


'''simple docstring'''




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


_a   :   Optional[int]												=   "scheduler_config.json"




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  List[str]          =	1
   _SCREAMING_SNAKE_CASE  :  Any          =	2
   _SCREAMING_SNAKE_CASE  :  int          =	3
   _SCREAMING_SNAKE_CASE  :  str          =	4
   _SCREAMING_SNAKE_CASE  :  int          =	5




@dataclass
class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray




class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	SCHEDULER_CONFIG_NAME
   _SCREAMING_SNAKE_CASE  :  Any          =	["dtype"]
   _SCREAMING_SNAKE_CASE  :  Tuple          =	[]
   _SCREAMING_SNAKE_CASE  :  Any          =	True
   @classmethod
   def a  (						cls       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, Any] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=False       ,   **SCREAMING_SNAKE_CASE_       :      Dict       ,   ) ->							Dict:
        __snake_case      ,       __snake_case           =						cls.load_config(
            pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_       ,   subfolder=SCREAMING_SNAKE_CASE_       ,   return_unused_kwargs=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )
        __snake_case      ,       __snake_case           =						cls.from_config(SCREAMING_SNAKE_CASE_       ,   return_unused_kwargs=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

        if hasattr(SCREAMING_SNAKE_CASE_       ,   'create_state'      ) and getattr(SCREAMING_SNAKE_CASE_       ,   'has_state'       ,   SCREAMING_SNAKE_CASE_      ):
             __snake_case           =						scheduler.create_state()

        if return_unused_kwargs:
             return scheduler, state, unused_kwargs

        return scheduler, state
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Union[str, os.PathLike]       ,   SCREAMING_SNAKE_CASE_       :      bool = False       ,   **SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							Union[str, Any]:
        self.save_config(save_directory=SCREAMING_SNAKE_CASE_       ,   push_to_hub=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   @property
   def a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        return self._get_compatibles()





   @classmethod
   def a  (						cls       :      Any      ) ->							int:
        __snake_case           =						list(set([cls.__name__] + cls._compatibles      )      )
        __snake_case           =						importlib.import_module(__name__.split('.'      )[0]      )
        __snake_case           =						[
            getattr(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        ]
        return compatible_classes





def   _a		(lowercase__			:   jnp.ndarray							,   lowercase__			:   Tuple[int]						)			->			jnp.ndarray:






     """simple docstring"""
     assert len(lowercase__						) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase__						) - x.ndim)						)							,   lowercase__						)





def   _a		(lowercase__			:   int							,   lowercase__			:   Optional[Any]=0.9_99							,   lowercase__			:   Dict=jnp.floataa						)			->			jnp.ndarray:






     """simple docstring"""

     def alpha_bar(lowercase__			:   Union[str, Any]						):
          return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

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




@flax.struct.dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   @classmethod
   def a  (						cls       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							Dict:
        __snake_case           =						scheduler.config

        if config.trained_betas is not None:
             __snake_case           =						jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype      )
        elif config.beta_schedule == "linear":
             __snake_case           =						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.
             __snake_case           =						(
                 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
             __snake_case           =						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__}'      )

        __snake_case           =						1.0 - betas

        __snake_case           =						jnp.cumprod(SCREAMING_SNAKE_CASE_       ,   axis=0      )

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





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						state.alphas_cumprod

     __snake_case           =						alphas_cumprod[timesteps] ** 0.5
     __snake_case           =						sqrt_alpha_prod.flatten()
     __snake_case           =						broadcast_to_shape_from_left(lowercase__							,   original_samples.shape						)

     __snake_case           =						(1 - alphas_cumprod[timesteps]) ** 0.5
     __snake_case           =						sqrt_one_minus_alpha_prod.flatten()
     __snake_case           =						broadcast_to_shape_from_left(lowercase__							,   original_samples.shape						)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			int:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						get_sqrt_alpha_prod(lowercase__							,   lowercase__							,   lowercase__							,   lowercase__						)
     __snake_case           =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			List[Any]:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						get_sqrt_alpha_prod(lowercase__							,   lowercase__							,   lowercase__							,   lowercase__						)
     __snake_case           =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity









"""simple docstring"""






import warnings

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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]=3_2000			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						Dict=24			,     _SCREAMING_SNAKE_CASE:						Optional[int]=16			,     _SCREAMING_SNAKE_CASE:						List[str]=4096			,     _SCREAMING_SNAKE_CASE:						Tuple="gelu"			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						List[Any]="bi"			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						List[str]=1e-12			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Any=512			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						List[Any]=-1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[int]="last"			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Dict="tanh"			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						Tuple=5			,     _SCREAMING_SNAKE_CASE:						List[str]=5			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=5			,     _SCREAMING_SNAKE_CASE:						Optional[int]=1			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			vocab_size
				__lowerCAmelCase :    Optional[int]				=			d_model
				__lowerCAmelCase :    Union[str, Any]				=			n_layer
				__lowerCAmelCase :    List[Any]				=			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})""")
				__lowerCAmelCase :    Optional[Any]				=			d_model // n_head
				__lowerCAmelCase :    Tuple				=			ff_activation
				__lowerCAmelCase :    Union[str, Any]				=			d_inner
				__lowerCAmelCase :    Any				=			untie_r
				__lowerCAmelCase :    int				=			attn_type

				__lowerCAmelCase :    Any				=			initializer_range
				__lowerCAmelCase :    List[Any]				=			layer_norm_eps

				__lowerCAmelCase :    int				=			dropout
				__lowerCAmelCase :    List[Any]				=			mem_len
				__lowerCAmelCase :    str				=			reuse_len
				__lowerCAmelCase :    Any				=			bi_data
				__lowerCAmelCase :    Optional[int]				=			clamp_len
				__lowerCAmelCase :    Union[str, Any]				=			same_length

				__lowerCAmelCase :    List[str]				=			summary_type
				__lowerCAmelCase :    Optional[int]				=			summary_use_proj
				__lowerCAmelCase :    Tuple				=			summary_activation
				__lowerCAmelCase :    Dict				=			summary_last_dropout
				__lowerCAmelCase :    str				=			start_n_top
				__lowerCAmelCase :    List[str]				=			end_n_top

				__lowerCAmelCase :    str				=			bos_token_id
				__lowerCAmelCase :    Optional[Any]				=			pad_token_id
				__lowerCAmelCase :    Optional[Any]				=			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."			,     _SCREAMING_SNAKE_CASE			,     )
							__lowerCAmelCase :    Optional[int]				=			kwargs["use_cache"]

				__lowerCAmelCase :    Optional[int]				=			use_mems_eval
				__lowerCAmelCase :    Any				=			use_mems_train
				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			int:

				"""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:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

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