
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 ViTImageProcessor


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       List[Any]=13				,       _snake_case		:       Any=3				,       _snake_case		:       Any=224				,       _snake_case		:       Union[str, Any]=30				,       _snake_case		:       List[str]=400				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       Optional[Any]=None				,       _snake_case		:       List[Any]=True				,       _snake_case		:       List[str]=[0.5, 0.5, 0.5]				,       _snake_case		:       Optional[Any]=[0.5, 0.5, 0.5]				,       ):
          __lowercase		:			int	    =       size if size is not None else {'''height''': 18, '''width''': 18}
          __lowercase		:			Any	    =       parent
          __lowercase		:			Dict	    =       batch_size
          __lowercase		:			Any	    =       num_channels
          __lowercase		:			Any	    =       image_size
          __lowercase		:			Dict	    =       min_resolution
          __lowercase		:			Optional[Any]	    =       max_resolution
          __lowercase		:			Dict	    =       do_resize
          __lowercase		:			Tuple	    =       size
          __lowercase		:			List[str]	    =       do_normalize
          __lowercase		:			Union[str, Any]	    =       image_mean
          __lowercase		:			Optional[Any]	    =       image_std




      def 		snake_case_			(      self		:       Union[str, Any]				):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    ViTImageProcessor if is_vision_available() else None




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Optional[int]	    =       EfficientFormerImageProcessorTester(self				)




      @property
      def 		snake_case_			(      self		:       Tuple				):
          return self.image_proc_tester.prepare_image_processor_dict()




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Union[str, Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_snake_case				,       '''image_mean'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''image_std'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_normalize'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_resize'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''size'''				)				)




      def 		snake_case_			(      self		:       List[str]				):
          pass




      def 		snake_case_			(      self		:       str				):
          # Initialize image_processor
          __lowercase		:			Tuple	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          __lowercase		:			Optional[Any]	    =       prepare_image_inputs(self.image_proc_tester				,       equal_resolution=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       Image.Image				)

          # Test not batched input
          __lowercase		:			Any	    =       image_processor(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			List[Any]	    =       image_processor(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )




      def 		snake_case_			(      self		:       Optional[Any]				):
          # Initialize image_processor
          __lowercase		:			Union[str, Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          __lowercase		:			Dict	    =       prepare_image_inputs(self.image_proc_tester				,       equal_resolution=_snake_case				,       numpify=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       np.ndarray				)

          # Test not batched input
          __lowercase		:			str	    =       image_processor(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			Any	    =       image_processor(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )




      def 		snake_case_			(      self		:       List[str]				):
          # Initialize image_processor
          __lowercase		:			str	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          __lowercase		:			List[Any]	    =       prepare_image_inputs(self.image_proc_tester				,       equal_resolution=_snake_case				,       torchify=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       torch.Tensor				)

          # Test not batched input
          __lowercase		:			List[str]	    =       image_processor(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			Optional[Any]	    =       image_processor(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['''height'''],
                  self.image_proc_tester.size['''width'''],
              )				,       )




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						__lowerCAmelCase	:



      """simple docstring"""







      def 		snake_case_			(      self		:       str				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=1				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=3				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			List[str]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			str	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Optional[int]				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=[1, 2]				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=6				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       class_embed_type='''timestep'''				,       mid_block_scale_factor=1.4_14				,       time_embedding_act_fn='''gelu'''				,       time_embedding_dim=32				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       )

          torch.manual_seed(0				)
          __lowercase		:			Any	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "image_noising_scheduler": image_noising_scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       self.get_dummy_components()
          __lowercase		:			Any	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			List[Any]	    =       inputs['''prompt''']
          __lowercase		:			int	    =       inputs['''generator''']
          __lowercase		:			Dict	    =       inputs['''num_inference_steps''']
          __lowercase		:			Optional[int]	    =       inputs['''output_type''']

          if "image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''image''']
          else:
              __lowercase		:			Dict	    =       None

          if "mask_image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''mask_image''']
          else:
              __lowercase		:			List[Any]	    =       None

          if "original_image" in inputs:
              __lowercase		:			List[Any]	    =       inputs['''original_image''']
          else:
              __lowercase		:			Optional[int]	    =       None

          __lowercase    ,      __lowercase		:			Optional[int]	    =       pipe.encode_prompt(_snake_case				)

          # inputs with prompt converted to embeddings
          __lowercase		:			int	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Tuple	    =       image

          if mask_image is not None:
              __lowercase		:			List[str]	    =       mask_image

          if original_image is not None:
              __lowercase		:			List[Any]	    =       original_image

          # set all optional components to None
          for optional_component in pipe._optional_components:
              setattr(_snake_case				,       _snake_case				,       _snake_case				)

          __lowercase		:			List[str]	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			List[Any]	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_snake_case				,       _snake_case				) is None				,       F'`{optional_component}` did not stay set to None after loading.'				,       )

          __lowercase		:			int	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			Union[str, Any]	    =       inputs['''generator''']
          __lowercase		:			Any	    =       inputs['''num_inference_steps''']
          __lowercase		:			Tuple	    =       inputs['''output_type''']

          # inputs with prompt converted to embeddings
          __lowercase		:			List[str]	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Dict	    =       image

          if mask_image is not None:
              __lowercase		:			Tuple	    =       mask_image

          if original_image is not None:
              __lowercase		:			Any	    =       original_image

          __lowercase		:			List[str]	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			Optional[int]	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Union[str, Any]	    =       self.get_dummy_components()
          __lowercase		:			Dict	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Dict	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			Any	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			Dict	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          __lowercase		:			Optional[Any]	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			str	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			int	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)


