'''simple docstring'''




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class     a__      (   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Optional[int]							=XLMTokenizer
     lowerCamelCase  :     List[Any]							=False






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __lowerCamelCase		=  [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __lowerCamelCase		=  dict(zip(a  ,				range(len(a    )    )    )    )
         __lowerCamelCase		=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __lowerCamelCase		=  os.path.join(self.tmpdirname  ,				VOCAB_FILES_NAMES['''vocab_file''']    )
         __lowerCamelCase		=  os.path.join(self.tmpdirname  ,				VOCAB_FILES_NAMES['''merges_file''']    )
         with open(self.vocab_file  ,				'''w'''    ) as fp:
             fp.write(json.dumps(a    )    )
         with open(self.merges_file  ,				'''w'''    ) as fp:
             fp.write('''\n'''.join(a    )    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				a		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  '''lower newer'''
         __lowerCamelCase		=  '''lower newer'''
         return input_text, output_text






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  XLMTokenizer(self.vocab_file  ,				self.merges_file    )

         __lowerCamelCase		=  '''lower'''
         __lowerCamelCase		=  ['''low''', '''er</w>''']
         __lowerCamelCase		=  tokenizer.tokenize(a    )
         self.assertListEqual(a  ,				a    )

         __lowerCamelCase		=  tokens + ['''<unk>''']
         __lowerCamelCase		=  [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a    )  ,				a    )






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''    )

         __lowerCamelCase		=  tokenizer.encode('''sequence builders'''  ,				add_special_tokens=a    )
         __lowerCamelCase		=  tokenizer.encode('''multi-sequence build'''  ,				add_special_tokens=a    )

         __lowerCamelCase		=  tokenizer.build_inputs_with_special_tokens(a    )
         __lowerCamelCase		=  tokenizer.build_inputs_with_special_tokens(a  ,				a    )

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]








"""simple docstring"""
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
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





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





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        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=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

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

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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					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=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



