






'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0




 def      __magic_name__	(      self	:							str      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[Any]:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				List[Any]									=Path(__lowercase      ) / '''preprocessor_config.json'''
             SCREAMING_SNAKE_CASE__     :				int									=Path(__lowercase      ) / '''config.json'''
             json.dump(
                 {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}					,      open(__lowercase					,      '''w'''      )					,      )
             json.dump({'''model_type''': '''clip'''}					,      open(__lowercase					,      '''w'''      )      )

             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=AutoImageProcessor.from_pretrained(__lowercase      )
             self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						List[Any]:
       # Ensure we can load the image processor from the feature extractor config
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Path(__lowercase      ) / '''preprocessor_config.json'''
             SCREAMING_SNAKE_CASE__     :				List[str]									=Path(__lowercase      ) / '''config.json'''
             json.dump(
                 {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}					,      open(__lowercase					,      '''w'''      )					,      )
             json.dump({'''model_type''': '''clip'''}					,      open(__lowercase					,      '''w'''      )      )

             SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoImageProcessor.from_pretrained(__lowercase      )
             self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				Any									=CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=Path(__lowercase      ) / '''preprocessor_config.json'''
             SCREAMING_SNAKE_CASE__     :				str									=Path(__lowercase      ) / '''config.json'''
             json.dump(
                 {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}					,      open(__lowercase					,      '''w'''      )					,      )
             json.dump({'''model_type''': '''clip'''}					,      open(__lowercase					,      '''w'''      )      )

             # remove image_processor_type to make sure config.json alone is enough to load image processor locally
             SCREAMING_SNAKE_CASE__     :				str									=AutoImageProcessor.from_pretrained(__lowercase      ).to_dict()

             config_dict.pop('''image_processor_type'''      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=CLIPImageProcessor(**__lowercase      )

             # save in new folder
             model_config.save_pretrained(__lowercase      )
             config.save_pretrained(__lowercase      )

             SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoImageProcessor.from_pretrained(__lowercase      )

             # make sure private variable is not incorrectly saved
             SCREAMING_SNAKE_CASE__     :				Any									=json.loads(config.to_json_string()      )
             self.assertTrue('''_processor_class''' not in dict_as_saved      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Union[str, Any]:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Path(__lowercase      ) / '''preprocessor_config.json'''
             json.dump(
                 {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}					,      open(__lowercase					,      '''w'''      )					,      )

             SCREAMING_SNAKE_CASE__     :				Dict									=AutoImageProcessor.from_pretrained(__lowercase      )
             self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       with self.assertRaisesRegex(
           __lowercase					,      '''clip-base is not a local folder and is not a valid model identifier'''      ):
             SCREAMING_SNAKE_CASE__     :				Any									=AutoImageProcessor.from_pretrained('''clip-base'''      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       with self.assertRaisesRegex(
           __lowercase					,      r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoImageProcessor.from_pretrained(__lowercase					,      revision='''aaaaaa'''      )




 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       with self.assertRaisesRegex(
           __lowercase					,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''					,      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''      )




 def      __magic_name__	(      self	:							Dict      )	->						str:
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(__lowercase      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''      )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(__lowercase      ):
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=AutoImageProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_image_processor'''					,      trust_remote_code=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				int									=AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''					,      trust_remote_code=__lowercase      )
       self.assertEqual(image_processor.__class__.__name__					,      '''NewImageProcessor'''      )

       # Test image processor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=AutoImageProcessor.from_pretrained(__lowercase					,      trust_remote_code=__lowercase      )
       self.assertEqual(reloaded_image_processor.__class__.__name__					,      '''NewImageProcessor'''      )




 def      __magic_name__	(      self	:							int      )	->						Any:
       try:
             AutoConfig.register('''custom'''					,      __lowercase      )
             AutoImageProcessor.register(__lowercase					,      __lowercase      )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(__lowercase      ):
                   AutoImageProcessor.register(__lowercase					,      __lowercase      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=Path(__lowercase      ) / '''preprocessor_config.json'''
                   SCREAMING_SNAKE_CASE__     :				Dict									=Path(__lowercase      ) / '''config.json'''
                   json.dump(
                       {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}					,      open(__lowercase					,      '''w'''      )					,      )
                   json.dump({'''model_type''': '''clip'''}					,      open(__lowercase					,      '''w'''      )      )

                   SCREAMING_SNAKE_CASE__     :				List[Any]									=CustomImageProcessor.from_pretrained(__lowercase      )

             # Now that the config is registered, it can be used as any other config with the auto-API
             with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(__lowercase      )
                   SCREAMING_SNAKE_CASE__     :				Any									=AutoImageProcessor.from_pretrained(__lowercase      )
                   self.assertIsInstance(__lowercase					,      __lowercase      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




 def      __magic_name__	(      self	:							str      )	->						str:






       class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
        snake_case_  							=							True

       try:
             AutoConfig.register('''custom'''					,      __lowercase      )
             AutoImageProcessor.register(__lowercase					,      __lowercase      )
             # If remote code is not set, the default is to use local
             SCREAMING_SNAKE_CASE__     :				Any									=AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''      )
             self.assertEqual(image_processor.__class__.__name__					,      '''NewImageProcessor'''      )
             self.assertTrue(image_processor.is_local      )

             # If remote code is disabled, we load the local one.
             SCREAMING_SNAKE_CASE__     :				Tuple									=AutoImageProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_image_processor'''					,      trust_remote_code=__lowercase      )
             self.assertEqual(image_processor.__class__.__name__					,      '''NewImageProcessor'''      )
             self.assertTrue(image_processor.is_local      )

             # If remote is enabled, we load from the Hub
             SCREAMING_SNAKE_CASE__     :				str									=AutoImageProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_image_processor'''					,      trust_remote_code=__lowercase      )
             self.assertEqual(image_processor.__class__.__name__					,      '''NewImageProcessor'''      )
             self.assertTrue(not hasattr(__lowercase					,      '''is_local'''      )      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                   del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







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

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							ShapEImgaImgPipeline
 snake_case_  							=							["""image"""]
 snake_case_  							=							["""image"""]
 snake_case_  							=							[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return 32




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       return 8




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Dict									=CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size					,      image_size=64					,      projection_dim=self.text_embedder_hidden_size					,      intermediate_size=37					,      num_attention_heads=4					,      num_channels=3					,      num_hidden_layers=5					,      patch_size=1					,      )

       SCREAMING_SNAKE_CASE__     :				str									=CLIPVisionModel(__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				int									=CLIPImageProcessor(
           crop_size=2_24					,      do_center_crop=__lowercase					,      do_normalize=__lowercase					,      do_resize=__lowercase					,      image_mean=[0.48145466, 0.4578275, 0.40821073]					,      image_std=[0.26862954, 0.26130258, 0.27577711]					,      resample=3					,      size=2_24					,      )

       return image_processor




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				str									={
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''embedding_proj_norm_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				str									=PriorTransformer(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       SCREAMING_SNAKE_CASE__     :				str									=ShapERenderer(**__lowercase      )
       return model




 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.dummy_prior
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_processor
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.dummy_renderer

       SCREAMING_SNAKE_CASE__     :				int									=HeunDiscreteScheduler(
           beta_schedule='''exp'''					,      num_train_timesteps=10_24					,      prediction_type='''sample'''					,      use_karras_sigmas=__lowercase					,      clip_sample=__lowercase					,      clip_sample_range=1.0					,      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any=0      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							List[str]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images[0]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       SCREAMING_SNAKE_CASE__     :				List[Any]									=np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]      )

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




 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
       self._test_inference_batch_single_identical(
           batch_size=2					,      test_max_difference=__lowercase					,      relax_max_difference=__lowercase					,      )




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
       SCREAMING_SNAKE_CASE__     :				List[str]									=2

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

       for key in inputs.keys():
             if key in self.batch_params:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=batch_size * [inputs[key]]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipe(**__lowercase					,      num_images_per_prompt=__lowercase      )[0]

       assert images.shape[0] == batch_size * num_images_per_prompt






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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe(
           __lowercase					,      generator=__lowercase					,      guidance_scale=3.0					,      num_inference_steps=64					,      frame_size=64					,      output_type='''np'''					,      ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )