





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       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Any			):
			lowercase__					:      List[Any]      =		0

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[Any]      =		AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'			)
			self.assertIsInstance(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any]			):
			with tempfile.TemporaryDirectory() as tmpdirname:
					lowercase__					:      str      =		Path(lowerCamelCase_			) / 'preprocessor_config.json'
					lowercase__					:      List[Any]      =		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__					:      Dict      =		AutoImageProcessor.from_pretrained(lowerCamelCase_			)
					self.assertIsInstance(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			# Ensure we can load the image processor from the feature extractor config
			with tempfile.TemporaryDirectory() as tmpdirname:
					lowercase__					:      Optional[int]      =		Path(lowerCamelCase_			) / 'preprocessor_config.json'
					lowercase__					:      Optional[Any]      =		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__					:      Union[str, Any]      =		AutoImageProcessor.from_pretrained(lowerCamelCase_			)
					self.assertIsInstance(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Any			):
			with tempfile.TemporaryDirectory() as tmpdirname:
					lowercase__					:      Union[str, Any]      =		CLIPConfig()

					# Create a dummy config file with image_proceesor_type
					lowercase__					:      List[str]      =		Path(lowerCamelCase_			) / 'preprocessor_config.json'
					lowercase__					:      Optional[int]      =		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__					:      Any      =		AutoImageProcessor.from_pretrained(lowerCamelCase_			).to_dict()

					config_dict.pop('image_processor_type'			)
					lowercase__					:      int      =		CLIPImageProcessor(**lowerCamelCase_			)

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

					lowercase__					:      Union[str, Any]      =		AutoImageProcessor.from_pretrained(lowerCamelCase_			)

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

			self.assertIsInstance(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			with tempfile.TemporaryDirectory() as tmpdirname:
					lowercase__					:      Tuple      =		Path(lowerCamelCase_			) / 'preprocessor_config.json'
					json.dump(
					    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},					open(lowerCamelCase_,					'w'			),					)

					lowercase__					:      Dict      =		AutoImageProcessor.from_pretrained(lowerCamelCase_			)
					self.assertIsInstance(lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			with self.assertRaisesRegex(
			    lowerCamelCase_,					'clip-base is not a local folder and is not a valid model identifier'			):
					lowercase__					:      str      =		AutoImageProcessor.from_pretrained('clip-base'			)

	def    snake_case__(						self:						Union[str, Any]			):
			with self.assertRaisesRegex(
			    lowerCamelCase_,					r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'			):
					lowercase__					:      Tuple      =		AutoImageProcessor.from_pretrained(lowerCamelCase_,					revision='aaaaaa'			)

	def    snake_case__(						self:						Union[str, Any]			):
			with self.assertRaisesRegex(
			    lowerCamelCase_,					'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',					):
					lowercase__					:      List[str]      =		AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'			)

	def    snake_case__(						self:						List[str]			):
			# If remote code is not set, we will time out when asking whether to load the model.
			with self.assertRaises(lowerCamelCase_			):
					lowercase__					:      Optional[Any]      =		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__					:      str      =		AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor',					trust_remote_code=lowerCamelCase_			)

			lowercase__					:      List[Any]      =		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__					:      Dict      =		AutoImageProcessor.from_pretrained(lowerCamelCase_,					trust_remote_code=lowerCamelCase_			)
			self.assertEqual(reloaded_image_processor.__class__.__name__,					'NewImageProcessor'			)

	def    snake_case__(						self:						Union[str, Any]			):
			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__					:      Any      =		Path(lowerCamelCase_			) / 'preprocessor_config.json'
							lowercase__					:      List[str]      =		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__					:      Union[str, Any]      =		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__					:      Dict      =		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    snake_case__(						self:						Optional[Any]			):


			class       __lowerCAmelCase					(       _UpperCamelCase      ):


				'''simple docstring'''





				_A													=				True

			try:
					AutoConfig.register('custom',					lowerCamelCase_			)
					AutoImageProcessor.register(lowerCamelCase_,					lowerCamelCase_			)
					# If remote code is not set, the default is to use local
					lowercase__					:      str      =		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__					:      List[Any]      =		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__					:      Tuple      =		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


