


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Union[str, Any]					       =						BlenderbotSmallTokenizer
      snake_case__    :					Union[str, Any]					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[Any]:
             super().setUp()

             a_ :		Union[str, Any]							=  ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
             a_ :		Optional[Any]							=  dict(zip(SCREAMING_SNAKE_CASE__					,							range(len(SCREAMING_SNAKE_CASE__					)					)					)					)

             a_ :		Tuple							=  ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
             a_ :		Dict							=  {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

             a_ :		List[str]							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']					)
             a_ :		Dict							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['merges_file']					)
             with open(self.vocab_file					,							'w'					,							encoding='utf-8'					) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__					) + '\n'					)
             with open(self.merges_file					,							'w'					,							encoding='utf-8'					) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							**SCREAMING_SNAKE_CASE__    :	Any					)		->      List[Any]:
             kwargs.update(self.special_tokens_map					)
             return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,							**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Tuple:
             a_ :		Tuple							=  'adapt act apte'
             a_ :		List[str]							=  'adapt act apte'
             return input_text, output_text
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             a_ :		Union[str, Any]							=  BlenderbotSmallTokenizer(self.vocab_file					,							self.merges_file					,							**self.special_tokens_map					)
             a_ :		Any							=  'adapt act apte'
             a_ :		Optional[int]							=  ['adapt', 'act', 'ap@@', 'te']
             a_ :		List[str]							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
             self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

             a_ :		str							=  [0, 1, 2, 3, 4, 5]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__					)					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      int:
             a_ :		int							=  BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)
             assert tok('sam'					).input_ids == [1_3_8_4]
             a_ :		Optional[int]							=  'I am a small frog.'
             a_ :		Tuple							=  tok([src_text]					,							padding=SCREAMING_SNAKE_CASE__					,							truncation=SCREAMING_SNAKE_CASE__					)['input_ids']
             a_ :		str							=  tok.batch_decode(SCREAMING_SNAKE_CASE__					,							skip_special_tokens=SCREAMING_SNAKE_CASE__					,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)[0]
             assert src_text != decoded  # I wish it did!
             assert decoded == "i am a small frog ."

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[str]:
             a_ :		Any							=  BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)
             a_ :		Tuple							=  'I am a small frog .'
             a_ :		Optional[Any]							=  '.'
             a_ :		Optional[int]							=  tok(SCREAMING_SNAKE_CASE__					)['input_ids']
             a_ :		Union[str, Any]							=  tok(SCREAMING_SNAKE_CASE__					)['input_ids']

             assert encoded[-1] == encoded_dot[0]






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


