



"""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     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      0



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



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

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



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

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      CLIPConfig()

       # Create a dummy config file with image_proceesor_type
       _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
       _snake_case   	=      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
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				).to_dict()

       config_dict.pop('''image_processor_type'''				)
       _snake_case   	=      CLIPImageProcessor(**__lowerCamelCase				)

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

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)

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

     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



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

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''clip-base is not a local folder and is not a valid model identifier'''				):
       _snake_case   	=      AutoImageProcessor.from_pretrained('''clip-base'''				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase							,       revision='''aaaaaa'''				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,       ):
       _snake_case   	=      AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



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

     _snake_case   	=      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				)
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase							,       trust_remote_code=__lowerCamelCase				)
     self.assertEqual(reloaded_image_processor.__class__.__name__							,       '''NewImageProcessor'''				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """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:
         _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
         _snake_case   	=      Path(__lowerCamelCase				) / '''config.json'''
         json.dump(
             {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )
         json.dump({'''model_type''': '''clip'''}							,       open(__lowerCamelCase							,       '''w'''				)				)

         _snake_case   	=      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				)
         _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""



     class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
        A__		:	int           =  True

     try:
       AutoConfig.register('''custom'''							,       __lowerCamelCase				)
       AutoImageProcessor.register(__lowerCamelCase							,       __lowerCamelCase				)
       # If remote code is not set, the default is to use local
       _snake_case   	=      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.
       _snake_case   	=      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
       _snake_case   	=      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]







"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

          with self.assertRaises(_a      ,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=_a      ,       timesteps=_a							)

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a      ,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,       ):
                 scheduler.set_timesteps(timesteps=_a							)


