


'''simple docstring'''

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image

def _SCREAMING_SNAKE_CASE				(A  ,			A=False  ,			A=False  ,			A=False	)	->	int:






       """simple docstring"""







       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       lowercase__				      =			[]
       for i in range(image_processor_tester.batch_size	):
              if equal_resolution:
                     lowercase__				      =			lowercase__				      =			image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     lowercase__				      =			image_processor_tester.min_resolution
                     if getattr(A  ,			'''size_divisor'''  ,			A	):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            lowercase__				      =			max(image_processor_tester.size_divisor  ,			A	)
                     lowercase__      ,lowercase__				      =			np.random.choice(np.arange(A  ,			image_processor_tester.max_resolution	)  ,			2	)
              image_inputs.append(
                  np.random.randint(255  ,			size=(image_processor_tester.num_channels, width, height)  ,			dtype=np.uinta	)	)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              lowercase__				      =			[Image.fromarray(np.moveaxis(A  ,			0  ,			-1	)	) for image in image_inputs]

       if torchify:
              lowercase__				      =			[torch.from_numpy(A	) for image in image_inputs]

       return image_inputs

def _SCREAMING_SNAKE_CASE				(A  ,			A=10  ,			A=10  ,			A=False  ,			A=False	)	->	Any:






       """simple docstring"""







       lowercase__				      =			[]
       for i in range(image_processor_tester.num_frames	):
              video.append(np.random.randint(255  ,			size=(image_processor_tester.num_channels, width, height)  ,			dtype=np.uinta	)	)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              lowercase__				      =			[Image.fromarray(np.moveaxis(A  ,			0  ,			-1	)	) for frame in video]

       if torchify:
              lowercase__				      =			[torch.from_numpy(A	) for frame in video]

       return video

def _SCREAMING_SNAKE_CASE				(A  ,			A=False  ,			A=False  ,			A=False	)	->	Dict:






       """simple docstring"""







       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       lowercase__				      =			[]
       for i in range(image_processor_tester.batch_size	):
              if equal_resolution:
                     lowercase__				      =			lowercase__				      =			image_processor_tester.max_resolution
              else:
                     lowercase__      ,lowercase__				      =			np.random.choice(
                         np.arange(image_processor_tester.min_resolution  ,			image_processor_tester.max_resolution	)  ,			2	)
                     lowercase__				      =			prepare_video(
                         image_processor_tester=A  ,			width=A  ,			height=A  ,			numpify=A  ,			torchify=A  ,			)
              video_inputs.append(A	)

       return video_inputs

class   __lowerCAmelCase						:





      '''simple docstring'''






      lowerCAmelCase__ :					List[Any]   =							None



      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             lowercase__				      =			self.image_processing_class(**self.image_processor_dict			)
             lowercase__				      =			json.loads(image_processor.to_json_string()			)
             for key, value in self.image_processor_dict.items():
                    self.assertEqual(obj[key]       ,						UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.image_processing_class(**self.image_processor_dict			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    lowercase__				      =			os.path.join(UpperCamelCase       ,						'''image_processor.json'''			)
                    image_processor_first.to_json_file(UpperCamelCase			)
                    lowercase__				      =			self.image_processing_class.from_json_file(UpperCamelCase			)

             self.assertEqual(image_processor_second.to_dict()       ,						image_processor_first.to_dict()			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             lowercase__				      =			self.image_processing_class(**self.image_processor_dict			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    lowercase__				      =			image_processor_first.save_pretrained(UpperCamelCase			)[0]
                    check_json_file_has_correct_format(UpperCamelCase			)
                    lowercase__				      =			self.image_processing_class.from_pretrained(UpperCamelCase			)

             self.assertEqual(image_processor_second.to_dict()       ,						image_processor_first.to_dict()			)



      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.image_processing_class()
             self.assertIsNotNone(UpperCamelCase			)






      @require_torch
      @require_vision
      def 				UpperCamelCase__		(self   :   Union[str, Any]			):

             '''simple docstring'''







             if self.test_cast_dtype is not None:
                    # Initialize image_processor
                    lowercase__				      =			self.image_processing_class(**self.image_processor_dict			)

                    # create random PyTorch tensors
                    lowercase__				      =			prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=UpperCamelCase       ,						torchify=UpperCamelCase			)

                    lowercase__				      =			image_processor(UpperCamelCase       ,						return_tensors='''pt'''			)
                    # for layoutLM compatiblity
                    self.assertEqual(encoding.pixel_values.device       ,						torch.device('''cpu'''			)			)
                    self.assertEqual(encoding.pixel_values.dtype       ,						torch.floataa			)

                    lowercase__				      =			image_processor(UpperCamelCase       ,						return_tensors='''pt'''			).to(torch.floataa			)
                    self.assertEqual(encoding.pixel_values.device       ,						torch.device('''cpu'''			)			)
                    self.assertEqual(encoding.pixel_values.dtype       ,						torch.floataa			)

                    lowercase__				      =			image_processor(UpperCamelCase       ,						return_tensors='''pt'''			).to('''cpu'''       ,						torch.bfloataa			)
                    self.assertEqual(encoding.pixel_values.device       ,						torch.device('''cpu'''			)			)
                    self.assertEqual(encoding.pixel_values.dtype       ,						torch.bfloataa			)

                    with self.assertRaises(UpperCamelCase			):
                           lowercase__				      =			image_processor(UpperCamelCase       ,						return_tensors='''pt'''			).to(torch.bfloataa       ,						'''cpu'''			)

                    # Try with text + image feature
                    lowercase__				      =			image_processor(UpperCamelCase       ,						return_tensors='''pt'''			)
                    encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]			)}			)
                    lowercase__				      =			encoding.to(torch.floataa			)

                    self.assertEqual(encoding.pixel_values.device       ,						torch.device('''cpu'''			)			)
                    self.assertEqual(encoding.pixel_values.dtype       ,						torch.floataa			)
                    self.assertEqual(encoding.input_ids.dtype       ,						torch.long			)








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

             lowercase__				      =			{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             self._test_save_load_optional_components()



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()       ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,						)
      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             super().test_save_load_floataa(expected_max_diff=1E-1			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             super().test_floataa_inference(expected_max_diff=1E-1			)



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             self._test_inference_batch_single_identical(
                 expected_max_diff=1E-2       ,						)




