

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					'''altclip_text_model'''



			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					List[Any]=25_0002       ,						lowerCAmelCase__  :					Union[str, Any]=1024       ,						lowerCAmelCase__  :					Tuple=24       ,						lowerCAmelCase__  :					List[str]=16       ,						lowerCAmelCase__  :					Tuple=4096       ,						lowerCAmelCase__  :					Tuple="gelu"       ,						lowerCAmelCase__  :					Tuple=0.1       ,						lowerCAmelCase__  :					Optional[int]=0.1       ,						lowerCAmelCase__  :					Union[str, Any]=514       ,						lowerCAmelCase__  :					Dict=1       ,						lowerCAmelCase__  :					Optional[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=0.02       ,						lowerCAmelCase__  :					int=1E-05       ,						lowerCAmelCase__  :					List[Any]=1       ,						lowerCAmelCase__  :					Optional[Any]=0       ,						lowerCAmelCase__  :					Any=2       ,						lowerCAmelCase__  :					Optional[int]="absolute"       ,						lowerCAmelCase__  :					List[str]=True       ,						lowerCAmelCase__  :					List[str]=768       ,						**lowerCAmelCase__  :					Any       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			List[str]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			str  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	initializer_factor
									SCREAMING_SNAKE_CASE_:			Dict  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Tuple  		=	position_embedding_type
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_cache
									SCREAMING_SNAKE_CASE_:			Tuple  		=	project_dim







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''altclip_vision_model'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]=768       ,						lowerCAmelCase__  :					str=3072       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=12       ,						lowerCAmelCase__  :					Any=12       ,						lowerCAmelCase__  :					Optional[Any]=3       ,						lowerCAmelCase__  :					List[str]=224       ,						lowerCAmelCase__  :					Optional[int]=32       ,						lowerCAmelCase__  :					Union[str, Any]="quick_gelu"       ,						lowerCAmelCase__  :					Optional[Any]=1E-5       ,						lowerCAmelCase__  :					List[Any]=0.0       ,						lowerCAmelCase__  :					Any=0.02       ,						lowerCAmelCase__  :					Dict=1.0       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	projection_dim
									SCREAMING_SNAKE_CASE_:			int  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Tuple  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Any  		=	num_channels
									SCREAMING_SNAKE_CASE_:			int  		=	patch_size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	image_size
									SCREAMING_SNAKE_CASE_:			Any  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			List[str]  		=	initializer_factor
									SCREAMING_SNAKE_CASE_:			Tuple  		=	attention_dropout
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_act




			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Union[str, Any]       ,						lowerCAmelCase__  :					Union[str, os.PathLike]       ,						**lowerCAmelCase__  :					Optional[Any]):
									cls._set_token_in_kwargs(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	cls.get_config_dict(lowerCAmelCase__       ,						**lowerCAmelCase__)

									# get the vision config dict if we are loading from AltCLIPConfig
									if config_dict.get("model_type") == "altclip":
															SCREAMING_SNAKE_CASE_:			Tuple  		=	config_dict["vision_config"]

									if "model_type" in config_dict and hasattr(cls       ,						"model_type") and config_dict["model_type"] != cls.model_type:
															logger.warning(
															    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
															    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

									return cls.from_dict(lowerCAmelCase__       ,						**lowerCAmelCase__)







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''altclip'''
			_UpperCAmelCase       :							Dict      							=					True



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					int=None       ,						lowerCAmelCase__  :					Union[str, Any]=None       ,						lowerCAmelCase__  :					List[str]=768       ,						lowerCAmelCase__  :					Optional[Any]=2.6592       ,						**lowerCAmelCase__  :					str):
									# If `_config_dict` exist, we use them for the backward compatibility.
									# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
									# of confusion!).
									SCREAMING_SNAKE_CASE_:			str  		=	kwargs.pop("text_config_dict"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	kwargs.pop("vision_config_dict"       ,						lowerCAmelCase__)

									super().__init__(**lowerCAmelCase__)

									# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
									# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
									# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
									if text_config_dict is not None:
															if text_config is None:
																					SCREAMING_SNAKE_CASE_:			Any  		=	{}

															# This is the complete result when using `text_config_dict`.
															SCREAMING_SNAKE_CASE_:			int  		=	AltCLIPTextConfig(**lowerCAmelCase__).to_dict()

															# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
															for key, value in _text_config_dict.items():
																					if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																	SCREAMING_SNAKE_CASE_:			Dict  		=	(
																																	    F"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
																																	    F"The value `text_config_dict[\"{key}\"]` will be used instead."
																																	)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																	SCREAMING_SNAKE_CASE_:			Any  		=	(
																																	    F"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
																																	    F"value `text_config[\"{key}\"]` will be overriden."
																																	)
																											logger.warning(lowerCAmelCase__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
															text_config.update(_text_config_dict)

									if vision_config_dict is not None:
															if vision_config is None:
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	{}

															# This is the complete result when using `vision_config_dict`.
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	AltCLIPVisionConfig(**lowerCAmelCase__).to_dict()
															# convert keys to string instead of integer
															if "id2label" in _vision_config_dict:
																					SCREAMING_SNAKE_CASE_:			int  		=	{
																					    str(lowerCAmelCase__): value for key, value in _vision_config_dict["id2label"].items()
																					}

															# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
															for key, value in _vision_config_dict.items():
																					if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																	SCREAMING_SNAKE_CASE_:			List[str]  		=	(
																																	    F"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
																																	    F"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
																																	)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																	SCREAMING_SNAKE_CASE_:			List[Any]  		=	(
																																	    F"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
																																	    F"The value `vision_config[\"{key}\"]` will be overriden."
																																	)
																											logger.warning(lowerCAmelCase__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
															vision_config.update(_vision_config_dict)

									if text_config is None:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	{}
															logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.")

									if vision_config is None:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{}
															logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	AltCLIPTextConfig(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AltCLIPVisionConfig(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	projection_dim
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	logit_scale_init_value
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	1.0



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Tuple       ,						lowerCAmelCase__  :					AltCLIPTextConfig       ,						lowerCAmelCase__  :					AltCLIPVisionConfig       ,						**lowerCAmelCase__  :					Dict):
									return cls(text_config=text_config.to_dict()       ,						vision_config=vision_config.to_dict()       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	copy.deepcopy(self.__dict__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self.text_config.to_dict()
									SCREAMING_SNAKE_CASE_:			int  		=	self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.__class__.model_type
									return output







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 DeformableDetrImageProcessor



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					Tuple=3       ,						lowerCAmelCase__  :					Optional[int]=30       ,						lowerCAmelCase__  :					Dict=400       ,						lowerCAmelCase__  :					int=True       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					Union[str, Any]=True       ,						lowerCAmelCase__  :					Any=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Optional[Any]=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Tuple=1 / 255       ,						lowerCAmelCase__  :					int=True       ,						):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									SCREAMING_SNAKE_CASE_:			str  		=	parent
									SCREAMING_SNAKE_CASE_:			Tuple  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	num_channels
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	min_resolution
									SCREAMING_SNAKE_CASE_:			Tuple  		=	max_resolution
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Any  		=	image_mean
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_std
									SCREAMING_SNAKE_CASE_:			Tuple  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			int  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			int  		=	do_pad



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

									else:
															SCREAMING_SNAKE_CASE_:			int  		=	[]
															for image in image_inputs:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_expected_values([image])
																					expected_values.append((expected_height, expected_width))
															SCREAMING_SNAKE_CASE_:			Tuple  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[0])[0]
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[1])[1]

									return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					DeformableDetrImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	DeformableDetrImageProcessingTester(self)



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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_mean"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_std"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_normalize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_resize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_rescale"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_pad"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"size"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,						{"shortest_edge": 18, "longest_edge": 1333})
									self.assertEqual(image_processor.do_pad       ,						lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									pass



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

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

									# Test batched
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values
									self.assertEqual(
									    encoded_images.shape       ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						numpify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						np.ndarray)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

									# Test batched
									SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:			int  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						torchify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						torch.Tensor)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image and target
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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_:			str  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{"image_id": 3_9769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE_:			str  		=	DeformableDetrImageProcessor()
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						return_tensors="pt")

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

									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			int  		=	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"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			str  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([75, 75, 63, 65, 17, 17])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))




			@slow
			def 	_SCREAMING_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_:			List[Any]  		=	json.loads(f.read())

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

									SCREAMING_SNAKE_CASE_:			int  		=	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

									# encode them
									SCREAMING_SNAKE_CASE_:			Any  		=	DeformableDetrImageProcessor(format="coco_panoptic")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						masks_path=lowerCAmelCase__       ,						return_tensors="pt")

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

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([17, 17, 63, 75, 75, 93])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify masks
									SCREAMING_SNAKE_CASE_:			Tuple  		=	82_2873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCAmelCase__)
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))






