
import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
 import torch.distributed.checkpoint as dist_cp
 from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
 from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
 from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_lowercase											=    get_logger(__name__)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Dict=0				)			->						int:
       os.makedirs(UpperCAmelCase_			,							exist_ok=UpperCAmelCase_				)
       with FSDP.state_dict_type(
           UpperCAmelCase_			,							fsdp_plugin.state_dict_type			,							fsdp_plugin.state_dict_config			,							fsdp_plugin.optim_state_dict_config				):
              SCREAMING_SNAKE_CASE_							:				List[Any]		=model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                     if accelerator.process_index == 0:
                            logger.info(f'Saving model to {output_model_file}'				)
                            torch.save(UpperCAmelCase_			,							UpperCAmelCase_				)
                            logger.info(f'Model saved to {output_model_file}'				)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                     logger.info(f'Saving model to {output_model_file}'				)
                     torch.save(UpperCAmelCase_			,							UpperCAmelCase_				)
                     logger.info(f'Model saved to {output_model_file}'				)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				int		=os.path.join(UpperCAmelCase_			,							f'{MODEL_NAME}_{model_index}'				)
                     os.makedirs(UpperCAmelCase_			,							exist_ok=UpperCAmelCase_				)
                     logger.info(f'Saving model to {ckpt_dir}'				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''model''': state_dict}

                     dist_cp.save_state_dict(
                         state_dict=UpperCAmelCase_			,							storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase_				)			,							planner=DefaultSavePlanner()			,							)
                     logger.info(f'Model saved to {ckpt_dir}'				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Any=0				)			->						Union[str, Any]:
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           UpperCAmelCase_			,							fsdp_plugin.state_dict_type			,							fsdp_plugin.state_dict_config			,							fsdp_plugin.optim_state_dict_config				):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(UpperCAmelCase_				) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
                                       '''initializing FSDP object'''				)
                            return
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     SCREAMING_SNAKE_CASE_							:				Tuple		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                     logger.info(f'Loading model from {input_model_file}'				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.load(UpperCAmelCase_				)
                     logger.info(f'Model loaded from {input_model_file}'				)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     SCREAMING_SNAKE_CASE_							:				Dict		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                     logger.info(f'Loading model from {input_model_file}'				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.load(UpperCAmelCase_				)
                     logger.info(f'Model loaded from {input_model_file}'				)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=(
                         os.path.join(UpperCAmelCase_			,							f'{MODEL_NAME}_{model_index}'				)
                         if f'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading model from {ckpt_dir}'				)
                     SCREAMING_SNAKE_CASE_							:				Any		={'''model''': model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=UpperCAmelCase_			,							storage_reader=dist_cp.FileSystemReader(UpperCAmelCase_				)			,							planner=DefaultLoadPlanner()			,							)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=state_dict['''model''']
                     logger.info(f'Model loaded from {ckpt_dir}'				)
              model.load_state_dict(UpperCAmelCase_				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     List[Any]=0				)			->						int:
       os.makedirs(UpperCAmelCase_			,							exist_ok=UpperCAmelCase_				)
       with FSDP.state_dict_type(
           UpperCAmelCase_			,							fsdp_plugin.state_dict_type			,							fsdp_plugin.state_dict_config			,							fsdp_plugin.optim_state_dict_config				):
              SCREAMING_SNAKE_CASE_							:				str		=FSDP.optim_state_dict(UpperCAmelCase_			,							UpperCAmelCase_				)
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=(
                                f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            SCREAMING_SNAKE_CASE_							:				Tuple		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                            logger.info(f'Saving Optimizer state to {output_optimizer_file}'				)
                            torch.save(UpperCAmelCase_			,							UpperCAmelCase_				)
                            logger.info(f'Optimizer state saved in {output_optimizer_file}'				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=os.path.join(UpperCAmelCase_			,							f'{OPTIMIZER_NAME}_{optimizer_index}'				)
                     os.makedirs(UpperCAmelCase_			,							exist_ok=UpperCAmelCase_				)
                     logger.info(f'Saving Optimizer state to {ckpt_dir}'				)
                     dist_cp.save_state_dict(
                         state_dict={'''optimizer''': optim_state}			,							storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase_				)			,							planner=DefaultSavePlanner()			,							)
                     logger.info(f'Optimizer state saved in {ckpt_dir}'				)

def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Any=0				)			->						Optional[int]:
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           UpperCAmelCase_			,							fsdp_plugin.state_dict_type			,							fsdp_plugin.state_dict_config			,							fsdp_plugin.optim_state_dict_config				):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     SCREAMING_SNAKE_CASE_							:				Dict		=None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     SCREAMING_SNAKE_CASE_							:				Dict		=(
                         f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=os.path.join(UpperCAmelCase_			,							UpperCAmelCase_				)
                     logger.info(f'Loading Optimizer state from {input_optimizer_file}'				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=torch.load(UpperCAmelCase_				)
                     logger.info(f'Optimizer state loaded from {input_optimizer_file}'				)
              else:
                     SCREAMING_SNAKE_CASE_							:				str		=(
                         os.path.join(UpperCAmelCase_			,							f'{OPTIMIZER_NAME}_{optimizer_index}'				)
                         if f'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading Optimizer from {ckpt_dir}'				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()			,							optimizer_key='''optimizer'''			,							storage_reader=dist_cp.FileSystemReader(UpperCAmelCase_				)			,							)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=optim_state['''optimizer''']
                     logger.info(f'Optimizer loaded from {ckpt_dir}'				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=FSDP.optim_state_dict_to_load(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)
              optimizer.load_state_dict(UpperCAmelCase_				)


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 YolosImageProcessor


class 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       __A=[0.5, 0.5, 0.5]						,       __A=[0.5, 0.5, 0.5]						,       __A=True						,       __A=1 / 255						,       __A=True						,       )						->    Tuple:
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              SCREAMING_SNAKE_CASE_							:				Dict		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_channels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				str		=max_resolution
              SCREAMING_SNAKE_CASE_							:				int		=do_resize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=size
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_mean
              SCREAMING_SNAKE_CASE_							:				Any		=image_std
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rescale_factor
              SCREAMING_SNAKE_CASE_							:				str		=do_pad


       def 	_snake_case				(    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 	_snake_case				(    self						,       __A						,       __A=False				)						->    Any:
              if not batched:
                     SCREAMING_SNAKE_CASE_							:				str		=image_inputs[0]
                     if isinstance(__A						,       Image.Image				):
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image.size
                     else:
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=image.shape[1], image.shape[2]
                     if w < h:
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=int(self.size['''shortest_edge'''] * h / w				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.size['''shortest_edge''']
                     elif w > h:
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=self.size['''shortest_edge''']
                            SCREAMING_SNAKE_CASE_							:				Dict		=int(self.size['''shortest_edge'''] * w / h				)
                     else:
                            SCREAMING_SNAKE_CASE_							:				str		=self.size['''shortest_edge''']
                            SCREAMING_SNAKE_CASE_							:				str		=self.size['''shortest_edge''']

              else:
                     SCREAMING_SNAKE_CASE_							:				Any		=[]
                     for image in image_inputs:
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=self.get_expected_values([image]				)
                            expected_values.append((expected_height, expected_width)				)
                     SCREAMING_SNAKE_CASE_							:				str		=max(__A						,       key=lambda __A				: item[0]				)[0]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=max(__A						,       key=lambda __A				: item[1]				)[1]

              return expected_height, expected_width






@require_torch
@require_vision
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						YolosImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=YolosImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Dict:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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						,       '''size'''				)				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
              self.assertEqual(image_processor.do_pad						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class.from_dict(
                  self.image_processor_dict						,       size=42						,       max_size=84						,       pad_and_return_pixel_mask=__A				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 42, '''longest_edge''': 84}				)
              self.assertEqual(image_processor.do_pad						,       __A				)


       def 	_snake_case				(    self				)						->    List[str]:
              pass


       def 	_snake_case				(    self				)						->    int:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              SCREAMING_SNAKE_CASE_							:				Union[str, 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_							:				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_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=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 	_snake_case				(    self				)						->    Optional[Any]:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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_							:				Dict		=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_							:				Dict		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=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 	_snake_case				(    self				)						->    Dict:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=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_							:				Any		=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_							:				str		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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 	_snake_case				(    self				)						->    List[Any]:
              # Initialize image_processings
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processing_class(**self.image_processor_dict				)
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(do_resize=__A						,       do_normalize=__A						,       do_rescale=__A				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

              # Test whether the method "pad" and calling the image processor return the same tensors
              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_processing_a.pad(__A						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_processing_a(__A						,       return_tensors='''pt'''				)

              self.assertTrue(
                  torch.allclose(encoded_images_with_method['''pixel_values''']						,       encoded_images['''pixel_values''']						,       atol=1e-4				)				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              # prepare image and target
              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_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Dict		={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(images=__A						,       annotations=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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
              SCREAMING_SNAKE_CASE_							:				Tuple		=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
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([39_769]				)
              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_							:				str		=torch.tensor([75, 75, 63, 65, 17, 17]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)





       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              # prepare image, target and masks_path
              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_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

              SCREAMING_SNAKE_CASE_							:				Tuple		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

              # encode them
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=YolosImageProcessor(format='''coco_panoptic'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_processing(images=__A						,       annotations=__A						,       masks_path=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Tuple		=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([17, 17, 63, 75, 75, 93]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify masks
              SCREAMING_SNAKE_CASE_							:				List[Any]		=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,       __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_							:				Any		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)
