import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      UnCLIPImageVariationPipeline
    __a  =      IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
    __a  =      IMAGE_VARIATION_BATCH_PARAMS

    __a  =      [
        "generator",
        "return_dict",
        "decoder_num_inference_steps",
        "super_res_num_inference_steps",
    ]
    __a  =      False
    @property
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     return 32
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     return 32
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return self.time_input_dim
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return self.time_input_dim * 4
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return 100
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   int=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
     return tokenizer
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      CLIPTextConfig(
         bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
     return CLIPTextModelWithProjection(lowerCAmelCase   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   str=      CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
     return CLIPVisionModelWithProjection(lowerCAmelCase   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   Any=      {
         '''clip_embeddings_dim''': self.text_embedder_hidden_size,
         '''time_embed_dim''': self.time_embed_dim,
         '''cross_attention_dim''': self.cross_attention_dim,
     }

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      UnCLIPTextProjModel(**lowerCAmelCase   )
     return model
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   str=      {
         '''sample_size''': 32,
         # RGB in channels
         '''in_channels''': 3,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 6,
         '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
         '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
         '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
         '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
         '''layers_per_block''': 1,
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': '''identity''',
     }

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      UNetaDConditionModel(**lowerCAmelCase   )
     return model
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return {
         "sample_size": 64,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   Any=      UNetaDModel(**self.dummy_super_res_kwargs   )
     return model
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      UNetaDModel(**self.dummy_super_res_kwargs   )
     return model
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.dummy_decoder
     SCREAMING_SNAKE_CASE__:   Dict=      self.dummy_text_proj
     SCREAMING_SNAKE_CASE__:   Any=      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__:   Any=      self.dummy_tokenizer
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.dummy_super_res_first
     SCREAMING_SNAKE_CASE__:   int=      self.dummy_super_res_last

     SCREAMING_SNAKE_CASE__:   Any=      UnCLIPScheduler(
         variance_type='''learned_range''' , prediction_type='''epsilon''' , num_train_timesteps=1000 , )

     SCREAMING_SNAKE_CASE__:   int=      UnCLIPScheduler(
         variance_type='''fixed_small_log''' , prediction_type='''epsilon''' , num_train_timesteps=1000 , )

     SCREAMING_SNAKE_CASE__:   Dict=      CLIPImageProcessor(crop_size=32 , size=32   )

     SCREAMING_SNAKE_CASE__:   List[str]=      self.dummy_image_encoder

     return {
         "decoder": decoder,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "text_proj": text_proj,
         "feature_extractor": feature_extractor,
         "image_encoder": image_encoder,
         "super_res_first": super_res_first,
         "super_res_last": super_res_last,
         "decoder_scheduler": decoder_scheduler,
         "super_res_scheduler": super_res_scheduler,
     }
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0 , lowerCAmelCase=True   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Tuple=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Tuple=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )

     if pil_image:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      input_image * 0.5 + 0.5
      SCREAMING_SNAKE_CASE__:   Optional[int]=      input_image.clamp(0 , 1   )
      SCREAMING_SNAKE_CASE__:   int=      input_image.cpu().permute(0 , 2 , 3 , 1   ).float().numpy()
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      DiffusionPipeline.numpy_to_pil(lowerCAmelCase   )[0]

     return {
         "image": input_image,
         "generator": generator,
         "decoder_num_inference_steps": 2,
         "super_res_num_inference_steps": 2,
         "output_type": "np",
     }
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      '''cpu'''

     SCREAMING_SNAKE_CASE__:   Any=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.pipeline_class(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      pipe.to(lowerCAmelCase   )

     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      output.images

     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      pipe(
         **lowerCAmelCase , return_dict=lowerCAmelCase , )[0]

     SCREAMING_SNAKE_CASE__:   Any=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__:   List[str]=      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np.array(
         [
             0.9997,
             0.0002,
             0.9997,
             0.9997,
             0.9969,
             0.0023,
             0.9997,
             0.9969,
             0.9970,
         ]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Any=      '''cpu'''

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.pipeline_class(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      pipe.to(lowerCAmelCase   )

     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      output.images

     SCREAMING_SNAKE_CASE__:   List[str]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      pipe(
         **lowerCAmelCase , return_dict=lowerCAmelCase , )[0]

     SCREAMING_SNAKE_CASE__:   Optional[int]=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__:   str=      np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Any=      '''cpu'''

     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.pipeline_class(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      pipe.to(lowerCAmelCase   )

     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         pipeline_inputs['''image'''],
         pipeline_inputs['''image'''],
     ]

     SCREAMING_SNAKE_CASE__:   Any=      pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      output.images

     SCREAMING_SNAKE_CASE__:   Tuple=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [
         tuple_pipeline_inputs['''image'''],
         tuple_pipeline_inputs['''image'''],
     ]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      pipe(
         **lowerCAmelCase , return_dict=lowerCAmelCase , )[0]

     SCREAMING_SNAKE_CASE__:   Any=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__:   Dict=      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (2, 64, 64, 3)

     SCREAMING_SNAKE_CASE__:   str=      np.array(
         [
             0.9997,
             0.9989,
             0.0008,
             0.0021,
             0.9960,
             0.0018,
             0.0014,
             0.0002,
             0.9933,
         ]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.device('''cpu'''   )






     class 		_lowerCamelCase		:
         __a  =      1
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__:   int=      self.pipeline_class(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      pipe.to(lowerCAmelCase   )

     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Any=      pipe.decoder.dtype
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      (
         batch_size,
         pipe.decoder.config.in_channels,
         pipe.decoder.config.sample_size,
         pipe.decoder.config.sample_size,
     )
     SCREAMING_SNAKE_CASE__:   Any=      pipe.prepare_latents(
         lowerCAmelCase , dtype=lowerCAmelCase , device=lowerCAmelCase , generator=lowerCAmelCase , latents=lowerCAmelCase , scheduler=DummyScheduler()   )

     SCREAMING_SNAKE_CASE__:   Any=      (
         batch_size,
         pipe.super_res_first.config.in_channels // 2,
         pipe.super_res_first.config.sample_size,
         pipe.super_res_first.config.sample_size,
     )
     SCREAMING_SNAKE_CASE__:   Dict=      pipe.prepare_latents(
         lowerCAmelCase , dtype=lowerCAmelCase , device=lowerCAmelCase , generator=lowerCAmelCase , latents=lowerCAmelCase , scheduler=DummyScheduler()   )

     SCREAMING_SNAKE_CASE__:   List[str]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      pipe(
         **lowerCAmelCase , decoder_latents=lowerCAmelCase , super_res_latents=lowerCAmelCase   ).images

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_dummy_inputs(lowerCAmelCase , pil_image=lowerCAmelCase   )
     # Don't pass image, instead pass embedding
     SCREAMING_SNAKE_CASE__:   Optional[int]=      pipeline_inputs.pop('''image'''   )
     SCREAMING_SNAKE_CASE__:   Dict=      pipe.image_encoder(lowerCAmelCase   ).image_embeds

     SCREAMING_SNAKE_CASE__:   Optional[int]=      pipe(
         **lowerCAmelCase , decoder_latents=lowerCAmelCase , super_res_latents=lowerCAmelCase , image_embeddings=lowerCAmelCase , ).images

     # make sure passing text embeddings manually is identical
     assert np.abs(img_out_a - img_out_a   ).max() < 1e-4
    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch_device == '''cpu'''

     # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
     SCREAMING_SNAKE_CASE__:   Optional[int]=      1e-2

     self._test_attention_slicing_forward_pass(
         test_max_difference=lowerCAmelCase , expected_max_diff=lowerCAmelCase   )
    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch_device == '''cpu'''
     SCREAMING_SNAKE_CASE__:   Optional[int]=      True
     SCREAMING_SNAKE_CASE__:   Any=      [
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=lowerCAmelCase , relax_max_difference=lowerCAmelCase , additional_params_copy_to_batched_inputs=lowerCAmelCase , )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   int=      [
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     if torch_device == "mps":
      # TODO: MPS errors with larger batch sizes
      SCREAMING_SNAKE_CASE__:   List[Any]=      [2, 3]
      self._test_inference_batch_consistent(
          batch_sizes=lowerCAmelCase , additional_params_copy_to_batched_inputs=lowerCAmelCase , )
     else:
      self._test_inference_batch_consistent(
          additional_params_copy_to_batched_inputs=lowerCAmelCase   )
    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     return super().test_save_load_local()



    @skip_mps
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Dict=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
     SCREAMING_SNAKE_CASE__:   int=      load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

     SCREAMING_SNAKE_CASE__:   Tuple=      UnCLIPImageVariationPipeline.from_pretrained(
         '''kakaobrain/karlo-v1-alpha-image-variations''' , torch_dtype=torch.floataa   )
     SCREAMING_SNAKE_CASE__:   Any=      pipeline.to(lowerCAmelCase   )
     pipeline.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      torch.Generator(device='''cpu'''   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Any=      pipeline(
         lowerCAmelCase , generator=lowerCAmelCase , output_type='''np''' , )

     SCREAMING_SNAKE_CASE__:   Tuple=      output.images[0]

     assert image.shape == (256, 256, 3)

     assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase , 15   )


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_pytesseract_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=3 , lowerCAmelCase=18 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=True , )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Tuple=      size if size is not None else {'''height''': 18, '''width''': 18}
     SCREAMING_SNAKE_CASE__:   Dict=      parent
     SCREAMING_SNAKE_CASE__:   Tuple=      batch_size
     SCREAMING_SNAKE_CASE__:   int=      num_channels
     SCREAMING_SNAKE_CASE__:   List[Any]=      image_size
     SCREAMING_SNAKE_CASE__:   Dict=      min_resolution
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      max_resolution
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      do_resize
     SCREAMING_SNAKE_CASE__:   List[Any]=      size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      apply_ocr



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      LayoutLMvaImageProcessor if is_pytesseract_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      LayoutLMvaImageProcessingTester(self   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     return self.image_processor_tester.prepare_image_processor_dict()
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase , '''do_resize'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''size'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''apply_ocr'''   )   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18}   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.image_processing_class.from_dict(self.image_processor_dict , size=42   )
     self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42}   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     pass
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   int=      self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     SCREAMING_SNAKE_CASE__:   Optional[int]=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , Image.Image   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   str=      image_processing(image_inputs[0] , return_tensors='''pt'''   )
     self.assertEqual(
         encoding.pixel_values.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )

     self.assertIsInstance(encoding.words , lowerCAmelCase   )
     self.assertIsInstance(encoding.boxes , lowerCAmelCase   )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__:   Dict=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , np.ndarray   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   Dict=      image_processing(image_inputs[0] , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )
    def 						UpperCamelCase_				(  self   )       ->					str:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__:   int=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , torch.Tensor   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   Optional[int]=      image_processing(image_inputs[0] , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Any=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ) , )



    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     # with apply_OCR = True
     SCREAMING_SNAKE_CASE__:   int=      LayoutLMvaImageProcessor()

     from datasets import load_dataset

     SCREAMING_SNAKE_CASE__:   int=      load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test'''   )

     SCREAMING_SNAKE_CASE__:   str=      Image.open(ds[0]['''file''']   ).convert('''RGB'''   )

     SCREAMING_SNAKE_CASE__:   str=      image_processing(lowerCAmelCase , return_tensors='''pt'''   )

     self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224)   )
     self.assertEqual(len(encoding.words   ) , len(encoding.boxes   )   )

     # fmt: off
     # the words and boxes were obtained with Tesseract 4.1.1
     SCREAMING_SNAKE_CASE__:   Dict=      [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
     SCREAMING_SNAKE_CASE__:   List[Any]=      [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
     # fmt: on

     self.assertListEqual(encoding.words , lowerCAmelCase   )
     self.assertListEqual(encoding.boxes , lowerCAmelCase   )

     # with apply_OCR = False
     SCREAMING_SNAKE_CASE__:   int=      LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      image_processing(lowerCAmelCase , return_tensors='''pt'''   )

     self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224)   )

