      hf_hub_download(
									    lowercase__,			filename=lowercase__,			cache_dir=lowercase__,			force_download=lowercase__,			proxies=lowercase__,			resume_download=lowercase__,			local_files_only=lowercase__,			use_auth_token=lowercase__,			user_agent=lowercase__,			subfolder=lowercase__,			revision=revision or commit_hash,			)
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    f'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
									    """listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
									    """token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
									    """login`."""     )
						except RevisionNotFoundError:
									raise EnvironmentError(
									    f'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
									    """this model name. Check the model page at """
									    f'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'     )
						except EntryNotFoundError:
									raise EnvironmentError(
									    f'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'     )
						except HTTPError as err:
									raise EnvironmentError(
									    f'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'     )
						except ValueError:
									raise EnvironmentError(
									    f'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
									    f' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
									    f' directory containing a file named {weights_name} or'
									    """ \nCheckout your internet connection or see how to run the library in"""
									    """ offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."""     )
						except EnvironmentError:
									raise EnvironmentError(
									    f'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
									    """'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
									    f'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
									    f'containing a file named {weights_name}'     )








"""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 			__snake_case   (unittest.TestCase  ):




							def __init__(     self:    Any     ,							A_:    Optional[int]     ,							A_:    Any=7     ,							A_:    Union[str, Any]=3     ,							A_:    List[Any]=30     ,							A_:    int=4_00     ,							A_:    Any=True     ,							A_:    str=None     ,							A_:    Dict=True     ,							A_:    Optional[Any]=[0.5, 0.5, 0.5]     ,							A_:    List[Any]=[0.5, 0.5, 0.5]     ,							A_:    Any=True     ,							A_:    Optional[Any]=1 / 2_55     ,							A_:    int=True     ,							):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										__lowerCamelCase          =      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      batch_size
										__lowerCamelCase          =      num_channels
										__lowerCamelCase          =      min_resolution
										__lowerCamelCase          =      max_resolution
										__lowerCamelCase          =      do_resize
										__lowerCamelCase          =      size
										__lowerCamelCase          =      do_normalize
										__lowerCamelCase          =      image_mean
										__lowerCamelCase          =      image_std
										__lowerCamelCase          =      do_rescale
										__lowerCamelCase          =      rescale_factor
										__lowerCamelCase          =      do_pad




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

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

										return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       DetaImageProcessor if is_vision_available() else None




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      DetaImageProcessingTester(self   )




							@property
							def   __a   (     self:    Optional[int]   ):
										return self.image_processor_tester.prepare_image_processor_dict()




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      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   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size     ,							{"""shortest_edge""": 18, """longest_edge""": 13_33}   )
										self.assertEqual(image_processor.do_pad     ,							A_   )




							def   __a   (     self:    Any   ):
										pass




							def   __a   (     self:    Optional[Any]   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										__lowerCamelCase          =      prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=A_   )
										for image in image_inputs:
													self.assertIsInstance(A_     ,							Image.Image   )

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

										__lowerCamelCase     ,__lowerCamelCase          =      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
										__lowerCamelCase     ,__lowerCamelCase          =      self.image_processor_tester.get_expected_values(A_     ,							batched=A_   )

										__lowerCamelCase          =      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   __a   (     self:    int   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(image_inputs[0]     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(A_     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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   __a   (     self:    Dict   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(image_inputs[0]     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(A_     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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   __a   (     self:    List[str]   ):
										# prepare image and target
										__lowerCamelCase          =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""     ,							"""r"""   ) as f:
													__lowerCamelCase          =      json.loads(f.read()   )

										__lowerCamelCase          =      {"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__lowerCamelCase          =      DetaImageProcessor()
										__lowerCamelCase          =      image_processing(images=A_     ,							annotations=A_     ,							return_tensors="""pt"""   )

										# verify pixel values
										__lowerCamelCase          =      torch.Size([1, 3, 8_00, 10_66]   )
										self.assertEqual(encoding["""pixel_values"""].shape     ,							A_   )

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

										# verify area
										__lowerCamelCase          =      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]     ,							A_   )   )
										# verify boxes
										__lowerCamelCase          =      torch.Size([6, 4]   )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,							A_   )
										__lowerCamelCase          =      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]     ,							A_     ,							atol=1E-3   )   )
										# verify image_id
										__lowerCamelCase          =      torch.tensor([3_97_69]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,							A_   )   )
										# verify is_crowd
										__lowerCamelCase          =      torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,							A_   )   )
										# verify class_labels
										__lowerCamelCase          =      torch.tensor([75, 75, 63, 65, 17, 17]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,							A_   )   )
										# verify orig_size
										__lowerCamelCase          =      torch.tensor([4_80, 6_40]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,							A_   )   )
										# verify size
										__lowerCamelCase          =      torch.tensor([8_00, 10_66]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,							A_   )   )




							@slow
							def   __a   (     self:    Tuple   ):
										# prepare image, target and masks_path
										__lowerCamelCase          =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""     ,							"""r"""   ) as f:
													__lowerCamelCase          =      json.loads(f.read()   )

										__lowerCamelCase          =      {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__lowerCamelCase          =      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

										# encode them
										__lowerCamelCase          =      DetaImageProcessor(format="""coco_panoptic"""   )
										__lowerCamelCase          =      image_processing(images=A_     ,							annotations=A_     ,							masks_path=A_     ,							return_tensors="""pt"""   )

										# verify pixel values
										__lowerCamelCase          =      torch.Size([1, 3, 8_00, 10_66]   )
										self.assertEqual(encoding["""pixel_values"""].shape     ,							A_   )

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

										# verify area
										__lowerCamelCase          =      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]     ,							A_   )   )
										# verify boxes
										__lowerCamelCase          =      torch.Size([6, 4]   )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,							A_   )
										__lowerCamelCase          =      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]     ,							A_     ,							atol=1E-3   )   )
										# verify image_id
										__lowerCamelCase          =      torch.tensor([3_97_69]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,							A_   )   )
										# verify is_crowd
										__lowerCamelCase          =      torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,							A_   )   )
										# verify class_labels
										__lowerCamelCase          =      torch.tensor([17, 17, 63, 75, 75, 93]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,							A_   )   )
										# verify masks
										__lowerCamelCase          =      82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()     ,							A_   )
										# verify orig_size
										__lowerCamelCase          =      torch.tensor([4_80, 6_40]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,							A_   )   )
										# verify size
										__lowerCamelCase          =      torch.tensor([8_00, 10_66]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,							A_   )   )




