





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      lowercase__    (							__A     ,					unittest.TestCase  ):
  __UpperCamelCase	     =  XLMTokenizer
  __UpperCamelCase	     =  False







  def 	UpperCAmelCase__		(	self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       lowerCAmelCase_					:       List[Any]    =		[
           """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_					:       Any    =		dict(zip(_lowercase    ,						range(len(_lowercase						)						)						)						)
       lowerCAmelCase_					:       List[Any]    =		["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

       lowerCAmelCase_					:       Optional[int]    =		os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]						)
       lowerCAmelCase_					:       List[str]    =		os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file    ,						"""w"""						) as fp:
            fp.write(json.dumps(_lowercase						)						)
       with open(self.merges_file    ,						"""w"""						) as fp:
            fp.write("""\n""".join(_lowercase						)						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       List[str]    =		"""lower newer"""
       lowerCAmelCase_					:       Union[str, Any]    =		"""lower newer"""
       return input_text, output_text







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		XLMTokenizer(self.vocab_file    ,						self.merges_file						)

       lowerCAmelCase_					:       int    =		"""lower"""
       lowerCAmelCase_					:       Union[str, Any]    =		["""low""", """er</w>"""]
       lowerCAmelCase_					:       List[str]    =		tokenizer.tokenize(_lowercase						)
       self.assertListEqual(_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Dict    =		tokens + ["""<unk>"""]
       lowerCAmelCase_					:       Union[str, Any]    =		[14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase						)    ,						_lowercase						)







  @slow
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

       lowerCAmelCase_					:       Dict    =		tokenizer.encode("""sequence builders"""    ,						add_special_tokens=_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=_lowercase						)

       lowerCAmelCase_					:       Union[str, Any]    =		tokenizer.build_inputs_with_special_tokens(_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.build_inputs_with_special_tokens(_lowercase    ,						_lowercase						)

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







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
