




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 SchedulerMixin


UpperCamelCase__              =						logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = 42


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):
					_lowerCamelCase		  = 1

					@register_to_config
					def __init__(				self ,       __lowercase = 40 ,       __lowercase = 0.0_02 ,       __lowercase = 80.0 ,       __lowercase = 0.5 ,       __lowercase = 1.0 ,       __lowercase = 7.0 ,       __lowercase = True ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							sigma_max

						lowerCamelCase_							:		Dict							   =							np.linspace(0 ,       1 ,       __lowercase					)
						lowerCamelCase_							:		List[Any]							   =							self._convert_to_karras(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							self.sigma_to_t(__lowercase					)

						# setable values
						lowerCamelCase_							:		Optional[int]							   =							None
						lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							torch.from_numpy(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							False
						lowerCamelCase_							:		List[str]							   =							False

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase=None					):







						'''simple docstring'''




						if schedule_timesteps is None:
							lowerCamelCase_							:		Optional[Any]							   =							self.timesteps

						lowerCamelCase_							:		Optional[int]							   =							(schedule_timesteps == timestep).nonzero()
						return indices.item()

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if isinstance(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		List[Any]							   =							timestep.to(self.timesteps.device					)
						lowerCamelCase_							:		List[str]							   =							self.index_for_timestep(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							self.sigmas[step_idx]

						lowerCamelCase_							:		int							   =							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

						lowerCamelCase_							:		int							   =							True
						return sample

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						if not isinstance(__lowercase ,       np.ndarray					):
							lowerCamelCase_							:		str							   =							np.array(__lowercase ,       dtype=np.floataa					)

						lowerCamelCase_							:		Optional[Any]							   =							1_000 * 0.25 * np.log(sigmas + 1E-44					)

						return timesteps

					def 	lowercase_							(				self ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       ):







						'''simple docstring'''




						if num_inference_steps is None and timesteps is None:
							raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'					)

						if num_inference_steps is not None and timesteps is not None:
							raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'					)

						# Follow DDPMScheduler custom timesteps logic
						if timesteps is not None:
							for i in range(1 ,       len(__lowercase					)					):
								if timesteps[i] >= timesteps[i - 1]:
									raise ValueError('`timesteps` must be in descending order.'					)

							if timesteps[0] >= self.config.num_train_timesteps:
								raise ValueError(
								    f"`timesteps` must start before `self.config.train_timesteps`:"
								    f" {self.config.num_train_timesteps}."					)

							lowerCamelCase_							:		Optional[int]							   =							np.array(__lowercase ,       dtype=np.intaa					)
							lowerCamelCase_							:		Union[str, Any]							   =							True
						else:
							if num_inference_steps > self.config.num_train_timesteps:
								raise ValueError(
								    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
								    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
								    f" maximal {self.config.num_train_timesteps} timesteps."					)

							lowerCamelCase_							:		Tuple							   =							num_inference_steps

							lowerCamelCase_							:		int							   =							self.config.num_train_timesteps // self.num_inference_steps
							lowerCamelCase_							:		str							   =							(np.arange(0 ,       __lowercase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
							lowerCamelCase_							:		Optional[int]							   =							False

						# Map timesteps to Karras sigmas directly for multistep sampling
						# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
						lowerCamelCase_							:		Optional[Any]							   =							self.config.num_train_timesteps
						lowerCamelCase_							:		str							   =							timesteps[::-1].copy()
						lowerCamelCase_							:		Optional[int]							   =							ramp / (num_train_timesteps - 1)
						lowerCamelCase_							:		int							   =							self._convert_to_karras(__lowercase					)
						lowerCamelCase_							:		int							   =							self.sigma_to_t(__lowercase					)

						lowerCamelCase_							:		List[str]							   =							np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
						lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					).to(device=__lowercase					)

						if str(__lowercase					).startswith('mps'					):
							# mps does not support float64
							lowerCamelCase_							:		List[str]							   =							torch.from_numpy(__lowercase					).to(__lowercase ,       dtype=torch.floataa					)
						else:
							lowerCamelCase_							:		Dict							   =							torch.from_numpy(__lowercase					).to(device=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		float							   =							self.config.sigma_min
						lowerCamelCase_							:		float							   =							self.config.sigma_max

						lowerCamelCase_							:		Union[str, Any]							   =							self.config.rho
						lowerCamelCase_							:		Any							   =							sigma_min ** (1 / rho)
						lowerCamelCase_							:		Any							   =							sigma_max ** (1 / rho)
						lowerCamelCase_							:		List[str]							   =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.config.sigma_data

						lowerCamelCase_							:		str							   =							sigma_data**2 / (sigma**2 + sigma_data**2)
						lowerCamelCase_							:		Dict							   =							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							self.config.sigma_min
						lowerCamelCase_							:		str							   =							self.config.sigma_data

						lowerCamelCase_							:		List[Any]							   =							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
						lowerCamelCase_							:		Any							   =							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       __lowercase = True ,       ):







						'''simple docstring'''




						if (
						    isinstance(__lowercase ,       __lowercase					)
						    or isinstance(__lowercase ,       torch.IntTensor					)
						    or isinstance(__lowercase ,       torch.LongTensor					)
						):
							raise ValueError(
							    (
							        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
							        f" `{self.__class__}.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(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		Union[str, Any]							   =							timestep.to(self.timesteps.device					)

						lowerCamelCase_							:		List[Any]							   =							self.config.sigma_min
						lowerCamelCase_							:		Union[str, Any]							   =							self.config.sigma_max

						lowerCamelCase_							:		Tuple							   =							self.index_for_timestep(__lowercase					)

						# sigma_next corresponds to next_t in original implementation
						lowerCamelCase_							:		Tuple							   =							self.sigmas[step_index]
						if step_index + 1 < self.config.num_train_timesteps:
							lowerCamelCase_							:		Optional[int]							   =							self.sigmas[step_index + 1]
						else:
							# Set sigma_next to sigma_min
							lowerCamelCase_							:		Dict							   =							self.sigmas[-1]

						# Get scalings for boundary conditions
						lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							self.get_scalings_for_boundary_condition(__lowercase					)

						# 1. Denoise model output using boundary conditions
						lowerCamelCase_							:		Any							   =							c_out * model_output + c_skip * sample
						if self.config.clip_denoised:
							lowerCamelCase_							:		Dict							   =							denoised.clamp(-1 ,       1					)

						# 2. Sample z ~ N(0, s_noise^2 * I)
						# Noise is not used for onestep sampling.
						if len(self.timesteps					) > 1:
							lowerCamelCase_							:		List[Any]							   =							randn_tensor(
							    model_output.shape ,       dtype=model_output.dtype ,       device=model_output.device ,       generator=__lowercase					)
						else:
							lowerCamelCase_							:		Tuple							   =							torch.zeros_like(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							noise * self.config.s_noise

						lowerCamelCase_							:		Union[str, Any]							   =							sigma_next.clamp(min=__lowercase ,       max=__lowercase					)

						# 3. Return noisy sample
						# tau = sigma_hat, eps = sigma_min
						lowerCamelCase_							:		List[Any]							   =							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

						if not return_dict:
							return (prev_sample,)

						return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       ):







						'''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(__lowercase					):
							# mps does not support float64
							lowerCamelCase_							:		Union[str, Any]							   =							self.timesteps.to(original_samples.device ,       dtype=torch.floataa					)
							lowerCamelCase_							:		List[str]							   =							timesteps.to(original_samples.device ,       dtype=torch.floataa					)
						else:
							lowerCamelCase_							:		str							   =							self.timesteps.to(original_samples.device					)
							lowerCamelCase_							:		Optional[int]							   =							timesteps.to(original_samples.device					)

						lowerCamelCase_							:		str							   =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						lowerCamelCase_							:		List[str]							   =							sigmas[step_indices].flatten()
						while len(sigma.shape					) < len(original_samples.shape					):
							lowerCamelCase_							:		Optional[Any]							   =							sigma.unsqueeze(-1					)

						lowerCamelCase_							:		Optional[int]							   =							original_samples + noise * sigma
						return noisy_samples





					def __len__(				self					):







						'''simple docstring'''




						return self.config.num_train_timesteps








"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 lowerCAmelCase_		:			int				   =					None


lowerCAmelCase_		:			Tuple				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			List[Any]				   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_		:			List[Any]				   =					{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_		:			int				   =					{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
lowerCAmelCase_		:			List[Any]				   =					['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class      UpperCamelCase_       (				a_     ):
  _A				:    Union[str, Any] =		VOCAB_FILES_NAMES
  _A				:    Optional[int] =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A				:    Tuple =		PRETRAINED_VOCAB_FILES_MAP
  _A				:    Dict =		['input_ids', 'attention_mask']
  _A				:    Any =		MBartaaTokenizer

  _A				:    List[int] =		[]
  _A				:    List[int] =		[]



  def __init__(					self       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__="</s>"       ,						snake_case__="</s>"       ,						snake_case__="<s>"       ,						snake_case__="<unk>"       ,						snake_case__="<pad>"       ,						snake_case__="<mask>"       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	AddedToken(snake_case__       ,						lstrip=snake_case__       ,						rstrip=snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else mask_token

         UpperCAmelCase					=	kwargs.get("""additional_special_tokens"""       ,						[] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             snake_case__       ,						src_lang=snake_case__       ,						tgt_lang=snake_case__       ,						tokenizer_file=snake_case__       ,						eos_token=snake_case__       ,						sep_token=snake_case__       ,						cls_token=snake_case__       ,						unk_token=snake_case__       ,						pad_token=snake_case__       ,						mask_token=snake_case__       ,						**snake_case__       ,						)

         UpperCAmelCase					=	vocab_file
         UpperCAmelCase					=	False if not self.vocab_file else True

         UpperCAmelCase					=	{
             lang_code: self.convert_tokens_to_ids(snake_case__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         UpperCAmelCase					=	src_lang if src_lang is not None else """en_XX"""
         UpperCAmelCase					=	tgt_lang
         UpperCAmelCase					=	self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )



  @property
  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         return self._src_lang



  @src_lang.setter
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """simple docstring"""





         if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = "en_XX"       ,						snake_case__ = None       ,						snake_case__ = "ro_RO"       ,						**snake_case__       ,						)	->	BatchEncoding:



         """simple docstring"""





         UpperCAmelCase					=	src_lang
         UpperCAmelCase					=	tgt_lang
         return super().prepare_seqaseq_batch(snake_case__       ,						snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self )	->	int:



         """simple docstring"""





         return self.set_src_lang_special_tokens(self.src_lang )



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



         """simple docstring"""





         return self.set_tgt_lang_special_tokens(self.tgt_lang )



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	[self.cur_lang_code_id]
         UpperCAmelCase					=	[self.eos_token_id]

         UpperCAmelCase					=	self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase					=	self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase					=	processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,						self.prefix_tokens + self.suffix_tokens ) )       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	[self.cur_lang_code_id]
         UpperCAmelCase					=	[self.eos_token_id]

         UpperCAmelCase					=	self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase					=	self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase					=	processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,						self.prefix_tokens + self.suffix_tokens ) )       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						**snake_case__ )	->	Dict:



         """simple docstring"""





         if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCAmelCase					=	src_lang
         UpperCAmelCase					=	self(snake_case__       ,						add_special_tokens=snake_case__       ,						return_tensors=snake_case__       ,						**snake_case__ )
         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	tgt_lang_id
         return inputs



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	Tuple[str]:



         """simple docstring"""





         if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

         if not os.path.isdir(snake_case__ ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
                return
         UpperCAmelCase					=	os.path.join(
             snake_case__       ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
                copyfile(self.vocab_file       ,						snake_case__ )

         return (out_vocab_file,)
