
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   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



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





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

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

										return expected_height, expected_width





@require_torch
@require_vision
class   UpperCamelCase_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Tuple				   =					YolosImageProcessor if is_vision_available() else None





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Optional[int]:
										lowercase         =      YolosImageProcessingTester(self     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->int:
										return self.image_processor_tester.prepare_image_processor_dict()





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Union[str, Any]:
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Dict:
										lowercase         =      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__     )

										lowercase         =      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							:str     )  ->Dict:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Any:
										# Initialize image_processing
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										lowercase         =      prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase__     )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase__		,    Image.Image     )

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

										lowercase     ,	lowercase         =      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
										lowercase     ,	lowercase         =      self.image_processor_tester.get_expected_values(lowerCAmelCase__		,    batched=lowerCAmelCase__     )

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

										lowercase     ,	lowercase         =      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
										lowercase         =      image_processing(lowerCAmelCase__		,    return_tensors="pt"     ).pixel_values

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

										lowercase     ,	lowercase         =      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
										lowercase         =      image_processing(lowerCAmelCase__		,    return_tensors="pt"     ).pixel_values

										lowercase     ,	lowercase         =      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							:Tuple     )  ->Tuple:
										# Initialize image_processings
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										lowercase         =      self.image_processing_class(do_resize=lowerCAmelCase__		,    do_normalize=lowerCAmelCase__		,    do_rescale=lowerCAmelCase__     )
										# create random PyTorch tensors
										lowercase         =      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
										lowercase         =      image_processing_a.pad(lowerCAmelCase__		,    return_tensors="pt"     )
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:str     )  ->Tuple:
										# prepare image and target
										lowercase         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,    "r"     ) as f:
														lowercase         =      json.loads(f.read()     )

										lowercase         =      {"image_id": 39769, "annotations": target}

										# encode them
										lowercase         =      YolosImageProcessor.from_pretrained("hustvl/yolos-small"     )
										lowercase         =      image_processing(images=lowerCAmelCase__		,    annotations=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      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
										lowercase         =      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
										lowercase         =      torch.Size([6, 4]     )
										self.assertEqual(encoding["labels"][0]["boxes"].shape		,    lowerCAmelCase__     )
										lowercase         =      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
										lowercase         =      torch.tensor([39769]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,    lowerCAmelCase__     )     )
										# verify is_crowd
										lowercase         =      torch.tensor([0, 0, 0, 0, 0, 0]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,    lowerCAmelCase__     )     )
										# verify class_labels
										lowercase         =      torch.tensor([75, 75, 63, 65, 17, 17]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,    lowerCAmelCase__     )     )
										# verify orig_size
										lowercase         =      torch.tensor([480, 640]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,    lowerCAmelCase__     )     )
										# verify size
										lowercase         =      torch.tensor([800, 1066]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,    lowerCAmelCase__     )     )





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

										lowercase         =      {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

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

										# encode them
										lowercase         =      YolosImageProcessor(format="coco_panoptic"     )
										lowercase         =      image_processing(images=lowerCAmelCase__		,    annotations=lowerCAmelCase__		,    masks_path=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      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
										lowercase         =      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
										lowercase         =      torch.Size([6, 4]     )
										self.assertEqual(encoding["labels"][0]["boxes"].shape		,    lowerCAmelCase__     )
										lowercase         =      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
										lowercase         =      torch.tensor([39769]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,    lowerCAmelCase__     )     )
										# verify is_crowd
										lowercase         =      torch.tensor([0, 0, 0, 0, 0, 0]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,    lowerCAmelCase__     )     )
										# verify class_labels
										lowercase         =      torch.tensor([17, 17, 63, 75, 75, 93]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,    lowerCAmelCase__     )     )
										# verify masks
										lowercase         =      822873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,    lowerCAmelCase__     )
										# verify orig_size
										lowercase         =      torch.tensor([480, 640]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,    lowerCAmelCase__     )     )
										# verify size
										lowercase         =      torch.tensor([800, 1066]     )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,    lowerCAmelCase__     )     )


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							List[str]     	=    {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					'''deberta-v2'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Union[str, Any]=128100		,    lowerCAmelCase__							:Optional[Any]=1536		,    lowerCAmelCase__							:Tuple=24		,    lowerCAmelCase__							:List[Any]=24		,    lowerCAmelCase__							:Union[str, Any]=6144		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:List[str]=512		,    lowerCAmelCase__							:Optional[int]=0		,    lowerCAmelCase__							:Optional[int]=0.02		,    lowerCAmelCase__							:int=1E-7		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:List[Any]=-1		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[Any]=0		,    lowerCAmelCase__							:Union[str, Any]="gelu"		,    **lowerCAmelCase__							:str		,    )  ->Optional[Any]:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      initializer_range
										lowercase         =      relative_attention
										lowercase         =      max_relative_positions
										lowercase         =      pad_token_id
										lowercase         =      position_biased_input

										# Backwards compatibility
										if type(lowerCAmelCase__     ) == str:
														lowercase         =      [x.strip() for x in pos_att_type.lower().split("|"     )]

										lowercase         =      pos_att_type
										lowercase         =      vocab_size
										lowercase         =      layer_norm_eps

										lowercase         =      kwargs.get("pooler_hidden_size"		,    lowerCAmelCase__     )
										lowercase         =      pooler_dropout
										lowercase         =      pooler_hidden_act





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowercase         =      {0: "batch", 1: "choice", 2: "sequence"}
										else:
														lowercase         =      {0: "batch", 1: "sequence"}
										if self._config.type_vocab_size > 0:
														return OrderedDict(
														    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]     )
										else:
														return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return 12





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional["TensorType"] = None		,    lowerCAmelCase__							:int = 3		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:"PreTrainedTokenizerBase" = None		,    )  ->Mapping[str, Any]:
										lowercase         =      super().generate_dummy_inputs(preprocessor=lowerCAmelCase__		,    framework=lowerCAmelCase__     )
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
														del dummy_inputs["token_type_ids"]
										return dummy_inputs
