

from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
			import tensorflow as tf

			from ..tf_utils import stable_softmax


if is_torch_available():
			import torch


a_						     =    logging.get_logger(__name__)






@add_end_docstrings(
    __A							,	R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '							,	)
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: int      ,      a							: GenericTensor	)							->       np.ndarray:

				"""simple docstring"""

				if self.framework == "tf":
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()
				elif self.framework == "pt":
					SCREAMING_SNAKE_CASE   :      Dict				     =       torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,      as_tuple=a	)
				else:
					raise ValueError("Unsupported framework"	)
				return masked_index
			def 							__UpperCamelCase (			self							: Any      ,      a							: GenericTensor	)							->       np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_masked_index(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       np.prod(masked_index.shape	)
				if numel < 1:
					raise PipelineException(
					    "fill-mask"      ,      self.model.base_model_prefix      ,      F"No mask_token ({self.tokenizer.mask_token}) found on the input"      ,      )
			def 							__UpperCamelCase (			self							: Dict      ,      a							: GenericTensor	)							->       int:

				"""simple docstring"""

				if isinstance(a      ,      a	):
					for model_input in model_inputs:
						self._ensure_exactly_one_mask_token(model_input["input_ids"][0]	)
				else:
					for input_ids in model_inputs["input_ids"]:
						self._ensure_exactly_one_mask_token(a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any      ,      a							: int=None      ,      **a							: Optional[int]	)							->       Dict[str, GenericTensor]:

				"""simple docstring"""

				if return_tensors is None:
					SCREAMING_SNAKE_CASE   :      int				     =       self.framework
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.tokenizer(a      ,      return_tensors=a	)
				self.ensure_exactly_one_mask_token(a	)
				return model_inputs
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.model(**a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model_inputs["input_ids"]
				return model_outputs
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any      ,      a							: Dict=5      ,      a							: List[str]=None	)							->       Optional[Any]:

				"""simple docstring"""

				if target_ids is not None and target_ids.shape[0] < top_k:
					SCREAMING_SNAKE_CASE   :      int				     =       target_ids.shape[0]
				SCREAMING_SNAKE_CASE   :      Dict				     =       model_outputs["input_ids"][0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model_outputs["logits"]

				if self.framework == "tf":
					SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()[:, 0]

					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       outputs.numpy()

					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       outputs[0, masked_index, :]
					SCREAMING_SNAKE_CASE   :      int				     =       stable_softmax(a      ,      axis=-1	)
					if target_ids is not None:
						SCREAMING_SNAKE_CASE   :      int				     =       tf.gather_nd(tf.squeeze(a      ,      0	)      ,      target_ids.reshape(-1      ,      1	)	)
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       tf.expand_dims(a      ,      0	)

					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tf.math.top_k(a      ,      k=a	)
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       topk.values.numpy(), topk.indices.numpy()
				else:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,      as_tuple=a	).squeeze(-1	)
					# Fill mask pipeline supports only one ${mask_token} per sample

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs[0, masked_index, :]
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       logits.softmax(dim=-1	)
					if target_ids is not None:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       probs[..., target_ids]

					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       probs.topk(a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       values.shape[0] == 1
				for i, (_values, _predictions) in enumerate(zip(values.tolist()      ,      predictions.tolist()	)	):
					SCREAMING_SNAKE_CASE   :      Any				     =       []
					for v, p in zip(_values      ,      _predictions	):
						# Copy is important since we're going to modify this array in place
						SCREAMING_SNAKE_CASE   :      List[str]				     =       input_ids.numpy().copy()
						if target_ids is not None:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       target_ids[p].tolist()

						SCREAMING_SNAKE_CASE   :      int				     =       p
						# Filter padding out:
						SCREAMING_SNAKE_CASE   :      Dict				     =       tokens[np.where(tokens != self.tokenizer.pad_token_id	)]
						# Originally we skip special tokens to give readable output.
						# For multi masks though, the other [MASK] would be removed otherwise
						# making the output look odd, so we add them back
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.tokenizer.decode(a      ,      skip_special_tokens=a	)
						SCREAMING_SNAKE_CASE   :      Dict				     =       {"score": v, "token": p, "token_str": self.tokenizer.decode([p]	), "sequence": sequence}
						row.append(a	)
					result.append(a	)
				if single_mask:
					return result[0]
				return result
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Any      ,      a							: Tuple=None	)							->       int:

				"""simple docstring"""

				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       [targets]
				try:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer.get_vocab()
				except Exception:
					SCREAMING_SNAKE_CASE   :      str				     =       {}
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
				for target in targets:
					SCREAMING_SNAKE_CASE   :      Dict				     =       vocab.get(a      ,      a	)
					if id_ is None:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer(
						    a      ,      add_special_tokens=a      ,      return_attention_mask=a      ,      return_token_type_ids=a      ,      max_length=1      ,      truncation=a      ,      )["input_ids"]
						if len(a	) == 0:
							logger.warning(
							    F"The specified target token `{target}` does not exist in the model vocabulary. "
							    "We cannot replace it with anything meaningful, ignoring it"	)
							continue
						SCREAMING_SNAKE_CASE   :      Dict				     =       input_ids[0]
						# XXX: If users encounter this pass
						# it becomes pretty slow, so let's make sure
						# The warning enables them to fix the input to
						# get faster performance.
						logger.warning(
						    F"The specified target token `{target}` does not exist in the model vocabulary. "
						    F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_	)}`."	)
					target_ids.append(id_	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       list(set(a	)	)
				if len(a	) == 0:
					raise ValueError("At least one target must be provided when passed."	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       np.array(a	)
				return target_ids
			def 							__UpperCamelCase (			self							: Any      ,      a							: Optional[int]=None      ,      a							: List[str]=None	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {}

				if targets is not None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_target_ids(a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Tuple				     =       target_ids

				if top_k is not None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       top_k

				if self.tokenizer.mask_token_id is None:
					raise PipelineException(
					    "fill-mask"      ,      self.model.base_model_prefix      ,      "The tokenizer does not define a `mask_token`."	)
				return {}, {}, postprocess_params






			def __call__(			self							: Any      ,      a							: Tuple      ,      *a							: int      ,      **a							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       super().__call__(a      ,      **a	)
				if isinstance(a      ,      a	) and len(a	) == 1:
					return outputs[0]
				return outputs


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	)	)