



"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class    a		(					unittest.TestCase    ):



  @patch("""sys.argv"""    ,						["""fakeprogrampath""", """env"""]						)
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     with CaptureStd() as cs:
        transformers.commands.transformers_cli.main()
     self.assertIn("""Python version"""    ,						cs.out						)
     self.assertIn("""Platform"""    ,						cs.out						)
     self.assertIn("""Using distributed or parallel set-up in script?"""    ,						cs.out						)



  @is_pt_tf_cross_test
  @patch(
      """sys.argv"""    ,						["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]						)
  def 		lowerCamelCase__    (  self	:   Any						)       ->   Optional[int]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,						ignore_errors=lowerCAmelCase						)  # cleans potential past runs
     transformers.commands.transformers_cli.main()

     # The original repo has no TF weights -- if they exist, they were created by the CLI
     self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""						)						)



  @require_torch
  @patch("""sys.argv"""    ,						["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]						)
  def 		lowerCamelCase__    (  self	:   int						)       ->   str:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     # # remove any previously downloaded model to start clean
     shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,						ignore_errors=lowerCAmelCase						)

     # run the command
     transformers.commands.transformers_cli.main()

     # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""						)						)





  @require_torch
  @patch(
      """sys.argv"""    ,						[
          """fakeprogrampath""",
          """download""",
          """hf-internal-testing/test_dynamic_model_with_tokenizer""",
          """--trust-remote-code""",
          """--cache-dir""",
          """/tmp""",
      ]    ,						)
  def 		lowerCamelCase__    (  self	:   int						)       ->   Union[str, Any]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     # # remove any previously downloaded model to start clean
     shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,						ignore_errors=lowerCAmelCase						)

     # run the command
     transformers.commands.transformers_cli.main()

     # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""						)						)
     self.assertTrue(
         os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""						)						)








"""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		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   PaintByExamplePipeline
  UpperCamelCase		:			str         =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCamelCase		:			Tuple         =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase		:			List[str]         =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Any            =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    ,						)
     SCREAMING_SNAKE_CASE_:	int            =PNDMScheduler(skip_prk_steps=lowerCAmelCase						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	str            =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						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPVisionConfig(
         hidden_size=32    ,						projection_dim=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						image_size=32    ,						patch_size=4    ,						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =PaintByExampleImageEncoder(lowerCAmelCase    ,						proj_size=32						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPImageProcessor(crop_size=32    ,						size=32						)

     SCREAMING_SNAKE_CASE_:	Any            ={
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """image_encoder""": image_encoder,
         """safety_checker""": None,
         """feature_extractor""": feature_extractor,
     }
     return components



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   int="cpu"    ,						lowerCAmelCase	:   Dict=0						)       ->   Any:


     '''simple docstring'''




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

     if str(lowerCAmelCase						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Dict            =torch.manual_seed(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            ={
         """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	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_dummy_components()

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

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_dummy_inputs()
     SCREAMING_SNAKE_CASE_:	int            =pipe(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	List[str]            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_:	Optional[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	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""cpu"""
     SCREAMING_SNAKE_CASE_:	Any            =self.get_dummy_inputs()
     inputs.pop("""mask_image"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.convert_to_pt(inputs.pop("""image"""						)						)
     SCREAMING_SNAKE_CASE_:	int            =image.clamp(0    ,						1						) / 2

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =PaintByExamplePipeline(**self.get_dummy_components()						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(image=lowerCAmelCase    ,						mask_image=mask_image[:, 0]    ,						**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	str            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	List[Any]            =mask_image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]

     SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(**self.get_dummy_inputs()						)
     SCREAMING_SNAKE_CASE_:	Optional[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	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




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







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



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Any:


     '''simple docstring'''




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





  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[Any]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Tuple            =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =torch.manual_seed(321						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         image=lowerCAmelCase    ,						mask_image=lowerCAmelCase    ,						example_image=lowerCAmelCase    ,						generator=lowerCAmelCase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type="""np"""    ,						)

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

     SCREAMING_SNAKE_CASE_:	Any            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE_:	Any            =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



