

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin

@flax.struct.dataclass
class 				__UpperCamelCase				:
	# setable values
	_lowercase  :   Optional[int]       				=  None
	_lowercase  :   Optional[jnp.ndarray]       				=  None
	_lowercase  :   Optional[jnp.ndarray]       				=  None  # sigma(t_i)







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: str      ):

						'''simple docstring'''





						return cls()




@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   jnp.ndarray
	_lowercase  :   jnp.ndarray
	_lowercase  :   KarrasVeSchedulerState




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return True







	@register_to_config
	def __init__(   self: Dict  , __UpperCamelCase: float = 0.02  , __UpperCamelCase: float = 1_00  , __UpperCamelCase: float = 1.007  , __UpperCamelCase: float = 80  , __UpperCamelCase: float = 0.05  , __UpperCamelCase: float = 50  , ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return KarrasVeSchedulerState.create()







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: KarrasVeSchedulerState  , __UpperCamelCase: int  , __UpperCamelCase: Tuple = ()      ):

						'''simple docstring'''





						__magic_name__							=    jnp.arange(0  , __UpperCamelCase      )[::-1].copy()
						__magic_name__							=    [
						    (
						        self.config.sigma_max**2
						        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
						    )
						    for i in timesteps
						]

						return state.replace(
						    num_inference_steps=__UpperCamelCase  , schedule=jnp.array(__UpperCamelCase  , dtype=jnp.floataa      )  , timesteps=__UpperCamelCase  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: KarrasVeSchedulerState  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: float  , __UpperCamelCase: random.KeyArray  , ):

						'''simple docstring'''





						if self.config.s_min <= sigma <= self.config.s_max:
											__magic_name__							=    min(self.config.s_churn / state.num_inference_steps  , 2**0.5 - 1      )
						else:
											__magic_name__							=    0

						# sample eps ~ N(0, S_noise^2 * I)
						__magic_name__							=    random.split(__UpperCamelCase  , num=1      )
						__magic_name__							=    self.config.s_noise * random.normal(key=__UpperCamelCase  , shape=sample.shape      )
						__magic_name__							=    sigma + gamma * sigma
						__magic_name__							=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: KarrasVeSchedulerState  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: float  , __UpperCamelCase: float  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						__magic_name__							=    sample_hat + sigma_hat * model_output
						__magic_name__							=    (sample_hat - pred_original_sample) / sigma_hat
						__magic_name__							=    sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
											return (sample_prev, derivative, state)

						return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase  , derivative=__UpperCamelCase  , state=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: KarrasVeSchedulerState  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: float  , __UpperCamelCase: float  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: jnp.ndarray  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						__magic_name__							=    sample_prev + sigma_prev * model_output
						__magic_name__							=    (sample_prev - pred_original_sample) / sigma_prev
						__magic_name__							=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

						if not return_dict:
											return (sample_prev, derivative, state)

						return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase  , derivative=__UpperCamelCase  , state=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: KarrasVeSchedulerState  , __UpperCamelCase: str  , __UpperCamelCase: str  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						raise NotImplementedError()







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						with open(self.vocab_file  , 'w'  , encoding='utf-8'      ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




