


'''simple docstring'''



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 ):




	def __init__(       self						,    __a						,    __a=7						,    __a=3						,    __a=30						,    __a=400						,    __a=True						,    __a=None						,    __a=True						,    __a=[0.5, 0.5, 0.5]						,    __a=[0.5, 0.5, 0.5]						,    __a=True						,    __a=1 / 255						,    __a=True						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
						__a      :	str    =  parent
						__a      :	Union[str, Any]    =  batch_size
						__a      :	List[Any]    =  num_channels
						__a      :	Dict    =  min_resolution
						__a      :	Tuple    =  max_resolution
						__a      :	Any    =  do_resize
						__a      :	Optional[Any]    =  size
						__a      :	int    =  do_normalize
						__a      :	List[str]    =  image_mean
						__a      :	Union[str, Any]    =  image_std
						__a      :	Dict    =  do_rescale
						__a      :	List[str]    =  rescale_factor
						__a      :	str    =  do_pad




	def 				__UpperCAmelCase							(       self							):

						'''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						,    __a						,    __a=False							):

						'''simple docstring'''





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

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

						return expected_height, expected_width





@require_torch
@require_vision
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   YolosImageProcessor if is_vision_available() else None




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  YolosImageProcessingTester(self							)




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__a						,    'image_mean'							)							)
						self.assertTrue(hasattr(__a						,    'image_std'							)							)
						self.assertTrue(hasattr(__a						,    'do_normalize'							)							)
						self.assertTrue(hasattr(__a						,    'do_resize'							)							)
						self.assertTrue(hasattr(__a						,    'size'							)							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[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							)

						__a      :	List[Any]    =  self.image_processing_class.from_dict(
						    self.image_processor_dict						,    size=42						,    max_size=84						,    pad_and_return_pixel_mask=__a							)
						self.assertEqual(image_processor.size						,    {'shortest_edge': 42, 'longest_edge': 84}							)
						self.assertEqual(image_processor.do_pad						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__a      :	Dict    =  prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,    Image.Image							)

						# Test not batched input
						__a      :	Optional[Any]    =  image_processing(image_inputs[0]						,    return_tensors='pt'							).pixel_values

						__a   ,    __a      :	int    =  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
						__a   ,    __a      :	Any    =  self.image_processor_tester.get_expected_values(__a						,    batched=__a							)

						__a      :	Union[str, Any]    =  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							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__a      :	List[Any]    =  prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__a						,    numpify=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,    np.ndarray							)

						# Test not batched input
						__a      :	Tuple    =  image_processing(image_inputs[0]						,    return_tensors='pt'							).pixel_values

						__a   ,    __a      :	str    =  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
						__a      :	Optional[int]    =  image_processing(__a						,    return_tensors='pt'							).pixel_values

						__a   ,    __a      :	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							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__a      :	Tuple    =  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
						__a      :	Dict    =  image_processing(image_inputs[0]						,    return_tensors='pt'							).pixel_values

						__a   ,    __a      :	Optional[int]    =  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
						__a      :	Tuple    =  image_processing(__a						,    return_tensors='pt'							).pixel_values

						__a   ,    __a      :	Tuple    =  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							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.image_processing_class(**self.image_processor_dict							)
						__a      :	Optional[Any]    =  self.image_processing_class(do_resize=__a						,    do_normalize=__a						,    do_rescale=__a							)
						# create random PyTorch tensors
						__a      :	List[str]    =  prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__a						,    torchify=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,    torch.Tensor							)

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

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




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,    'r'							) as f:
											__a      :	Optional[Any]    =  json.loads(f.read()							)

						__a      :	str    =  {'image_id': 3_9769, 'annotations': target}

						# encode them
						__a      :	int    =  YolosImageProcessor.from_pretrained('hustvl/yolos-small'							)
						__a      :	List[Any]    =  image_processing(images=__a						,    annotations=__a						,    return_tensors='pt'							)

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

						__a      :	Optional[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
						__a      :	Any    =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,    __a							)							)
						# verify boxes
						__a      :	Union[str, Any]    =  torch.Size([6, 4]							)
						self.assertEqual(encoding['labels'][0]['boxes'].shape						,    __a							)
						__a      :	str    =  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
						__a      :	Union[str, Any]    =  torch.tensor([3_9769]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,    __a							)							)
						# verify is_crowd
						__a      :	str    =  torch.tensor([0, 0, 0, 0, 0, 0]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,    __a							)							)
						# verify class_labels
						__a      :	str    =  torch.tensor([75, 75, 63, 65, 17, 17]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,    __a							)							)
						# verify orig_size
						__a      :	Any    =  torch.tensor([480, 640]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,    __a							)							)
						# verify size
						__a      :	Dict    =  torch.tensor([800, 1066]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,    __a							)							)





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,    'r'							) as f:
											__a      :	Optional[Any]    =  json.loads(f.read()							)

						__a      :	Dict    =  {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

						__a      :	Optional[int]    =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

						# encode them
						__a      :	int    =  YolosImageProcessor(format='coco_panoptic'							)
						__a      :	Union[str, Any]    =  image_processing(images=__a						,    annotations=__a						,    masks_path=__a						,    return_tensors='pt'							)

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

						__a      :	Dict    =  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
						__a      :	Optional[int]    =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,    __a							)							)
						# verify boxes
						__a      :	List[str]    =  torch.Size([6, 4]							)
						self.assertEqual(encoding['labels'][0]['boxes'].shape						,    __a							)
						__a      :	Union[str, Any]    =  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
						__a      :	Optional[int]    =  torch.tensor([3_9769]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,    __a							)							)
						# verify is_crowd
						__a      :	Union[str, Any]    =  torch.tensor([0, 0, 0, 0, 0, 0]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,    __a							)							)
						# verify class_labels
						__a      :	Dict    =  torch.tensor([17, 17, 63, 75, 75, 93]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,    __a							)							)
						# verify masks
						__a      :	Optional[Any]    =  82_2873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,    __a							)
						# verify orig_size
						__a      :	Optional[Any]    =  torch.tensor([480, 640]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,    __a							)							)
						# verify size
						__a      :	str    =  torch.tensor([800, 1066]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,    __a							)							)







'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   None
	A_           =   None
	A_           =   None
	A_           =   None





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a=1						,    __a=0						,    __a=2						,    __a=512						,    __a="cls"						,    __a=False						,    __a=True						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a						,    **__a							)
						__a      :	Any    =  project_dim
						__a      :	Optional[Any]    =  pooler_fn
						__a      :	int    =  learn_encoder
						__a      :	str    =  use_attention_mask





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   [r"pooler", r"logit_scale"]
	A_           =   [r"position_ids", r"predictions.decoder.bias"]
	A_           =   "roberta"
	A_           =   RobertaSeriesConfig




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(__a							)
						__a      :	Optional[Any]    =  XLMRobertaModel(__a							)
						__a      :	str    =  nn.Linear(config.hidden_size						,    config.project_dim							)
						__a      :	Optional[int]    =  getattr(__a						,    'has_pre_transformation'						,    __a							)
						if self.has_pre_transformation:
											__a      :	int    =  nn.Linear(config.hidden_size						,    config.project_dim							)
											__a      :	List[str]    =  nn.LayerNorm(config.hidden_size						,    eps=config.layer_norm_eps							)
						self.post_init()





	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Tuple    =  self.base_model(
						    input_ids=__a						,    attention_mask=__a						,    token_type_ids=__a						,    position_ids=__a						,    head_mask=__a						,    inputs_embeds=__a						,    encoder_hidden_states=__a						,    encoder_attention_mask=__a						,    output_attentions=__a						,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states						,    return_dict=__a						,    )

						if self.has_pre_transformation:
											__a      :	Optional[Any]    =  outputs['hidden_states'][-2]
											__a      :	Optional[int]    =  self.pre_LN(__a							)
											__a      :	Union[str, Any]    =  self.transformation_pre(__a							)

											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )
						else:
											__a      :	Optional[Any]    =  self.transformation(outputs.last_hidden_state							)
											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )



