


'''simple docstring'''


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        __magic_name__  (					A__   ):

     """simple docstring"""






     lowerCamelCase__						=							'''gpt2'''
     lowerCamelCase__						=							['''past_key_values''']
     lowerCamelCase__						=							{
         '''hidden_size''': '''n_embd''',
         '''max_position_embeddings''': '''n_positions''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }


     def __init__(      self ,    lowerCamelCase=5_0257 ,    lowerCamelCase=1024 ,    lowerCamelCase=768 ,    lowerCamelCase=12 ,    lowerCamelCase=12 ,    lowerCamelCase=None ,    lowerCamelCase="gelu_new" ,    lowerCamelCase=0.1 ,    lowerCamelCase=0.1 ,    lowerCamelCase=0.1 ,    lowerCamelCase=1E-5 ,    lowerCamelCase=0.02 ,    lowerCamelCase="cls_index" ,    lowerCamelCase=True ,    lowerCamelCase=None ,    lowerCamelCase=True ,    lowerCamelCase=0.1 ,    lowerCamelCase=True ,    lowerCamelCase=True ,    lowerCamelCase=5_0256 ,    lowerCamelCase=5_0256 ,    lowerCamelCase=False ,    lowerCamelCase=False ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       vocab_size
          __A  :					Any						     =       n_positions
          __A  :					Optional[Any]						     =       n_embd
          __A  :					Any						     =       n_layer
          __A  :					Dict						     =       n_head
          __A  :					Dict						     =       n_inner
          __A  :					Any						     =       activation_function
          __A  :					Tuple						     =       resid_pdrop
          __A  :					Tuple						     =       embd_pdrop
          __A  :					Dict						     =       attn_pdrop
          __A  :					str						     =       layer_norm_epsilon
          __A  :					Any						     =       initializer_range
          __A  :					Dict						     =       summary_type
          __A  :					Tuple						     =       summary_use_proj
          __A  :					str						     =       summary_activation
          __A  :					Dict						     =       summary_first_dropout
          __A  :					Optional[int]						     =       summary_proj_to_labels
          __A  :					Optional[Any]						     =       scale_attn_weights
          __A  :					List[Any]						     =       use_cache
          __A  :					Dict						     =       scale_attn_by_inverse_layer_idx
          __A  :					List[Any]						     =       reorder_and_upcast_attn

          __A  :					int						     =       bos_token_id
          __A  :					Dict						     =       eos_token_id

          super().__init__(bos_token_id=lowerCamelCase ,    eos_token_id=lowerCamelCase ,    **lowerCamelCase		)







class        __magic_name__  (					A__   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase = "default" ,    lowerCamelCase = None ,    lowerCamelCase = False ,    ):


          '''simple docstring'''





          super().__init__(lowerCamelCase ,    task=lowerCamelCase ,    patching_specs=lowerCamelCase ,    use_past=lowerCamelCase		)
          if not getattr(self._config ,    "pad_token_id" ,    lowerCamelCase		):
               # TODO: how to do that better?
               __A  :					List[str]						     =       0


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
          if self.use_past:
               self.fill_with_past_key_values_(lowerCamelCase ,    direction="inputs"		)
               __A  :					int						     =       {0: "batch", 1: "past_sequence + sequence"}
          else:
               __A  :					List[Any]						     =       {0: "batch", 1: "sequence"}

          return common_inputs


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self._config.n_layer


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self._config.n_head


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = -1 ,    lowerCamelCase = -1 ,    lowerCamelCase = False ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					int						     =       super(lowerCamelCase ,    self		).generate_dummy_inputs(
              lowerCamelCase ,    batch_size=lowerCamelCase ,    seq_length=lowerCamelCase ,    is_pair=lowerCamelCase ,    framework=lowerCamelCase		)

          # We need to order the input in the way they appears in the forward()
          __A  :					Union[str, Any]						     =       OrderedDict({"input_ids": common_inputs["input_ids"]}		)

          # Need to add the past_keys
          if self.use_past:
               if not is_torch_available():
                    raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
               else:
                    import torch

                    __A  :					Optional[int]						     =       common_inputs["input_ids"].shape
                    # Not using the same length for past_key_values
                    __A  :					List[Any]						     =       seqlen + 2
                    __A  :					str						     =       (
                        batch,
                        self.num_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // self.num_attention_heads,
                    )
                    __A  :					str						     =       [
                        (torch.zeros(lowerCamelCase		), torch.zeros(lowerCamelCase		)) for _ in range(self.num_layers		)
                    ]

          __A  :					Tuple						     =       common_inputs["attention_mask"]
          if self.use_past:
               __A  :					str						     =       ordered_inputs["attention_mask"].dtype
               __A  :					Optional[Any]						     =       torch.cat(
                   [ordered_inputs["attention_mask"], torch.ones(lowerCamelCase ,    lowerCamelCase ,    dtype=lowerCamelCase		)] ,    dim=1		)

          return ordered_inputs


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return 13









'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(ScoreSdeVeScheduler,)
     lowerCamelCase__						=							()


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       4
          __A  :					Union[str, Any]						     =       3
          __A  :					Optional[int]						     =       8
          __A  :					Dict						     =       8

          __A  :					Tuple						     =       torch.rand((batch_size, num_channels, height, width)		)

          return sample


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       4
          __A  :					Dict						     =       3
          __A  :					str						     =       8
          __A  :					List[Any]						     =       8

          __A  :					Dict						     =       batch_size * num_channels * height * width
          __A  :					List[str]						     =       torch.arange(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       sample.reshape(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[Any]						     =       sample / num_elems
          __A  :					Dict						     =       sample.permute(3 ,    0 ,    1 ,    2		)

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          def model(lowerCamelCase ,    lowerCamelCase ,    *lowerCamelCase		):
               return sample * t / (t + 1)

          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       dict(self.forward_default_kwargs		)

          for scheduler_class in self.scheduler_classes:
               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[Any]						     =       0.1 * sample

               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Tuple						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					List[str]						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

               __A  :					Dict						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[Any]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler correction are not identical"


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       dict(self.forward_default_kwargs		)
          kwargs.update(lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               __A  :					str						     =       self.get_scheduler_config()
               __A  :					Optional[Any]						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

               __A  :					List[str]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler correction are not identical"


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01] ,    [1, 100, 1000]		):
               self.check_over_configs(sigma_min=lowerCamelCase ,    sigma_max=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					Dict						     =       self.scheduler_classes[0]
          __A  :					str						     =       self.get_scheduler_config()
          __A  :					List[Any]						     =       scheduler_class(**lowerCamelCase		)

          __A  :					Union[str, Any]						     =       3

          __A  :					Union[str, Any]						     =       self.dummy_model()
          __A  :					Union[str, Any]						     =       self.dummy_sample_deter

          scheduler.set_sigmas(lowerCamelCase		)
          scheduler.set_timesteps(lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.manual_seed(0		)

          for i, t in enumerate(scheduler.timesteps		):
               __A  :					int						     =       scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps		):
                    with torch.no_grad():
                         __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Optional[int]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		).prev_sample

               with torch.no_grad():
                    __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		)
               __A     ,__A  :					int						     =       output.prev_sample, output.prev_sample_mean

          __A  :					Union[str, Any]						     =       torch.sum(torch.abs(lowerCamelCase		)		)
          __A  :					str						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert np.isclose(result_sum.item() ,    143_7275_8528.0		)
          assert np.isclose(result_mean.item() ,    1871_4530.0		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					List[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					int						     =       self.get_scheduler_config()
               __A  :					Any						     =       scheduler_class(**lowerCamelCase		)

               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase ,    "set_timesteps"		):
                    scheduler.set_timesteps(lowerCamelCase		)
               elif num_inference_steps is not None and not hasattr(lowerCamelCase ,    "set_timesteps"		):
                    __A  :					Any						     =       num_inference_steps

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    0 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       scheduler.step_pred(lowerCamelCase ,    1 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

               self.assertEqual(output_a.shape ,    sample.shape		)
               self.assertEqual(output_a.shape ,    output_a.shape		)

