

'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							PaintByExamplePipeline
   _SCREAMING_SNAKE_CASE					:  Dict			 =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      torch.manual_seed(0						)
      _lowercase    :   List[str]      =     UNetaDConditionModel(
          block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,       cross_attention_dim=32    ,       )
      _lowercase    :   Optional[Any]      =     PNDMScheduler(skip_prk_steps=_UpperCamelCase						)
      torch.manual_seed(0						)
      _lowercase    :   List[Any]      =     AutoencoderKL(
          block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,       latent_channels=4    ,       )
      torch.manual_seed(0						)
      _lowercase    :   Optional[Any]      =     CLIPVisionConfig(
          hidden_size=32    ,       projection_dim=32    ,       intermediate_size=37    ,       layer_norm_eps=1E-0_5    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       image_size=32    ,       patch_size=4    ,       )
      _lowercase    :   str      =     PaintByExampleImageEncoder(_UpperCamelCase    ,       proj_size=32						)
      _lowercase    :   int      =     CLIPImageProcessor(crop_size=32    ,       size=32						)

      _lowercase    :   List[str]      =     {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Any      =     np.array(image.convert("RGB"						)						)
      _lowercase    :   Union[str, Any]      =     image[None].transpose(0    ,       3    ,       1    ,       2						)
      _lowercase    :   str      =     torch.from_numpy(_UpperCamelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
      return image


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase="cpu"    ,       _UpperCamelCase=0						):





      """simple docstring"""





      _lowercase    :   List[str]      =     floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
      _lowercase    :   Tuple      =     image.cpu().permute(0    ,       2    ,       3    ,       1						)[0]
      _lowercase    :   Optional[Any]      =     Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("RGB"						).resize((64, 64)						)
      _lowercase    :   Tuple      =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
      _lowercase    :   int      =     Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("RGB"						).resize((32, 32)						)

      if str(_UpperCamelCase						).startswith("mps"						):
         _lowercase    :   List[Any]      =     torch.manual_seed(_UpperCamelCase						)
      else:
         _lowercase    :   Union[str, Any]      =     torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
      _lowercase    :   int      =     {
          "example_image": example_image,
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _lowercase    :   Union[str, Any]      =     PaintByExamplePipeline(**_UpperCamelCase						)
      _lowercase    :   Optional[Any]      =     pipe.to("cpu"						)
      pipe.set_progress_bar_config(disable=_UpperCamelCase						)

      _lowercase    :   List[Any]      =     self.get_dummy_inputs()
      _lowercase    :   Optional[Any]      =     pipe(**_UpperCamelCase						)
      _lowercase    :   Tuple      =     output.images

      _lowercase    :   Optional[Any]      =     image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      _lowercase    :   List[Any]      =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     "cpu"
      _lowercase    :   Dict      =     self.get_dummy_inputs()
      inputs.pop("mask_image"						)
      _lowercase    :   int      =     self.convert_to_pt(inputs.pop("image"						)						)
      _lowercase    :   str      =     image.clamp(0    ,       1						) / 2

      # make sure here that pndm scheduler skips prk
      _lowercase    :   Optional[int]      =     PaintByExamplePipeline(**self.get_dummy_components()						)
      _lowercase    :   Union[str, Any]      =     pipe.to(_UpperCamelCase						)
      pipe.set_progress_bar_config(disable=_UpperCamelCase						)

      _lowercase    :   Dict      =     pipe(image=_UpperCamelCase    ,       mask_image=mask_image[:, 0]    ,       **_UpperCamelCase						)
      _lowercase    :   Tuple      =     output.images

      _lowercase    :   List[Any]      =     image.cpu().permute(0    ,       2    ,       3    ,       1						)[0]
      _lowercase    :   Optional[int]      =     mask_image.cpu().permute(0    ,       2    ,       3    ,       1						)[0]

      _lowercase    :   Dict      =     Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("RGB"						)
      _lowercase    :   Union[str, Any]      =     Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("RGB"						)

      _lowercase    :   List[Any]      =     pipe(**self.get_dummy_inputs()						)
      _lowercase    :   int      =     output.images

      assert out_a.shape == (1, 64, 64, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




@slow
@require_torch_gpu
class      a__						(							unittest.TestCase						):


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/dog_in_bucket.png"						)
      _lowercase    :   List[str]      =     load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/mask.png"						)
      _lowercase    :   Dict      =     load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/panda.jpg"						)

      _lowercase    :   Any      =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
      _lowercase    :   int      =     pipe.to(_UpperCamelCase						)
      pipe.set_progress_bar_config(disable=_UpperCamelCase						)

      _lowercase    :   int      =     torch.manual_seed(321						)
      _lowercase    :   Tuple      =     pipe(
          image=_UpperCamelCase    ,       mask_image=_UpperCamelCase    ,       example_image=_UpperCamelCase    ,       generator=_UpperCamelCase    ,       guidance_scale=5.0    ,       num_inference_steps=50    ,       output_type="np"    ,       )

      _lowercase    :   Union[str, Any]      =     output.images

      _lowercase    :   Optional[Any]      =     image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowercase    :   str      =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2









'''simple docstring'''
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor


class      a__						:


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=False    ,       _UpperCamelCase=13    ,       _UpperCamelCase=(32, 128)    ,       _UpperCamelCase=4    ,       _UpperCamelCase=3    ,       _UpperCamelCase=27    ,       _UpperCamelCase=38    ,       _UpperCamelCase=99    ,       _UpperCamelCase=99    ,       _UpperCamelCase=32    ,       _UpperCamelCase=5    ,       _UpperCamelCase=4    ,       _UpperCamelCase=4.0    ,       _UpperCamelCase=257    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     parent
      _lowercase    :   Dict      =     is_training
      _lowercase    :   Optional[Any]      =     batch_size
      _lowercase    :   int      =     image_size
      _lowercase    :   Dict      =     patch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   List[str]      =     max_token_length
      _lowercase    :   int      =     num_character_labels
      _lowercase    :   int      =     num_bpe_labels
      _lowercase    :   int      =     num_wordpiece_labels
      _lowercase    :   Union[str, Any]      =     hidden_size
      _lowercase    :   List[str]      =     num_hidden_layers
      _lowercase    :   int      =     num_attention_heads
      _lowercase    :   Any      =     mlp_ratio
      _lowercase    :   Optional[Any]      =     patch_embeds_hidden_size
      _lowercase    :   Optional[Any]      =     output_hidden_states


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
      _lowercase    :   int      =     self.get_config()
      return config, pixel_values


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return MgpstrConfig(
          image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       max_token_length=self.max_token_length    ,       num_character_labels=self.num_character_labels    ,       num_bpe_labels=self.num_bpe_labels    ,       num_wordpiece_labels=self.num_wordpiece_labels    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       mlp_ratio=self.mlp_ratio    ,       output_hidden_states=self.output_hidden_states    ,       )


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   List[str]      =     MgpstrForSceneTextRecognition(_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)
      self.parent.assertEqual(
          generated_ids[0][0].shape    ,       (self.batch_size, self.max_token_length, self.num_character_labels)						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.prepare_config_and_inputs()
      _lowercase  ,   _lowercase    :   Optional[int]      =     config_and_inputs
      _lowercase    :   Union[str, Any]      =     {"pixel_values": pixel_values}
      return config, inputs_dict




@require_torch
class      a__						(							lowerCamelCase_						,   lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  str			 =							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False

   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							False
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     MgpstrModelTester(self						)
      _lowercase    :   Optional[Any]      =     ConfigTester(self    ,       config_class=_UpperCamelCase    ,       has_text_modality=_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not use inputs_embeds"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Optional[int]      =     model_class(_UpperCamelCase						)
         self.assertIsInstance(model.get_input_embeddings()    ,       (nn.Module)						)
         _lowercase    :   Any      =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_UpperCamelCase    ,       nn.Linear						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Any      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         _lowercase    :   Union[str, Any]      =     inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowercase    :   Optional[int]      =     [*signature.parameters.keys()]

         _lowercase    :   Optional[Any]      =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]    ,       _UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not support feedforward chunking"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   List[str]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         if not model_class.supports_gradient_checkpointing:
            continue

         _lowercase    :   List[Any]      =     True
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         self.assertTrue(model.is_gradient_checkpointing						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      def check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						):
         _lowercase    :   Dict      =     model_class(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         with torch.no_grad():
            _lowercase    :   Dict      =     model(**self._prepare_for_class(_UpperCamelCase    ,       _UpperCamelCase						)						)

         _lowercase    :   int      =     outputs.hidden_states

         _lowercase    :   Any      =     getattr(
             self.model_tester    ,       "expected_num_hidden_layers"    ,       self.model_tester.num_hidden_layers + 1						)
         self.assertEqual(len(_UpperCamelCase						)    ,       _UpperCamelCase						)

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]						)    ,       [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]    ,       )

      _lowercase  ,   _lowercase    :   Optional[int]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Dict      =     True
         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowercase    :   Dict      =     True

         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Union[str, Any]      =     self.model_tester.prepare_config_and_inputs_for_common()

      _lowercase    :   str      =     _config_zero_init(_UpperCamelCase						)
      for model_class in self.all_model_classes:
         _lowercase    :   Union[str, Any]      =     model_class(config=_UpperCamelCase						)
         for name, param in model.named_parameters():
            if isinstance(_UpperCamelCase    ,       (nn.Linear, nn.Convad, nn.LayerNorm)						):
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item()    ,       [0.0, 1.0]    ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,       )






   @unittest.skip(reason="Retain_grad is tested in individual model tests"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass







def       _A	(			)    ->			str:
   _lowercase    :   Any      =     "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
   _lowercase    :   Any      =     Image.open(requests.get(snake_case	,							stream=snake_case  ).raw  ).convert("RGB"  )
   return im


@require_vision
@require_torch
class      a__						(							unittest.TestCase						):


   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     "alibaba-damo/mgp-str-base"
      _lowercase    :   Any      =     MgpstrForSceneTextRecognition.from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
      _lowercase    :   List[Any]      =     MgpstrProcessor.from_pretrained(_UpperCamelCase						)

      _lowercase    :   Tuple      =     prepare_img()
      _lowercase    :   Any      =     processor(images=_UpperCamelCase    ,       return_tensors="pt"						).pixel_values.to(_UpperCamelCase						)

      # forward pass
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape    ,       torch.Size((1, 27, 38)						)						)

      _lowercase    :   Tuple      =     processor.batch_decode(outputs.logits						)
      _lowercase    :   Optional[Any]      =     "ticket"

      self.assertEqual(out_strs["generated_text"][0]    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]    ,       device=_UpperCamelCase    ,       )

      self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]    ,       _UpperCamelCase    ,       atol=1E-4						)						)






