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


lowercase_     		= '''scheduler_config.json'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							1
		lowercase   :      Any       =							2
		lowercase   :      List[str]       =							3
		lowercase   :      Dict       =							4
		lowercase   :      str       =							5

@dataclass
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      jnp.ndarray

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      Tuple       =							SCHEDULER_CONFIG_NAME
		lowercase   :      List[Any]       =							['dtype']
		lowercase   :      str       =							[]
		lowercase   :      Union[str, Any]       =							True






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A = None		,						__A = None		,						__A=False		,						**__A		,						) ->     Optional[int]:
								_lowerCAmelCase						,				_lowerCAmelCase										=cls.load_config(
								    pretrained_model_name_or_path=__A		,						subfolder=__A		,						return_unused_kwargs=__A		,						**__A		,						)
								_lowerCAmelCase						,				_lowerCAmelCase										=cls.from_config(__A		,						return_unused_kwargs=__A		,						**__A				)

								if hasattr(__A		,						'create_state'				) and getattr(__A		,						'has_state'		,						__A				):
														_lowerCAmelCase										=scheduler.create_state()

								if return_unused_kwargs:
														return scheduler, state, unused_kwargs

								return scheduler, state






		def 		UpperCamelCase__       (   self		,						__A		,						__A = False		,						**__A				) ->     Union[str, Any]:
								self.save_config(save_directory=__A		,						push_to_hub=__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Any:
								return self._get_compatibles()






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     Tuple:
								_lowerCAmelCase										=list(set([cls.__name__] + cls._compatibles				)				)
								_lowerCAmelCase										=importlib.import_module(__name__.split('.'				)[0]				)
								_lowerCAmelCase										=[
								    getattr(__A		,						__A				) for c in compatible_classes_str if hasattr(__A		,						__A				)
								]
								return compatible_classes



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						assert len(a__      ) >= x.ndim
						return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a__      ) - x.ndim)      )			,      a__      )



def 							UpperCamelCase__			(			a__			,      a__=0.999			,      a__=jnp.floataa      ):


						'''simple docstring'''






						def alpha_bar(a__      ):
												return math.cos((time_step + 0.008) / 1.008 * math.pi / 2      ) ** 2

						_lowerCAmelCase										=[]
						for i in range(a__      ):
												_lowerCAmelCase										=i / num_diffusion_timesteps
												_lowerCAmelCase										=(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar(a__      ) / alpha_bar(a__      )			,      a__      )      )
						return jnp.array(a__			,      dtype=a__      )
@flax.struct.dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      jnp.ndarray
		lowercase   :      jnp.ndarray
		lowercase   :      jnp.ndarray






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A				) ->     Dict:
								_lowerCAmelCase										=scheduler.config

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

								_lowerCAmelCase										=1.0 - betas

								_lowerCAmelCase										=jnp.cumprod(__A		,						axis=0				)

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=state.alphas_cumprod

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

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

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase						,				_lowerCAmelCase										=get_sqrt_alpha_prod(a__			,      a__			,      a__			,      a__      )
						_lowerCAmelCase										=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase						,				_lowerCAmelCase										=get_sqrt_alpha_prod(a__			,      a__			,      a__			,      a__      )
						_lowerCAmelCase										=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity






'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {'''vocab_file''': '''vocab.txt'''}

lowercase_     		= {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowercase_     		= {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


lowercase_     		= {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      Tuple       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
		lowercase   :      int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      List[str]       =							ConvBertTokenizer






		def __init__(   self		,						__A=None		,						__A=None		,						__A=True		,						__A="[UNK]"		,						__A="[SEP]"		,						__A="[PAD]"		,						__A="[CLS]"		,						__A="[MASK]"		,						__A=True		,						__A=None		,						**__A		,						) ->     Union[str, Any]:
								super().__init__(
								    __A		,						tokenizer_file=__A		,						do_lower_case=__A		,						unk_token=__A		,						sep_token=__A		,						pad_token=__A		,						cls_token=__A		,						mask_token=__A		,						tokenize_chinese_chars=__A		,						strip_accents=__A		,						**__A		,						)

								_lowerCAmelCase										=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'		,						__A				) != do_lower_case
								    or normalizer_state.get('strip_accents'		,						__A				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'		,						__A				) != tokenize_chinese_chars
								):
														_lowerCAmelCase										=getattr(__A		,						normalizer_state.pop('type'				)				)
														_lowerCAmelCase										=do_lower_case
														_lowerCAmelCase										=strip_accents
														_lowerCAmelCase										=tokenize_chinese_chars
														_lowerCAmelCase										=normalizer_class(**__A				)

								_lowerCAmelCase										=do_lower_case






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     int:
								_lowerCAmelCase										=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     List[int]:
								_lowerCAmelCase										=[self.sep_token_id]
								_lowerCAmelCase										=[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		,						__A		,						__A = None				) ->     Tuple[str]:
								_lowerCAmelCase										=self._tokenizer.model.save(__A		,						name=__A				)
								return tuple(__A				)





