




'''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 DetrImageProcessor
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def __init__(	self     :						List[str]      ,       _lowercase     :						Any      ,       _lowercase     :						List[Any]=7      ,       _lowercase     :						List[Any]=3      ,       _lowercase     :						Dict=30      ,       _lowercase     :						Optional[int]=400      ,       _lowercase     :						Union[str, Any]=True      ,       _lowercase     :						str=None      ,       _lowercase     :						List[Any]=True      ,       _lowercase     :						Optional[Any]=1 / 255      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Dict=[0.5, 0.5, 0.5]      ,       _lowercase     :						List[str]=[0.5, 0.5, 0.5]      ,       _lowercase     :						Optional[int]=True      ,       )    ->							Dict:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								A_									= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
								A_									= parent
								A_									= batch_size
								A_									= num_channels
								A_									= min_resolution
								A_									= max_resolution
								A_									= do_resize
								A_									= size
								A_									= do_rescale
								A_									= rescale_factor
								A_									= do_normalize
								A_									= image_mean
								A_									= image_std
								A_									= do_pad



					def     __snake_case						(	self     :						Dict)    ->							Optional[int]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_pad": self.do_pad,
								}



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

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

								return expected_height, expected_width





@require_torch
@require_vision
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		DetrImageProcessor if is_vision_available() else None



					def     __snake_case						(	self     :						Union[str, Any])    ->							List[Any]:
								A_									= DetrImageProcessingTester(self)



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



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase      ,       'image_mean'))
								self.assertTrue(hasattr(_lowercase      ,       'image_std'))
								self.assertTrue(hasattr(_lowercase      ,       'do_normalize'))
								self.assertTrue(hasattr(_lowercase      ,       'do_rescale'))
								self.assertTrue(hasattr(_lowercase      ,       'rescale_factor'))
								self.assertTrue(hasattr(_lowercase      ,       'do_resize'))
								self.assertTrue(hasattr(_lowercase      ,       'size'))
								self.assertTrue(hasattr(_lowercase      ,       'do_pad'))



					def     __snake_case						(	self     :						Tuple)    ->							List[str]:
								A_									= self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size      ,       {'shortest_edge': 18, 'longest_edge': 1_333})
								self.assertEqual(image_processor.do_pad      ,       _lowercase)

								A_									= self.image_processing_class.from_dict(
								    self.image_processor_dict      ,       size=42      ,       max_size=84      ,       pad_and_return_pixel_mask=_lowercase)
								self.assertEqual(image_processor.size      ,       {'shortest_edge': 42, 'longest_edge': 84})
								self.assertEqual(image_processor.do_pad      ,       _lowercase)



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



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

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

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

								# Test batched
								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

								A_									= image_processing(_lowercase      ,       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     :						Tuple)    ->							Optional[int]:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       np.ndarray)

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

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

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

								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     :						str)    ->							str:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       torch.Tensor)

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

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase)

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

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values

								A_		, A_									= self.image_processor_tester.get_expected_values(_lowercase      ,       batched=_lowercase)

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



					@slow
					def     __snake_case						(	self     :						Union[str, Any])    ->							str:
								# prepare image and target
								A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
								with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,       'r') as f:
											A_									= json.loads(f.read())

								A_									= {'image_id': 39_769, 'annotations': target}

								# encode them
								A_									= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
								A_									= image_processing(images=_lowercase      ,       annotations=_lowercase      ,       return_tensors='pt')

								# verify pixel values
								A_									= torch.Size([1, 3, 800, 1_066])
								self.assertEqual(encoding['pixel_values'].shape      ,       _lowercase)

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

								# verify area
								A_									= 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']      ,       _lowercase))
								# verify boxes
								A_									= torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       _lowercase)
								A_									= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       _lowercase      ,       atol=1E-3))
								# verify image_id
								A_									= torch.tensor([39_769])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       _lowercase))
								# verify is_crowd
								A_									= torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       _lowercase))
								# verify class_labels
								A_									= torch.tensor([75, 75, 63, 65, 17, 17])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       _lowercase))
								# verify orig_size
								A_									= torch.tensor([480, 640])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       _lowercase))
								# verify size
								A_									= torch.tensor([800, 1_066])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       _lowercase))



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

								A_									= {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

								A_									= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

								# encode them
								A_									= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic')
								A_									= image_processing(images=_lowercase      ,       annotations=_lowercase      ,       masks_path=_lowercase      ,       return_tensors='pt')

								# verify pixel values
								A_									= torch.Size([1, 3, 800, 1_066])
								self.assertEqual(encoding['pixel_values'].shape      ,       _lowercase)

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

								# verify area
								A_									= 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']      ,       _lowercase))
								# verify boxes
								A_									= torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       _lowercase)
								A_									= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       _lowercase      ,       atol=1E-3))
								# verify image_id
								A_									= torch.tensor([39_769])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       _lowercase))
								# verify is_crowd
								A_									= torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       _lowercase))
								# verify class_labels
								A_									= torch.tensor([17, 17, 63, 75, 75, 93])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       _lowercase))
								# verify masks
								A_									= 822_873
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,       _lowercase)
								# verify orig_size
								A_									= torch.tensor([480, 640])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       _lowercase))
								# verify size
								A_									= torch.tensor([800, 1_066])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       _lowercase))










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""openai-gpt"""
					_UpperCamelCase							=		{
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}



					def __init__(	self     :						Any      ,       _lowercase     :						List[Any]=40_478      ,       _lowercase     :						Tuple=512      ,       _lowercase     :						Dict=768      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Optional[int]=12      ,       _lowercase     :						Tuple="gelu"      ,       _lowercase     :						int=0.1      ,       _lowercase     :						Tuple=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						List[str]=1E-5      ,       _lowercase     :						str=0.02      ,       _lowercase     :						Optional[Any]="cls_index"      ,       _lowercase     :						int=True      ,       _lowercase     :						str=None      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						List[Any]=0.1      ,       **_lowercase     :						int      ,       )    ->							Optional[int]:
								A_									= vocab_size
								A_									= n_positions
								A_									= n_embd
								A_									= n_layer
								A_									= n_head
								A_									= afn
								A_									= resid_pdrop
								A_									= embd_pdrop
								A_									= attn_pdrop
								A_									= layer_norm_epsilon
								A_									= initializer_range
								A_									= summary_type
								A_									= summary_use_proj
								A_									= summary_activation
								A_									= summary_first_dropout
								A_									= summary_proj_to_labels
								super().__init__(**_lowercase)




