
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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=3_0    ,    __SCREAMING_SNAKE_CASE=4_0_0    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,    __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=1 / 2_5_5    ,    __SCREAMING_SNAKE_CASE=True    ,    )	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
           UpperCamelCase__     : Any											=					parent
           UpperCamelCase__     : List[str]											=					batch_size
           UpperCamelCase__     : Tuple											=					num_channels
           UpperCamelCase__     : Union[str, Any]											=					min_resolution
           UpperCamelCase__     : Any											=					max_resolution
           UpperCamelCase__     : List[Any]											=					do_resize
           UpperCamelCase__     : Tuple											=					size
           UpperCamelCase__     : Tuple											=					do_normalize
           UpperCamelCase__     : int											=					image_mean
           UpperCamelCase__     : Tuple											=					image_std
           UpperCamelCase__     : str											=					do_rescale
           UpperCamelCase__     : Tuple											=					rescale_factor
           UpperCamelCase__     : Optional[Any]											=					do_pad






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False	)	-> Any:




           """simple docstring"""







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

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

           return expected_height, expected_width




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	DeformableDetrImageProcessor if is_vision_available() else None






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					DeformableDetrImageProcessingTester(self	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           return self.image_processor_tester.prepare_image_processor_dict()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}	)
           self.assertEqual(image_processor.do_pad    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					self.image_processing_class.from_dict(
               self.image_processor_dict    ,    size=4_2    ,    max_size=8_4    ,    pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}	)
           self.assertEqual(image_processor.do_pad    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           UpperCamelCase__     : Tuple											=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__SCREAMING_SNAKE_CASE	)
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    Image.Image	)

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

           UpperCamelCase__					,UpperCamelCase__     : Any											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

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

           # Test batched
           UpperCamelCase__					,UpperCamelCase__     : Tuple											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE    ,    batched=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					image_processing(__SCREAMING_SNAKE_CASE    ,    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	)	-> Any:




           """simple docstring"""







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

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

           UpperCamelCase__					,UpperCamelCase__     : str											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

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

           # Test batched
           UpperCamelCase__     : Tuple											=					image_processing(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	).pixel_values

           UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE    ,    batched=__SCREAMING_SNAKE_CASE	)

           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	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           UpperCamelCase__     : Any											=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__SCREAMING_SNAKE_CASE    ,    torchify=__SCREAMING_SNAKE_CASE	)
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    torch.Tensor	)

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

           UpperCamelCase__					,UpperCamelCase__     : Any											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

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

           # Test batched
           UpperCamelCase__     : Union[str, Any]											=					image_processing(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	).pixel_values

           UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE    ,    batched=__SCREAMING_SNAKE_CASE	)

           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	)	-> Union[str, Any]:




           """simple docstring"""







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

           UpperCamelCase__     : Optional[Any]											=					{'''image_id''': 3_9_7_6_9, '''annotations''': target}

           # encode them
           UpperCamelCase__     : Union[str, Any]											=					DeformableDetrImageProcessor()
           UpperCamelCase__     : Dict											=					image_processing(images=__SCREAMING_SNAKE_CASE    ,    annotations=__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)

           # verify pixel values
           UpperCamelCase__     : Any											=					torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
           self.assertEqual(encoding['''pixel_values'''].shape    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Any											=					torch.tensor([0.2796, 0.3138, 0.3481]	)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)

           # verify area
           UpperCamelCase__     : List[str]											=					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify boxes
           UpperCamelCase__     : List[Any]											=					torch.Size([6, 4]	)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)
           # verify image_id
           UpperCamelCase__     : Tuple											=					torch.tensor([3_9_7_6_9]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify is_crowd
           UpperCamelCase__     : int											=					torch.tensor([0, 0, 0, 0, 0, 0]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify class_labels
           UpperCamelCase__     : Optional[int]											=					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify orig_size
           UpperCamelCase__     : Tuple											=					torch.tensor([4_8_0, 6_4_0]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify size
           UpperCamelCase__     : Optional[int]											=					torch.tensor([8_0_0, 1_0_6_6]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,    __SCREAMING_SNAKE_CASE	)	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







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

           UpperCamelCase__     : List[Any]											=					{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

           UpperCamelCase__     : Any											=					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''	)

           # encode them
           UpperCamelCase__     : Dict											=					DeformableDetrImageProcessor(format='''coco_panoptic'''	)
           UpperCamelCase__     : Optional[int]											=					image_processing(images=__SCREAMING_SNAKE_CASE    ,    annotations=__SCREAMING_SNAKE_CASE    ,    masks_path=__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)

           # verify pixel values
           UpperCamelCase__     : Tuple											=					torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
           self.assertEqual(encoding['''pixel_values'''].shape    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					torch.tensor([0.2796, 0.3138, 0.3481]	)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)

           # verify area
           UpperCamelCase__     : List[Any]											=					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify boxes
           UpperCamelCase__     : Dict											=					torch.Size([6, 4]	)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)
           # verify image_id
           UpperCamelCase__     : List[str]											=					torch.tensor([3_9_7_6_9]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify is_crowd
           UpperCamelCase__     : int											=					torch.tensor([0, 0, 0, 0, 0, 0]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify class_labels
           UpperCamelCase__     : List[Any]											=					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify masks
           UpperCamelCase__     : List[str]											=					8_2_2_8_7_3
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,    __SCREAMING_SNAKE_CASE	)
           # verify orig_size
           UpperCamelCase__     : List[Any]											=					torch.tensor([4_8_0, 6_4_0]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,    __SCREAMING_SNAKE_CASE	)	)
           # verify size
           UpperCamelCase__     : Tuple											=					torch.tensor([8_0_0, 1_0_6_6]	)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,    __SCREAMING_SNAKE_CASE	)	)




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


lowerCamelCase  =logging.get_logger(__name__)

lowerCamelCase  ={
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''gpt_neox_japanese'''






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=3_2_0_0_0    ,    __SCREAMING_SNAKE_CASE=2_5_6_0    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=1.00    ,    __SCREAMING_SNAKE_CASE=1_0_0_0_0    ,    __SCREAMING_SNAKE_CASE=2_0_4_8    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=1e-5    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=3_1_9_9_6    ,    __SCREAMING_SNAKE_CASE=3_1_9_9_9    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.0    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> List[str]:




           """simple docstring"""







           super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,    eos_token_id=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					vocab_size
           UpperCamelCase__     : int											=					max_position_embeddings
           UpperCamelCase__     : Union[str, Any]											=					hidden_size
           UpperCamelCase__     : Any											=					num_hidden_layers
           UpperCamelCase__     : List[Any]											=					num_attention_heads
           UpperCamelCase__     : int											=					intermediate_multiple_size
           UpperCamelCase__     : List[Any]											=					hidden_act
           UpperCamelCase__     : Tuple											=					rotary_pct
           UpperCamelCase__     : Optional[int]											=					rotary_emb_base
           UpperCamelCase__     : Optional[Any]											=					initializer_range
           UpperCamelCase__     : Optional[int]											=					layer_norm_eps
           UpperCamelCase__     : Optional[Any]											=					use_cache
           UpperCamelCase__     : List[str]											=					attention_dropout
           UpperCamelCase__     : Optional[int]											=					hidden_dropout


