





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   Tuple						       =						{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class        __magic_name__	(				_a				):





     """simple docstring"""



     __UpperCamelCase            =  """xlnet"""
     __UpperCamelCase            =  ["""mems"""]
     __UpperCamelCase            =  {
         """n_token""": """vocab_size""",  # Backward compatibility
         """hidden_size""": """d_model""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }



     def __init__(				self							:str      ,   snake_case							:Union[str, Any]=32_000      ,   snake_case							:int=1_024      ,   snake_case							:Optional[int]=24      ,   snake_case							:List[str]=16      ,   snake_case							:Optional[int]=4_096      ,   snake_case							:Optional[Any]="gelu"      ,   snake_case							:str=True      ,   snake_case							:List[str]="bi"      ,   snake_case							:Union[str, Any]=0.02      ,   snake_case							:Dict=1e-12      ,   snake_case							:str=0.1      ,   snake_case							:Union[str, Any]=512      ,   snake_case							:List[str]=None      ,   snake_case							:Optional[Any]=True      ,   snake_case							:Any=False      ,   snake_case							:Dict=False      ,   snake_case							:Dict=-1      ,   snake_case							:Tuple=False      ,   snake_case							:Tuple="last"      ,   snake_case							:List[Any]=True      ,   snake_case							:Optional[int]="tanh"      ,   snake_case							:Optional[int]=0.1      ,   snake_case							:List[Any]=5      ,   snake_case							:Union[str, Any]=5      ,   snake_case							:Dict=5      ,   snake_case							:List[str]=1      ,   snake_case							:Optional[int]=2      ,   **snake_case							:int      ,   ):





        '''simple docstring'''




        A_       :					List[Any]        =      vocab_size
        A_       :					List[Any]        =      d_model
        A_       :					List[str]        =      n_layer
        A_       :					List[str]        =      n_head
        if d_model % n_head != 0:
           raise ValueError(f"\'d_model % n_head\' ({d_model % n_head}) should be equal to 0"	)
        if "d_head" in kwargs:
           if kwargs["d_head"] != d_model // n_head:
              raise ValueError(
                  f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"	)
        A_       :					Optional[Any]        =      d_model // n_head
        A_       :					List[Any]        =      ff_activation
        A_       :					int        =      d_inner
        A_       :					Any        =      untie_r
        A_       :					Union[str, Any]        =      attn_type

        A_       :					Optional[Any]        =      initializer_range
        A_       :					List[Any]        =      layer_norm_eps

        A_       :					Dict        =      dropout
        A_       :					List[str]        =      mem_len
        A_       :					Optional[int]        =      reuse_len
        A_       :					str        =      bi_data
        A_       :					List[Any]        =      clamp_len
        A_       :					List[Any]        =      same_length

        A_       :					int        =      summary_type
        A_       :					str        =      summary_use_proj
        A_       :					Optional[int]        =      summary_activation
        A_       :					Tuple        =      summary_last_dropout
        A_       :					Dict        =      start_n_top
        A_       :					Union[str, Any]        =      end_n_top

        A_       :					int        =      bos_token_id
        A_       :					List[Any]        =      pad_token_id
        A_       :					Optional[int]        =      eos_token_id

        if "use_cache" in kwargs:
           warnings.warn(
               "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
               " instead."      ,   snake_case      ,   )
           A_       :					Dict        =      kwargs['use_cache']

        A_       :					str        =      use_mems_eval
        A_       :					Tuple        =      use_mems_train
        super().__init__(pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   **snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."	)
        return -1





     @max_position_embeddings.setter
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Any	):





        '''simple docstring'''




        raise NotImplementedError(
            f"The model {self.model_type} is one of the few models that has no sequence length limit."	)




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


