


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     a		(unittest.TestCase				):




							"""simple docstring"""


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

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

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

														return expected_height, expected_width

@require_torch
@require_vision
class     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  YolosImageProcessor if is_vision_available() else None

							def    __snake_case (							self	:  Optional[int]    )     ->   List[Any]:
														__snake_case		:      List[str]										=       YolosImageProcessingTester(self    )

							@property
							def    __snake_case (							self	:  int    )     ->   Tuple:
														return self.image_processor_tester.prepare_image_processor_dict()

							def    __snake_case (							self	:  List[str]    )     ->   List[str]:
														__snake_case		:      Tuple										=       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   ,     "size"    )    )

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														__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   ,     lowerCamelCase    )

														__snake_case		:      Union[str, Any]										=       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    __snake_case (							self	:  Union[str, Any]    )     ->   Dict:
														pass

							def    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														# Initialize image_processing
														__snake_case		:      str										=       self.image_processing_class(**self.image_processor_dict    )
														# create random PIL images
														__snake_case		:      int										=       prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=lowerCamelCase    )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase   ,     Image.Image    )

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

														__snake_case			,      __snake_case		:      Union[str, 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
														__snake_case			,      __snake_case		:      Tuple										=       self.image_processor_tester.get_expected_values(lowerCamelCase   ,     batched=lowerCamelCase    )

														__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    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														# Initialize image_processing
														__snake_case		:      int										=       self.image_processing_class(**self.image_processor_dict    )
														# create random numpy tensors
														__snake_case		:      Union[str, 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
														__snake_case		:      Optional[int]										=       image_processing(image_inputs[0]   ,     return_tensors="pt"    ).pixel_values

														__snake_case			,      __snake_case		:      int										=       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
														__snake_case		:      Tuple										=       image_processing(lowerCamelCase   ,     return_tensors="pt"    ).pixel_values

														__snake_case			,      __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    __snake_case (							self	:  Dict    )     ->   Dict:
														# Initialize image_processing
														__snake_case		:      List[Any]										=       self.image_processing_class(**self.image_processor_dict    )
														# create random PyTorch tensors
														__snake_case		:      Dict										=       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
														__snake_case		:      Any										=       image_processing(image_inputs[0]   ,     return_tensors="pt"    ).pixel_values

														__snake_case			,      __snake_case		:      Union[str, 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
														__snake_case		:      Union[str, Any]										=       image_processing(lowerCamelCase   ,     return_tensors="pt"    ).pixel_values

														__snake_case			,      __snake_case		:      Optional[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    __snake_case (							self	:  Optional[Any]    )     ->   Union[str, Any]:
														# Initialize image_processings
														__snake_case		:      Any										=       self.image_processing_class(**self.image_processor_dict    )
														__snake_case		:      Optional[Any]										=       self.image_processing_class(do_resize=lowerCamelCase   ,     do_normalize=lowerCamelCase   ,     do_rescale=lowerCamelCase    )
														# create random PyTorch tensors
														__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 whether the method "pad" and calling the image processor return the same tensors
														__snake_case		:      Optional[Any]										=       image_processing_a.pad(lowerCamelCase   ,     return_tensors="pt"    )
														__snake_case		:      Optional[Any]										=       image_processing_a(lowerCamelCase   ,     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	:  Optional[Any]    )     ->   int:
														# prepare image and target
														__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:
																					__snake_case		:      Any										=       json.loads(f.read()    )

														__snake_case		:      List[str]										=       {"image_id": 39769, "annotations": target}

														# encode them
														__snake_case		:      Union[str, Any]										=       YolosImageProcessor.from_pretrained("hustvl/yolos-small"    )
														__snake_case		:      Union[str, Any]										=       image_processing(images=lowerCamelCase   ,     annotations=lowerCamelCase   ,     return_tensors="pt"    )

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

														__snake_case		:      Optional[int]										=       torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
														self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )

														# verify area
														__snake_case		:      Optional[int]										=       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,     lowerCamelCase    )    )
														# verify boxes
														__snake_case		:      List[Any]										=       torch.Size([6, 4]    )
														self.assertEqual(encoding["labels"][0]["boxes"].shape   ,     lowerCamelCase    )
														__snake_case		:      int										=       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,     lowerCamelCase   ,     atol=1E-3    )    )
														# verify image_id
														__snake_case		:      Dict										=       torch.tensor([39769]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,     lowerCamelCase    )    )
														# verify is_crowd
														__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
														__snake_case		:      Optional[int]										=       torch.tensor([75, 75, 63, 65, 17, 17]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,     lowerCamelCase    )    )
														# verify orig_size
														__snake_case		:      str										=       torch.tensor([480, 640]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,     lowerCamelCase    )    )
														# verify size
														__snake_case		:      Tuple										=       torch.tensor([800, 1066]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,     lowerCamelCase    )    )






							@slow
							def    __snake_case (							self	:  List[str]    )     ->   Tuple:
														# prepare image, target and masks_path
														__snake_case		:      int										=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
														with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,     "r"    ) as f:
																					__snake_case		:      Tuple										=       json.loads(f.read()    )

														__snake_case		:      List[Any]										=       {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

														__snake_case		:      List[str]										=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

														# encode them
														__snake_case		:      Any										=       YolosImageProcessor(format="coco_panoptic"    )
														__snake_case		:      Union[str, Any]										=       image_processing(images=lowerCamelCase   ,     annotations=lowerCamelCase   ,     masks_path=lowerCamelCase   ,     return_tensors="pt"    )

														# verify pixel values
														__snake_case		:      List[str]										=       torch.Size([1, 3, 800, 1066]    )
														self.assertEqual(encoding["pixel_values"].shape   ,     lowerCamelCase    )

														__snake_case		:      str										=       torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
														self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )

														# verify area
														__snake_case		:      List[Any]										=       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,     lowerCamelCase    )    )
														# verify boxes
														__snake_case		:      str										=       torch.Size([6, 4]    )
														self.assertEqual(encoding["labels"][0]["boxes"].shape   ,     lowerCamelCase    )
														__snake_case		:      int										=       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,     lowerCamelCase   ,     atol=1E-3    )    )
														# verify image_id
														__snake_case		:      List[Any]										=       torch.tensor([39769]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,     lowerCamelCase    )    )
														# verify is_crowd
														__snake_case		:      List[Any]										=       torch.tensor([0, 0, 0, 0, 0, 0]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,     lowerCamelCase    )    )
														# verify class_labels
														__snake_case		:      str										=       torch.tensor([17, 17, 63, 75, 75, 93]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,     lowerCamelCase    )    )
														# verify masks
														__snake_case		:      List[str]										=       822873
														self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,     lowerCamelCase    )
														# verify orig_size
														__snake_case		:      List[Any]										=       torch.tensor([480, 640]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,     lowerCamelCase    )    )
														# verify size
														__snake_case		:      List[str]										=       torch.tensor([800, 1066]    )
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,     lowerCamelCase    )    )





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

