
"""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 					__a     ( unittest.TestCase       ):



							'''simple docstring'''







							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       0
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"       )
										self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowercase       =       Path(lowerCamelCase_       ) / 'preprocessor_config.json'
													__lowercase       =       Path(lowerCamelCase_       ) / 'config.json'
													json.dump(
													    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}  ,    open(lowerCamelCase_  ,    "w"       )  ,    )
													json.dump({"model_type": "clip"}  ,    open(lowerCamelCase_  ,    "w"       )       )

													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_       )
													self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										# Ensure we can load the image processor from the feature extractor config
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowercase       =       Path(lowerCamelCase_       ) / 'preprocessor_config.json'
													__lowercase       =       Path(lowerCamelCase_       ) / 'config.json'
													json.dump(
													    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}  ,    open(lowerCamelCase_  ,    "w"       )  ,    )
													json.dump({"model_type": "clip"}  ,    open(lowerCamelCase_  ,    "w"       )       )

													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_       )
													self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowercase       =       CLIPConfig()

													# Create a dummy config file with image_proceesor_type
													__lowercase       =       Path(lowerCamelCase_       ) / 'preprocessor_config.json'
													__lowercase       =       Path(lowerCamelCase_       ) / 'config.json'
													json.dump(
													    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}  ,    open(lowerCamelCase_  ,    "w"       )  ,    )
													json.dump({"model_type": "clip"}  ,    open(lowerCamelCase_  ,    "w"       )       )

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

													config_dict.pop("image_processor_type"       )
													__lowercase       =       CLIPImageProcessor(**lowerCamelCase_       )

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

													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_       )

													# make sure private variable is not incorrectly saved
													__lowercase       =       json.loads(config.to_json_string()       )
													self.assertTrue("_processor_class" not in dict_as_saved       )

										self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowercase       =       Path(lowerCamelCase_       ) / 'preprocessor_config.json'
													json.dump(
													    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}  ,    open(lowerCamelCase_  ,    "w"       )  ,    )

													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_       )
													self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    lowerCamelCase_  ,    "clip-base is not a local folder and is not a valid model identifier"       ):
													__lowercase       =       AutoImageProcessor.from_pretrained("clip-base"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    lowerCamelCase_  ,    R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_  ,    revision="aaaaaa"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    lowerCamelCase_  ,    "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."  ,    ):
													__lowercase       =       AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[int]:




										'''simple docstring'''
										# If remote code is not set, we will time out when asking whether to load the model.
										with self.assertRaises(lowerCamelCase_       ):
													__lowercase       =       AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"       )
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(lowerCamelCase_       ):
													__lowercase       =       AutoImageProcessor.from_pretrained(
													    "hf-internal-testing/test_dynamic_image_processor"  ,    trust_remote_code=lowerCamelCase_       )

										__lowercase       =       AutoImageProcessor.from_pretrained(
										    "hf-internal-testing/test_dynamic_image_processor"  ,    trust_remote_code=lowerCamelCase_       )
										self.assertEqual(image_processor.__class__.__name__  ,    "NewImageProcessor"       )

										# Test image processor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
													image_processor.save_pretrained(lowerCamelCase_       )
													__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_  ,    trust_remote_code=lowerCamelCase_       )
										self.assertEqual(reloaded_image_processor.__class__.__name__  ,    "NewImageProcessor"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										try:
													AutoConfig.register("custom"  ,    lowerCamelCase_       )
													AutoImageProcessor.register(lowerCamelCase_  ,    lowerCamelCase_       )
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(lowerCamelCase_       ):
																AutoImageProcessor.register(lowerCamelCase_  ,    lowerCamelCase_       )

													with tempfile.TemporaryDirectory() as tmpdirname:
																__lowercase       =       Path(lowerCamelCase_       ) / 'preprocessor_config.json'
																__lowercase       =       Path(lowerCamelCase_       ) / 'config.json'
																json.dump(
																    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}  ,    open(lowerCamelCase_  ,    "w"       )  ,    )
																json.dump({"model_type": "clip"}  ,    open(lowerCamelCase_  ,    "w"       )       )

																__lowercase       =       CustomImageProcessor.from_pretrained(lowerCamelCase_       )

													# 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(lowerCamelCase_       )
																__lowercase       =       AutoImageProcessor.from_pretrained(lowerCamelCase_       )
																self.assertIsInstance(lowerCamelCase_  ,    lowerCamelCase_       )

										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   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''

										class 					__a     ( _UpperCamelCase       ):



																	'''simple docstring'''







																	_lowerCamelCase					:			Any          =   True

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

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












import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],					[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
					self.check_over_configs(beta_start=lowerCamelCase_,					beta_end=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Optional[int]      =		torch.manual_seed(0			)

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      Dict      =		torch.manual_seed(0			)

			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Any      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[Any]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
			assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1E-3


