code: 
  
  
  '''simple docstring'''
  
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  __lowercase  :      Any      		=				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class _snake_case   ( lowerCAmelCase__   ):
         """simple docstring"""
  
  
  
  
  
         __a												=     4_2
         __a												=     None
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase=0.9_9_9							,						lowercase="cosine"							,						)			->Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
  
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(lowercase    ):
              return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(lowercase    ):
              return math.exp(t * -1_2.0    )
  
      else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )
  
      __lowerCamelCase		:      Any											=					[]
      for i in range(lowercase    ):
          __lowerCamelCase		:      Any											=					i / num_diffusion_timesteps
          __lowerCamelCase		:      Optional[Any]											=					(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowercase    ) / alpha_bar_fn(lowercase    )							,						lowercase    )    )
      return torch.tensor(lowercase							,						dtype=torch.floataa    )
  
  
  
  
  
  class _snake_case   ( lowerCAmelCase__    ,   lowerCAmelCase__   ):
         """simple docstring"""
  
  
  
  
  
         __a												=     [e.name for e in KarrasDiffusionSchedulers]
         __a												=     1
  
  
  
  
  
  
  
         @register_to_config
         def __init__(   self			,				__A = 1000			,				__A = 0.0001			,				__A = 0.02			,				__A = "linear"			,				__A = None			,				__A = "epsilon"			,				__A = "linspace"			,				__A = 0			,				)     ->Optional[int]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             if trained_betas is not None:
                 __lowerCamelCase		:      Union[str, Any]											=					torch.tensor(_lowerCamelCase			,				dtype=torch.floataa	)
             elif beta_schedule == "linear":
                 __lowerCamelCase		:      Dict											=					torch.linspace(_lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase			,				dtype=torch.floataa	)
             elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 __lowerCamelCase		:      Optional[int]											=					(
                     torch.linspace(beta_start**0.5			,				beta_end**0.5			,				_lowerCamelCase			,				dtype=torch.floataa	) ** 2
                 )
             elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 __lowerCamelCase		:      Any											=					betas_for_alpha_bar(_lowerCamelCase	)
             else:
                 raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'	)
  
             __lowerCamelCase		:      List[str]											=					1.0 - self.betas
             __lowerCamelCase		:      Any											=					torch.cumprod(self.alphas			,				dim=0	)
  
             __lowerCamelCase		:      Tuple											=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
             __lowerCamelCase		:      Optional[int]											=					np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
             __lowerCamelCase		:      Tuple											=					torch.from_numpy(_lowerCamelCase	)
  
             # setable values
             __lowerCamelCase		:      int											=					None
             __lowerCamelCase		:      Any											=					np.linspace(0			,				num_train_timesteps - 1			,				_lowerCamelCase			,				dtype=_lowerCamelCase	)[::-1].copy()
             __lowerCamelCase		:      int											=					torch.from_numpy(_lowerCamelCase	)
             __lowerCamelCase		:      Optional[Any]											=					False
  
  
  
  
  
  
  
         @property
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             if self.config.timestep_spacing in ["linspace", "trailing"]:
                 return self.sigmas.max()
  
             return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A	)     ->Tuple:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             if isinstance(_lowerCamelCase			,				torch.Tensor	):
                 __lowerCamelCase		:      Optional[int]											=					timestep.to(self.timesteps.device	)
             __lowerCamelCase		:      List[str]											=					(self.timesteps == timestep).nonzero().item()
             __lowerCamelCase		:      int											=					self.sigmas[step_index]
             __lowerCamelCase		:      List[Any]											=					sample / ((sigma**2 + 1) ** 0.5)
             __lowerCamelCase		:      int											=					True
             return sample
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->Dict:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Dict											=					num_inference_steps
  
             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                 __lowerCamelCase		:      Union[str, Any]											=					np.linspace(0			,				self.config.num_train_timesteps - 1			,				_lowerCamelCase			,				dtype=_lowerCamelCase	)[
                     ::-1
                 ].copy()
             elif self.config.timestep_spacing == "leading":
                 __lowerCamelCase		:      Any											=					self.config.num_train_timesteps // self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __lowerCamelCase		:      Tuple											=					(np.arange(0			,				_lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase	)
                 timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                 __lowerCamelCase		:      int											=					self.config.num_train_timesteps / self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __lowerCamelCase		:      int											=					(np.arange(self.config.num_train_timesteps			,				0			,				-step_ratio	)).round().copy().astype(_lowerCamelCase	)
                 timesteps -= 1
             else:
                 raise ValueError(
                     f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'	)
  
             __lowerCamelCase		:      Any											=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
             __lowerCamelCase		:      Dict											=					np.interp(_lowerCamelCase			,				np.arange(0			,				len(_lowerCamelCase	)	)			,				_lowerCamelCase	)
             __lowerCamelCase		:      Union[str, Any]											=					np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
             __lowerCamelCase		:      Tuple											=					torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)
             if str(_lowerCamelCase	).startswith("mps"	):
                 # mps does not support float64
                 __lowerCamelCase		:      List[Any]											=					torch.from_numpy(_lowerCamelCase	).to(_lowerCamelCase			,				dtype=torch.floataa	)
             else:
                 __lowerCamelCase		:      List[Any]											=					torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A			,				__A = None			,				__A = True			,				)     ->Union[str, Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             if (
                 isinstance(_lowerCamelCase			,				_lowerCamelCase	)
                 or isinstance(_lowerCamelCase			,				torch.IntTensor	)
                 or isinstance(_lowerCamelCase			,				torch.LongTensor	)
             ):
                 raise ValueError(
                     (
                         "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                         " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                         " one of the `scheduler.timesteps` as a timestep."
                     )			,				)
  
             if not self.is_scale_input_called:
                 logger.warning(
                     "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                     "See `StableDiffusionPipeline` for a usage example."	)
  
             if isinstance(_lowerCamelCase			,				torch.Tensor	):
                 __lowerCamelCase		:      Tuple											=					timestep.to(self.timesteps.device	)
  
             __lowerCamelCase		:      Dict											=					(self.timesteps == timestep).nonzero().item()
             __lowerCamelCase		:      Optional[Any]											=					self.sigmas[step_index]
  
             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                 __lowerCamelCase		:      Tuple											=					sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 __lowerCamelCase		:      Tuple											=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
             elif self.config.prediction_type == "sample":
                 raise NotImplementedError("prediction_type not implemented yet: sample"	)
             else:
                 raise ValueError(
                     f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'	)
  
             __lowerCamelCase		:      Dict											=					self.sigmas[step_index]
             __lowerCamelCase		:      Optional[int]											=					self.sigmas[step_index + 1]
             __lowerCamelCase		:      List[Any]											=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
             __lowerCamelCase		:      List[Any]											=					(sigma_to**2 - sigma_up**2) ** 0.5
  
             # 2. Convert to an ODE derivative
             __lowerCamelCase		:      Union[str, Any]											=					(sample - pred_original_sample) / sigma
  
             __lowerCamelCase		:      Optional[Any]											=					sigma_down - sigma
  
             __lowerCamelCase		:      Tuple											=					sample + derivative * dt
  
             __lowerCamelCase		:      Dict											=					model_output.device
             __lowerCamelCase		:      Optional[Any]											=					randn_tensor(model_output.shape			,				dtype=model_output.dtype			,				device=_lowerCamelCase			,				generator=_lowerCamelCase	)
  
             __lowerCamelCase		:      Optional[Any]											=					prev_sample + noise * sigma_up
  
             if not return_dict:
                 return (prev_sample,)
  
             return EulerAncestralDiscreteSchedulerOutput(
                 prev_sample=_lowerCamelCase			,				pred_original_sample=_lowerCamelCase	)
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A			,				)     ->Optional[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Any											=					self.sigmas.to(device=original_samples.device			,				dtype=original_samples.dtype	)
             if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase	):
                 # mps does not support float64
                 __lowerCamelCase		:      Any											=					self.timesteps.to(original_samples.device			,				dtype=torch.floataa	)
                 __lowerCamelCase		:      str											=					timesteps.to(original_samples.device			,				dtype=torch.floataa	)
             else:
                 __lowerCamelCase		:      List[str]											=					self.timesteps.to(original_samples.device	)
                 __lowerCamelCase		:      Dict											=					timesteps.to(original_samples.device	)
  
             __lowerCamelCase		:      Optional[int]											=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
             __lowerCamelCase		:      Optional[int]											=					sigmas[step_indices].flatten()
             while len(sigma.shape	) < len(original_samples.shape	):
                 __lowerCamelCase		:      List[Any]											=					sigma.unsqueeze(-1	)
  
             __lowerCamelCase		:      Union[str, Any]											=					original_samples + noise * sigma
             return noisy_samples
  
  
  
  
  
  
  
         def __len__(   self	)     ->List[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  
  from transformers import ErnieMTokenizer
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  __lowercase  :      Any      		=				get_tests_dir("""fixtures/spiece.model""")
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  class _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     ErnieMTokenizer
  							__a												=     False
  							__a												=     True
  							__a												=     False
  							__a												=     False
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().setUp()
  
  											# We have a SentencePiece fixture for testing
  											__lowerCamelCase		:      Optional[int]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  											tokenizer.save_pretrained(self.tmpdirname	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					"this is a test"
  											__lowerCamelCase		:      List[str]											=					"this is a test"
  											return input_text, output_text
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					"<pad>"
  											__lowerCamelCase		:      Any											=					0
  
  											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A	)			,				__A	)
  											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A	)			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					list(self.get_tokenizer().get_vocab().keys()	)
  
  											self.assertEqual(vocab_keys[0]			,				"<pad>"	)
  											self.assertEqual(vocab_keys[1]			,				"<unk>"	)
  											self.assertEqual(vocab_keys[-1]			,				"▁eloquent"	)
  											self.assertEqual(len(__A	)			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self.assertEqual(self.get_tokenizer().vocab_size			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not self.test_rust_tokenizer:
  															return
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.get_tokenizer()
  											__lowerCamelCase		:      Optional[Any]											=					self.get_rust_tokenizer()
  
  											__lowerCamelCase		:      Dict											=					"I was born in 92000, and this is falsé."
  
  											__lowerCamelCase		:      str											=					tokenizer.tokenize(__A	)
  											__lowerCamelCase		:      str											=					rust_tokenizer.tokenize(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.encode(__A			,				add_special_tokens=__A	)
  											__lowerCamelCase		:      Tuple											=					rust_tokenizer.encode(__A			,				add_special_tokens=__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Optional[int]											=					self.get_rust_tokenizer()
  											__lowerCamelCase		:      Dict											=					tokenizer.encode(__A	)
  											__lowerCamelCase		:      List[str]											=					rust_tokenizer.encode(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					ErnieMTokenizer(__A			,				do_lower_case=__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      Optional[Any]											=					tokenizer.tokenize("This is a test"	)
  											self.assertListEqual(__A			,				["▁this", "▁is", "▁a", "▁test"]	)
  
  											self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)			,				[48, 25, 21, 1289]	)
  
  											__lowerCamelCase		:      Any											=					tokenizer.tokenize("I was born in 92000, and this is falsé."	)
  											# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
  											self.assertListEqual(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.convert_tokens_to_ids(__A	)
  											self.assertListEqual(__A			,				[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)
  
  											__lowerCamelCase		:      Dict											=					tokenizer.convert_ids_to_tokens(__A	)
  											self.assertListEqual(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      int											=					tokenizer.encode("sequence builders"	)
  											__lowerCamelCase		:      List[Any]											=					tokenizer.encode("multi-sequence build"	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.build_inputs_with_special_tokens(__A	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.build_inputs_with_special_tokens(__A			,				__A	)
  
  											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
  											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
  											    tokenizer.sep_token_id
  											] + text_a + [tokenizer.sep_token_id]
  
  
  
  
  
  
  
  							@slow
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
  											# fmt: on
  
  											self.tokenizer_integration_test_util(
  											    expected_encoding=__A			,				model_name="susnato/ernie-m-base_pytorch"			,				sequences=[
  											        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
  											        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
  											        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
  											        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
  											        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
  											        "conditioning on both left and right context in all layers.",
  											        "The quick brown fox jumps over the lazy dog.",
  											    ]			,				)
  
  
  
  
  
  