


'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				(DDPMScheduler,)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for timesteps in [1, 5, 100, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,   [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_    ,   beta_end=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							self.check_over_configs(thresholding=UpperCamelCase_							)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=UpperCamelCase_    ,   prediction_type=UpperCamelCase_    ,   sample_max_value=UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for t in [0, 500, 999]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :int												= self.scheduler_classes[0]
							UpperCamelCase__   :Tuple												= self.get_scheduler_config()
							UpperCamelCase__   :Union[str, Any]												= scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1e-5




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.scheduler_classes[0]
							UpperCamelCase__   :Any												= self.get_scheduler_config()
							UpperCamelCase__   :int												= scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__   :Dict												= len(UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= self.dummy_model()
							UpperCamelCase__   :int												= self.dummy_sample_deter
							UpperCamelCase__   :Optional[Any]												= torch.manual_seed(0							)

							for t in reversed(range(UpperCamelCase_							)							):
												# 1. predict noise residual
												UpperCamelCase__   :Dict												= model(UpperCamelCase_    ,   UpperCamelCase_							)

												# 2. predict previous mean of sample x_t-1
												UpperCamelCase__   :Any												= scheduler.step(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   generator=UpperCamelCase_							).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												UpperCamelCase__   :Optional[int]												= pred_prev_sample

							UpperCamelCase__   :Dict												= torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__   :Optional[Any]												= torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 258.9606							) < 1e-2
							assert abs(result_mean.item() - 0.3372							) < 1e-3




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.scheduler_classes[0]
							UpperCamelCase__   :Union[str, Any]												= self.get_scheduler_config(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :List[Any]												= scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= len(UpperCamelCase_							)

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

							for t in reversed(range(UpperCamelCase_							)							):
												# 1. predict noise residual
												UpperCamelCase__   :Tuple												= model(UpperCamelCase_    ,   UpperCamelCase_							)

												# 2. predict previous mean of sample x_t-1
												UpperCamelCase__   :Optional[int]												= scheduler.step(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   generator=UpperCamelCase_							).prev_sample

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

							UpperCamelCase__   :List[str]												= torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__   :Tuple												= torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 202.0296							) < 1e-2
							assert abs(result_mean.item() - 0.2631							) < 1e-3




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.scheduler_classes[0]
							UpperCamelCase__   :Optional[Any]												= self.get_scheduler_config()
							UpperCamelCase__   :List[Any]												= scheduler_class(**UpperCamelCase_							)

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

							scheduler.set_timesteps(timesteps=UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= scheduler.timesteps

							for i, timestep in enumerate(UpperCamelCase_							):
												if i == len(UpperCamelCase_							) - 1:
																	UpperCamelCase__   :List[str]												= -1
												else:
																	UpperCamelCase__   :List[str]												= timesteps[i + 1]

												UpperCamelCase__   :Optional[int]												= scheduler.previous_timestep(UpperCamelCase_							)
												UpperCamelCase__   :Optional[Any]												= prev_t.item()

												self.assertEqual(UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.scheduler_classes[0]
							UpperCamelCase__   :List[Any]												= self.get_scheduler_config()
							UpperCamelCase__   :Union[str, Any]												= scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= [100, 87, 50, 51, 0]

							with self.assertRaises(UpperCamelCase_    ,   msg='''`custom_timesteps` must be in descending order.'''							):
												scheduler.set_timesteps(timesteps=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self.scheduler_classes[0]
							UpperCamelCase__   :Union[str, Any]												= self.get_scheduler_config()
							UpperCamelCase__   :Union[str, Any]												= scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= [100, 87, 50, 1, 0]
							UpperCamelCase__   :List[Any]												= len(UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.scheduler_classes[0]
							UpperCamelCase__   :Any												= self.get_scheduler_config()
							UpperCamelCase__   :Dict												= scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= [scheduler.config.num_train_timesteps]

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



import copy
import tempfile
import unittest

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

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @parameterized.expand([(None,), ("foo.json",)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            _A      :       str				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)
                   _A      :       Tuple				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)

            # Checks parameters that were specified
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.temperature ,						0.7)
            self.assertEqual(loaded_config.length_penalty ,						1.0)
            self.assertEqual(loaded_config.bad_words_ids ,						[[1, 2, 3], [4, 5]])

            # Checks parameters that were not specified (defaults)
            self.assertEqual(loaded_config.top_k ,						5_0)
            self.assertEqual(loaded_config.max_length ,						2_0)
            self.assertEqual(loaded_config.max_time ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = AutoConfig.from_pretrained("gpt2")
            _A      :       int				    = GenerationConfig.from_model_config(__lowerCamelCase)
            _A      :       List[Any]				    = GenerationConfig()

            # The generation config has loaded a few non-default parameters from the model config
            self.assertNotEqual(__lowerCamelCase ,						__lowerCamelCase)

            # One of those parameters is eos_token_id -- check if it matches
            self.assertNotEqual(generation_config_from_model.eos_token_id ,						default_generation_config.eos_token_id)
            self.assertEqual(generation_config_from_model.eos_token_id ,						model_config.eos_token_id)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[Any]				    = GenerationConfig()
            _A      :       List[Any]				    = {
                "max_new_tokens": 1_0_2_4,
                "foo": "bar",
            }
            _A      :       List[str]				    = copy.deepcopy(__lowerCamelCase)
            _A      :       int				    = generation_config.update(**__lowerCamelCase)

            # update_kwargs was not modified (no side effects)
            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)

            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(generation_config.max_new_tokens ,						1_0_2_4)

            # `.update()` returns a dictionary of unused kwargs
            self.assertEqual(__lowerCamelCase ,						{"foo": "bar"})


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       int				    = GenerationConfig()
            _A      :       int				    = "bar"

            with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
                   generation_config.save_pretrained(__lowerCamelCase)

                   _A      :       Any				    = GenerationConfig.from_pretrained(__lowerCamelCase)
            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(new_config.foo ,						"bar")

            _A      :       Optional[Any]				    = GenerationConfig.from_model_config(__lowerCamelCase)
            assert not hasattr(__lowerCamelCase ,						"foo")  # no new kwargs should be initialized if from config


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Union[str, Any]				    = GenerationConfig()
            self.assertEqual(default_config.temperature ,						1.0)
            self.assertEqual(default_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(default_config.num_beams ,						1)

            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            self.assertEqual(config.temperature ,						0.7)
            self.assertEqual(config.do_sample ,						__lowerCamelCase)
            self.assertEqual(config.num_beams ,						1)

            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase)
                   _A      :       Optional[int]				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						temperature=1.0)

            self.assertEqual(loaded_config.temperature ,						1.0)
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.num_beams ,						1)  # default value







@is_staging_test
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @classmethod
     def  _lowerCamelCase (						cls)			->				Optional[int]:
            _A      :       Dict				    = TOKEN
            HfFolder.save_token(__lowerCamelCase)


     @classmethod
     def  _lowerCamelCase (						cls)			->				List[Any]:
            try:
                   delete_repo(token=cls._token ,						repo_id="test-generation-config")
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token ,						repo_id="valid_org/test-generation-config-org")
            except HTTPError:
                   pass


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("test-generation-config" ,						use_auth_token=self._token)

            _A      :       Union[str, Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="test-generation-config")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="test-generation-config" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Union[str, Any]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("valid_org/test-generation-config-org" ,						use_auth_token=self._token)

            _A      :       int				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="valid_org/test-generation-config-org")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="valid_org/test-generation-config-org" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[int]				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))
