import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible



class        snake_case__							(					unittest.TestCase				):



   def        UpperCAmelCase__							(					self : List[str] )				->  Any:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[
             "safety_checker/pytorch_model.bin",
             "safety_checker/model.safetensors",
             "vae/diffusion_pytorch_model.bin",
             "vae/diffusion_pytorch_model.safetensors",
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Tuple )				->  int:




         '''simple docstring'''




         snake_case_  :      Tuple  	=							[
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : int )				->  List[str]:




         '''simple docstring'''




         snake_case_  :      Optional[Any]  	=							[
             "safety_checker/pytorch_model.bin",
             "safety_checker/model.safetensors",
             "vae/diffusion_pytorch_model.bin",
             "vae/diffusion_pytorch_model.safetensors",
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
             "unet/diffusion_pytorch_model.bin",
             # Removed: 'unet/diffusion_pytorch_model.safetensors',
         ]
         self.assertFalse(is_safetensors_compatible(_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  List[str]:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
         ]
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Optional[int] )				->  List[str]:




         '''simple docstring'''




         snake_case_  :      List[Any]  	=							[
             "safety_checker/pytorch_model.bin",
             "safety_checker/model.safetensors",
             "vae/diffusion_pytorch_model.bin",
             "vae/diffusion_pytorch_model.safetensors",
             "text_encoder/pytorch_model.bin",
             # Removed: 'text_encoder/model.safetensors',
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         self.assertFalse(is_safetensors_compatible(_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  Tuple:




         '''simple docstring'''




         snake_case_  :      Any  	=							[
             "safety_checker/pytorch_model.fp16.bin",
             "safety_checker/model.fp16.safetensors",
             "vae/diffusion_pytorch_model.fp16.bin",
             "vae/diffusion_pytorch_model.fp16.safetensors",
             "text_encoder/pytorch_model.fp16.bin",
             "text_encoder/model.fp16.safetensors",
             "unet/diffusion_pytorch_model.fp16.bin",
             "unet/diffusion_pytorch_model.fp16.safetensors",
         ]
         snake_case_  :      Optional[int]  	=							"fp16"
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : List[Any] )				->  int:




         '''simple docstring'''




         snake_case_  :      List[str]  	=							[
             "unet/diffusion_pytorch_model.fp16.bin",
             "unet/diffusion_pytorch_model.fp16.safetensors",
         ]
         snake_case_  :      List[str]  	=							"fp16"
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Dict )				->  Any:




         '''simple docstring'''




         snake_case_  :      Tuple  	=							[
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         snake_case_  :      Any  	=							"fp16"
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Tuple )				->  Optional[Any]:




         '''simple docstring'''




         snake_case_  :      Union[str, Any]  	=							[
             "safety_checker/pytorch_model.fp16.bin",
             "safety_checker/model.fp16.safetensors",
             "vae/diffusion_pytorch_model.fp16.bin",
             "vae/diffusion_pytorch_model.fp16.safetensors",
             "text_encoder/pytorch_model.fp16.bin",
             "text_encoder/model.fp16.safetensors",
             "unet/diffusion_pytorch_model.fp16.bin",
             # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
         ]
         snake_case_  :      List[str]  	=							"fp16"
         self.assertFalse(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  Any:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[
             "text_encoder/pytorch_model.fp16.bin",
             "text_encoder/model.fp16.safetensors",
         ]
         snake_case_  :      int  	=							"fp16"
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



   def        UpperCAmelCase__							(					self : List[str] )				->  str:




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
         ]
         snake_case_  :      Any  	=							"fp16"
         self.assertTrue(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )



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




         '''simple docstring'''




         snake_case_  :      Optional[int]  	=							[
             "safety_checker/pytorch_model.fp16.bin",
             "safety_checker/model.fp16.safetensors",
             "vae/diffusion_pytorch_model.fp16.bin",
             "vae/diffusion_pytorch_model.fp16.safetensors",
             "text_encoder/pytorch_model.fp16.bin",
             # 'text_encoder/model.fp16.safetensors',
             "unet/diffusion_pytorch_model.fp16.bin",
             "unet/diffusion_pytorch_model.fp16.safetensors",
         ]
         snake_case_  :      int  	=							"fp16"
         self.assertFalse(is_safetensors_compatible(_UpperCamelCase      ,						variant=_UpperCamelCase ) )







'''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 DeformableDetrImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=True			,     _UpperCamelCase=1 / 255			,     _UpperCamelCase=True			,     )->      Tuple:
            # 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': 1333}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							do_resize
            _A				=							size
            _A				=							do_normalize
            _A				=							image_mean
            _A				=							image_std
            _A				=							do_rescale
            _A				=							rescale_factor
            _A				=							do_pad






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            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			,     _UpperCamelCase			,     _UpperCamelCase=False   )->      List[str]:
            if not batched:
                 _A				=							image_inputs[0]
                 if isinstance(_UpperCamelCase			,     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(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[0]   )[0]
                 _A				=							max(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[1]   )[1]

            return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DeformableDetrImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							DeformableDetrImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'image_mean'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'image_std'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_resize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_rescale'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_pad'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'size'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							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			,     _UpperCamelCase   )

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






       def   UpperCamelCase						( self   )->      Optional[Any]:
            pass






       def   UpperCamelCase						( self   )->      Optional[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=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

            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(_UpperCamelCase			,     batched=_UpperCamelCase   )

            _A				=							image_processing(_UpperCamelCase			,     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   )->      List[str]:
            # 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=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

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

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase			,     batched=_UpperCamelCase   )

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






       def   UpperCamelCase						( self   )->      Dict:
            # 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=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

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

            # Test batched
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							self.image_processor_tester.get_expected_values(_UpperCamelCase			,     batched=_UpperCamelCase   )

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






       @slow
       def   UpperCamelCase						( self   )->      List[Any]:
            # 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': 3_9769, 'annotations': target}

            # encode them
            _A				=							DeformableDetrImageProcessor()
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     return_tensors='pt'   )

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

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

            # verify area
            _A				=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )






       @slow
       def   UpperCamelCase						( self   )->      Any:
            # 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': 3_9769, 'segments_info': target}

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

            # encode them
            _A				=							DeformableDetrImageProcessor(format='coco_panoptic'   )
            _A				=							image_processing(images=_UpperCamelCase			,     annotations=_UpperCamelCase			,     masks_path=_UpperCamelCase			,     return_tensors='pt'   )

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

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

            # verify area
            _A				=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify masks
            _A				=							82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,     _UpperCamelCase   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )





