



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)






@flax.struct.dataclass
class        UpperCAmelCase__						:
					a   :  List[str]   =				4_2

					# setable values
					a   :  Tuple   =				4_2
					a   :  List[str]   =				4_2
					a   :  Optional[Any]   =				4_2
					a   :  Any   =				None

					# running values
					a   :  Any   =				None



					@classmethod
					def 							UpperCAmelCase_			(     cls      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      int:
						return cls(common=UpperCamelCase      ,					init_noise_sigma=UpperCamelCase      ,					timesteps=UpperCamelCase      ,					sigmas=UpperCamelCase  )






@dataclass
class        UpperCAmelCase__						(		_lowerCamelCase			):
					a   :  Union[str, Any]   =				4_2






class        UpperCAmelCase__						(		_lowerCamelCase				,							_lowerCamelCase			):
					a   :  List[str]   =				[e.name for e in FlaxKarrasDiffusionSchedulers]

					a   :  List[Any]   =				4_2



					@property
					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						return True



					@register_to_config
					def __init__(     self      ,					UpperCamelCase = 1000      ,					UpperCamelCase = 0.00_01      ,					UpperCamelCase = 0.02      ,					UpperCamelCase = "linear"      ,					UpperCamelCase = None      ,					UpperCamelCase = "epsilon"      ,					UpperCamelCase = jnp.floataa      ,					)  ->      Optional[Any]:
						__lowerCAmelCase       				=	dtype



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase = None  )  ->      Tuple:


						if common is None:
							__lowerCAmelCase       				=	CommonSchedulerState.create(self  )

						__lowerCAmelCase       				=	jnp.arange(0      ,					self.config.num_train_timesteps  ).round()[::-1]
						__lowerCAmelCase       				=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						__lowerCAmelCase       				=	sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=UpperCamelCase      ,					init_noise_sigma=UpperCamelCase      ,					timesteps=UpperCamelCase      ,					sigmas=UpperCamelCase      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      int:
						((__lowerCAmelCase					)				,												)       				=	jnp.where(state.timesteps == timestep      ,					size=1  )
						__lowerCAmelCase       				=	step_index[0]

						__lowerCAmelCase       				=	state.sigmas[step_index]
						__lowerCAmelCase       				=	sample / ((sigma**2 + 1) ** 0.5)
						return sample



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      int:


						def lms_derivative(UpperCamelCase  ):
							__lowerCAmelCase       				=	1.0
							for k in range(UpperCamelCase  ):
								if current_order == k:
									continue
								prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
							return prod

						__lowerCAmelCase       				=	integrate.quad(UpperCamelCase      ,					state.sigmas[t]      ,					state.sigmas[t + 1]      ,					epsrel=1E-4  )[0]

						return integrated_coeff



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = ()  )  ->      List[str]:
						__lowerCAmelCase       				=	jnp.linspace(self.config.num_train_timesteps - 1      ,					0      ,					UpperCamelCase      ,					dtype=self.dtype  )

						__lowerCAmelCase       				=	jnp.floor(UpperCamelCase  ).astype(jnp.intaa  )
						__lowerCAmelCase       				=	jnp.ceil(UpperCamelCase  ).astype(jnp.intaa  )

						__lowerCAmelCase       				=	jnp.mod(UpperCamelCase      ,					1.0  )

						__lowerCAmelCase       				=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						__lowerCAmelCase       				=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						__lowerCAmelCase       				=	jnp.concatenate([sigmas, jnp.array([0.0]      ,					dtype=self.dtype  )]  )

						__lowerCAmelCase       				=	timesteps.astype(jnp.intaa  )

						# initial running values
						__lowerCAmelCase       				=	jnp.zeros((0,) + shape      ,					dtype=self.dtype  )

						return state.replace(
						    timesteps=UpperCamelCase      ,					sigmas=UpperCamelCase      ,					num_inference_steps=UpperCamelCase      ,					derivatives=UpperCamelCase      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = 4      ,					UpperCamelCase = True      ,					)  ->      Optional[int]:


						if state.num_inference_steps is None:
							raise ValueError(
							    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"  )

						__lowerCAmelCase       				=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
							__lowerCAmelCase       				=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							__lowerCAmelCase       				=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
							raise ValueError(
							    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

						# 2. Convert to an ODE derivative
						__lowerCAmelCase       				=	(sample - pred_original_sample) / sigma
						__lowerCAmelCase       				=	state.replace(derivatives=jnp.append(state.derivatives      ,					UpperCamelCase  )  )
						if len(state.derivatives  ) > order:
							__lowerCAmelCase       				=	state.replace(derivatives=jnp.delete(state.derivatives      ,					0  )  )

						# 3. Compute linear multistep coefficients
						__lowerCAmelCase       				=	min(timestep + 1      ,					UpperCamelCase  )
						__lowerCAmelCase       				=	[self.get_lms_coefficient(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  ) for curr_order in range(UpperCamelCase  )]

						# 4. Compute previous sample based on the derivatives path
						__lowerCAmelCase       				=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCamelCase      ,					reversed(state.derivatives  )  )  )

						if not return_dict:
							return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase      ,					state=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      Union[str, Any]:
						__lowerCAmelCase       				=	state.sigmas[timesteps].flatten()
						__lowerCAmelCase       				=	broadcast_to_shape_from_left(UpperCamelCase      ,					noise.shape  )

						__lowerCAmelCase       				=	original_samples + noise * sigma

						return noisy_samples



					def __len__(     self  )  ->      str:
						return self.config.num_train_timesteps


"""simple docstring"""



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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    None

SCREAMING_SNAKE_CASE_       :						int					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Any					=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_       :						Optional[int]					=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       VOCAB_FILES_NAMES
    UpperCAmelCase    			=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase    			=       PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase    			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase    			=       DebertaVaTokenizer

    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			List[str]="[CLS]"     ,    UpperCamelCase:			Optional[Any]="[SEP]"     ,    UpperCamelCase:			int="[UNK]"     ,    UpperCamelCase:			List[Any]="[SEP]"     ,    UpperCamelCase:			Optional[Any]="[PAD]"     ,    UpperCamelCase:			Any="[CLS]"     ,    UpperCamelCase:			List[Any]="[MASK]"     ,    **UpperCamelCase:			List[str]     ,    ):






         """simple docstring"""


         super().__init__(
             UpperCamelCase     ,    tokenizer_file=UpperCamelCase     ,    do_lower_case=UpperCamelCase     ,    bos_token=UpperCamelCase     ,    eos_token=UpperCamelCase     ,    unk_token=UpperCamelCase     ,    sep_token=UpperCamelCase     ,    pad_token=UpperCamelCase     ,    cls_token=UpperCamelCase     ,    mask_token=UpperCamelCase     ,    split_by_punct=UpperCamelCase     ,    **UpperCamelCase     ,    )

         A__				      =	do_lower_case
         A__				      =	split_by_punct
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__				      =	[self.cls_token_id]
         A__				      =	[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Tuple=False							):






         """simple docstring"""


         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase     ,    token_ids_a=UpperCamelCase     ,    already_has_special_tokens=UpperCamelCase							)

         if token_ids_a is not None:
              return [1] + ([0] * len(UpperCamelCase							)) + [1] + ([0] * len(UpperCamelCase							)) + [1]
         return [1] + ([0] * len(UpperCamelCase							)) + [1]

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Dict=None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Optional[str] = None							):






         """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(UpperCamelCase							):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
              return
         A__				      =	os.path.join(
             UpperCamelCase     ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

         return (out_vocab_file,)






