


'''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









'''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.",
											    ]			,				)





