

import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402


__UpperCAmelCase								=			get_tests_dir("""fixtures/dummy-config.json""")



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						0







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     self.assertIsNotNone(transformers.models.auto.__spec__   )
     self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						AutoConfig.from_pretrained("""bert-base-uncased"""   )
     self.assertIsInstance(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						AutoConfig.from_pretrained(lowerCAmelCase   )
     self.assertIsInstance(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						AutoConfig.from_pretrained(lowerCAmelCase   )
     self.assertIsInstance(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						AutoConfig.for_model("""roberta"""   )
     self.assertIsInstance(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Any:






     """simple docstring"""







     with tempfile.TemporaryDirectory() as tmp_dir:
        # This model name contains bert and roberta, but roberta ends up being picked.
        __lowerCAmelCase  :      List[Any]    =						os.path.join(lowerCAmelCase	,							"""fake-roberta"""   )
        os.makedirs(lowerCAmelCase	,							exist_ok=lowerCAmelCase   )
        with open(os.path.join(lowerCAmelCase	,							"""config.json"""   )	,							"""w"""   ) as f:
           f.write(json.dumps({}   )   )
        __lowerCAmelCase  :      Any    =						AutoConfig.from_pretrained(lowerCAmelCase   )
        self.assertEqual(type(lowerCAmelCase   )	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     try:
        AutoConfig.register("""custom"""	,							lowerCAmelCase   )
        # Wrong model type will raise an error
        with self.assertRaises(lowerCAmelCase   ):
           AutoConfig.register("""model"""	,							lowerCAmelCase   )
        # Trying to register something existing in the Transformers library will raise an error
        with self.assertRaises(lowerCAmelCase   ):
           AutoConfig.register("""bert"""	,							lowerCAmelCase   )

        # Now that the config is registered, it can be used as any other config with the auto-API
        __lowerCAmelCase  :      Optional[int]    =						CustomConfig()
        with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(lowerCAmelCase   )
           __lowerCAmelCase  :      Union[str, Any]    =						AutoConfig.from_pretrained(lowerCAmelCase   )
           self.assertIsInstance(lowerCAmelCase	,							lowerCAmelCase   )

     finally:
        if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[str]:






     """simple docstring"""







     with self.assertRaisesRegex(
         lowerCAmelCase	,							"""bert-base is not a local folder and is not a valid model identifier"""   ):
        __lowerCAmelCase  :      List[str]    =						AutoConfig.from_pretrained("""bert-base"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     int:






     """simple docstring"""







     with self.assertRaisesRegex(
         lowerCAmelCase	,							r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""   ):
        __lowerCAmelCase  :      Dict    =						AutoConfig.from_pretrained(lowerCAmelCase	,							revision="""aaaaaa"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     with self.assertRaisesRegex(
         lowerCAmelCase	,							"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""	,							):
        __lowerCAmelCase  :      Dict    =						AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     with self.assertRaises(lowerCAmelCase   ):
        __lowerCAmelCase  :      Any    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""   )
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(lowerCAmelCase   ):
        __lowerCAmelCase  :      List[Any]    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	,							trust_remote_code=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	,							trust_remote_code=lowerCAmelCase   )
     self.assertEqual(config.__class__.__name__	,							"""NewModelConfig"""   )

     # Test config can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						AutoConfig.from_pretrained(lowerCAmelCase	,							trust_remote_code=lowerCAmelCase   )
     self.assertEqual(reloaded_config.__class__.__name__	,							"""NewModelConfig"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     class SCREAMING_SNAKE_CASE     (  a_			):
       """simple docstring"""






       lowerCamelCase   :      str										="new-model"

     try:
        AutoConfig.register("""new-model"""	,							lowerCAmelCase   )
        # If remote code is not set, the default is to use local
        __lowerCAmelCase  :      str    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""   )
        self.assertEqual(config.__class__.__name__	,							"""NewModelConfigLocal"""   )

        # If remote code is disabled, we load the local one.
        __lowerCAmelCase  :      Any    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	,							trust_remote_code=lowerCAmelCase   )
        self.assertEqual(config.__class__.__name__	,							"""NewModelConfigLocal"""   )

        # If remote is enabled, we load from the Hub
        __lowerCAmelCase  :      Optional[Any]    =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	,							trust_remote_code=lowerCAmelCase   )
        self.assertEqual(config.__class__.__name__	,							"""NewModelConfig"""   )

     finally:
        if "new-model" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["new-model"]





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

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

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

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

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


