






'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


a_					=	get_tests_dir('fixtures')






class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[Any] )		->  Union[str, Any]:
       # A mock response for an HTTP head request to emulate server down
       SCREAMING_SNAKE_CASE__       :			List[str]													=mock.Mock()
       SCREAMING_SNAKE_CASE__       :			Any													=5_00
       SCREAMING_SNAKE_CASE__       :			List[Any]													={}
       SCREAMING_SNAKE_CASE__       :			Dict													=HTTPError
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													={}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__       :			int													=ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch('''requests.Session.request'''   , return_value=__lowercase ) as mock_head:
             SCREAMING_SNAKE_CASE__       :			int													=ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
             # This check we did call the fake head request
             mock_head.assert_called()




 def        __magic_name__		( self		:							str )		->  Dict:
       # This test is for deprecated behavior and can be removed in v5
       SCREAMING_SNAKE_CASE__       :			str													=ViTImageProcessor.from_pretrained(
           '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )




 def        __magic_name__		( self		:							Any )		->  Tuple:
       with self.assertRaises(__lowercase ):
             # config is in subfolder, the following should not work without specifying the subfolder
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/stable-diffusion-all-variants'''   , subfolder='''feature_extractor''' )

       self.assertIsNotNone(__lowercase )






@is_staging_test
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 @classmethod
 def        __magic_name__		( cls		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Any													=TOKEN
       HfFolder.save_token(__lowercase )




 @classmethod
 def        __magic_name__		( cls		:							Dict )		->  Any:
       try:
             delete_repo(token=cls._token   , repo_id='''test-image-processor''' )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token   , repo_id='''valid_org/test-image-processor-org''' )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token   , repo_id='''test-dynamic-image-processor''' )
       except HTTPError:
             pass




 def        __magic_name__		( self		:							List[str] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=ViTImageProcessor.from_pretrained(__lowercase )
       image_processor.push_to_hub('''test-image-processor'''   , use_auth_token=self._token )

       SCREAMING_SNAKE_CASE__       :			Any													=ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor" )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(__lowercase   , getattr(__lowercase   , __lowercase ) )

       # Reset repo
       delete_repo(token=self._token   , repo_id='''test-image-processor''' )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 __lowercase   , repo_id='''test-image-processor'''   , push_to_hub=__lowercase   , use_auth_token=self._token )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor" )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(__lowercase   , getattr(__lowercase   , __lowercase ) )




 def        __magic_name__		( self		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=ViTImageProcessor.from_pretrained(__lowercase )
       image_processor.push_to_hub('''valid_org/test-image-processor'''   , use_auth_token=self._token )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(__lowercase   , getattr(__lowercase   , __lowercase ) )

       # Reset repo
       delete_repo(token=self._token   , repo_id='''valid_org/test-image-processor''' )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 __lowercase   , repo_id='''valid_org/test-image-processor-org'''   , push_to_hub=__lowercase   , use_auth_token=self._token )

       SCREAMING_SNAKE_CASE__       :			int													=ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(__lowercase   , getattr(__lowercase   , __lowercase ) )




 def        __magic_name__		( self		:							Tuple )		->  Any:
       CustomImageProcessor.register_for_auto_class()
       SCREAMING_SNAKE_CASE__       :			Tuple													=CustomImageProcessor.from_pretrained(__lowercase )

       image_processor.push_to_hub('''test-dynamic-image-processor'''   , use_auth_token=self._token )

       # This has added the proper auto_map field to the config
       self.assertDictEqual(
           image_processor.auto_map   , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}   , )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=AutoImageProcessor.from_pretrained(
           F"{USER}/test-dynamic-image-processor"   , trust_remote_code=__lowercase )
       # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
       self.assertEqual(new_image_processor.__class__.__name__   , '''CustomImageProcessor''' )







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

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							StableDiffusionSAGPipeline
 snake_case_												=							TEXT_TO_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							False




 def        __magic_name__		( self		:							str )		->  Union[str, Any]:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=UNetaDConditionModel(
           block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=4   , out_channels=4   , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   , cross_attention_dim=32   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DDIMScheduler(
           beta_start=0.00085   , beta_end=0.012   , beta_schedule='''scaled_linear'''   , clip_sample=__lowercase   , set_alpha_to_one=__lowercase   , )
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			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 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=CLIPTextConfig(
           bos_token_id=0   , eos_token_id=2   , hidden_size=32   , intermediate_size=37   , layer_norm_eps=1e-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=10_00   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=CLIPTextModel(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def        __magic_name__		( self		:							int   , __lowercase		:							Union[str, Any]   , __lowercase		:							Any=0 )		->  Optional[Any]:
       if str(__lowercase ).startswith('''mps''' ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.manual_seed(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.Generator(device=__lowercase ).manual_seed(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={
           '''prompt''': '''.''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 1.0,
           '''sag_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        __magic_name__		( self		:							int )		->  str:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






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




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




 def        __magic_name__		( self		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			int													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			str													=np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Tuple													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[str]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													=output.images

       SCREAMING_SNAKE_CASE__       :			Tuple													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[Any]													=np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							str )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sag_pipe(
           [prompt]   , width=7_68   , height=5_12   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np'''   , )

       SCREAMING_SNAKE_CASE__       :			Any													=output.images

       assert image.shape == (1, 5_12, 7_68, 3)