


"""simple docstring"""





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__		:     List[Any]    				=				get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class 		snake_case_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					GPTSwaTokenizer
     __UpperCamelCase  =					False
     __UpperCamelCase  =					True
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					List[str]				):
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCAmelCase					:	Any       =   GPTSwaTokenizer(UpperCamelCase_					,				eos_token='''<unk>'''					,				bos_token='''<unk>'''					,				pad_token='''<unk>'''				)

           tokenizer.save_pretrained(self.tmpdirname				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Optional[Any]       =   '''This is a test'''
           lowerCAmelCase					:	int       =   '''This is a test'''
           return input_text, output_text






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	int       =   '''<s>'''
           lowerCAmelCase					:	str       =   1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_				)					,				UpperCamelCase_				)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_				)					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	List[str]       =   list(self.get_tokenizer().get_vocab().keys()				)

           self.assertEqual(vocab_keys[0]					,				'''<unk>'''				)
           self.assertEqual(vocab_keys[1]					,				'''<s>'''				)
           self.assertEqual(vocab_keys[-1]					,				'''j'''				)
           self.assertEqual(len(UpperCamelCase_				)					,				2_0_0_0				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           self.assertEqual(self.get_tokenizer().vocab_size					,				2_0_0_0				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   GPTSwaTokenizer(UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   tokenizer.tokenize('''This is a test'''				)
           self.assertListEqual(UpperCamelCase_					,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)					,				[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]				)

           lowerCAmelCase					:	Tuple       =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
           # fmt: off
           self.assertListEqual(
               UpperCamelCase_					,				['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']					,				)
           # fmt: on

           lowerCAmelCase					:	Dict       =   tokenizer.convert_tokens_to_ids(UpperCamelCase_				)
           self.assertListEqual(
               UpperCamelCase_					,				[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]					,				)

           lowerCAmelCase					:	Dict       =   tokenizer.convert_ids_to_tokens(UpperCamelCase_				)
           # fmt: off
           self.assertListEqual(
               UpperCamelCase_					,				['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
           # fmt: on






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   GPTSwaTokenizer(UpperCamelCase_				)
           lowerCAmelCase					:	int       =   ['''This is a test''', '''I was born in 92000, and this is falsé.''']
           lowerCAmelCase					:	str       =   [
               [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
               [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
           ]

           # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
           for text, expected_ids in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertListEqual(tokenizer.encode_fast(UpperCamelCase_				)					,				UpperCamelCase_				)

           # Test that decode_fast returns the input text
           for text, token_ids in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertEqual(tokenizer.decode_fast(UpperCamelCase_				)					,				UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Optional[int]       =   [
               '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
               '''Hey there, how are you doing this fine day?''',
               '''This is a text with a trailing spaces followed by a dot     .''',
               '''Häj sväjs lillebrör! =)''',
               '''Det är inget fel på Mr. Cool''',
           ]

           # fmt: off
           lowerCAmelCase					:	List[Any]       =   {'''input_ids''': [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
           # fmt: on
           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase_					,				model_name='''AI-Sweden/gpt-sw3-126m'''					,				sequences=UpperCamelCase_					,				)






"""simple docstring"""





import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					None



def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			List[str]=0.999 ,   _snake_case				:			Dict="cosine" ,   ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case				:			List[Any]			):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case				:			Optional[int]			):
                  return math.exp(t * -12.0			)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

      lowerCAmelCase					:	List[Any]       =   []
      for i in range(_snake_case			):
            lowerCAmelCase					:	int       =   i / num_diffusion_timesteps
            lowerCAmelCase					:	Tuple       =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,   _snake_case			)			)
      return torch.tensor(_snake_case ,   dtype=torch.floataa			)






class 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					Any					,				UpperCamelCase_  :					int = 1_0_0_0					,				UpperCamelCase_  :					str = "fixed_small_log"					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[float] = 1.0					,				UpperCamelCase_  :					str = "epsilon"					,				UpperCamelCase_  :					str = "squaredcos_cap_v2"					,				):
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

           lowerCAmelCase					:	Any       =   betas_for_alpha_bar(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   1.0 - self.betas
           lowerCAmelCase					:	Union[str, Any]       =   torch.cumprod(self.alphas					,				dim=0				)
           lowerCAmelCase					:	Tuple       =   torch.tensor(1.0				)

           # standard deviation of the initial noise distribution
           lowerCAmelCase					:	Any       =   1.0

           # setable values
           lowerCAmelCase					:	Any       =   None
           lowerCAmelCase					:	Any       =   torch.from_numpy(np.arange(0					,				UpperCamelCase_				)[::-1].copy()				)

           lowerCAmelCase					:	List[str]       =   variance_type






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None				):
           return sample






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, torch.device] = None				):
           lowerCAmelCase					:	Any       =   num_inference_steps
           lowerCAmelCase					:	str       =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCAmelCase					:	Tuple       =   (np.arange(0					,				UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
           lowerCAmelCase					:	Any       =   torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Any=None				):
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	int       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	Dict       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	Tuple       =   self.betas[t]
           else:
                 lowerCAmelCase					:	Union[str, Any]       =   1 - alpha_prod_t / alpha_prod_t_prev

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
           # and sample from it to get previous sample
           # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
           lowerCAmelCase					:	Optional[Any]       =   beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 lowerCAmelCase					:	List[str]       =   self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 lowerCAmelCase					:	Any       =   torch.log(torch.clamp(UpperCamelCase_					,				min=1E-20				)				)
                 lowerCAmelCase					:	Union[str, Any]       =   torch.exp(0.5 * variance				)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 lowerCAmelCase					:	Optional[Any]       =   variance.log()
                 lowerCAmelCase					:	Union[str, Any]       =   beta.log()

                 lowerCAmelCase					:	Dict       =   (predicted_variance + 1) / 2
                 lowerCAmelCase					:	Union[str, Any]       =   frac * max_log + (1 - frac) * min_log

           return variance






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					bool = True					,				):
           lowerCAmelCase					:	Optional[Any]       =   timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   torch.split(UpperCamelCase_					,				sample.shape[1]					,				dim=1				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   None

           # 1. compute alphas, betas
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Optional[int]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	int       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	List[Any]       =   self.betas[t]
                 lowerCAmelCase					:	Optional[int]       =   self.alphas[t]
           else:
                 lowerCAmelCase					:	List[Any]       =   1 - alpha_prod_t / alpha_prod_t_prev
                 lowerCAmelCase					:	Dict       =   1 - beta

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCAmelCase					:	List[Any]       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                 lowerCAmelCase					:	Tuple       =   model_output
           else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     ''' for the UnCLIPScheduler.'''				)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 lowerCAmelCase					:	Dict       =   torch.clamp(
                     UpperCamelCase_					,				-self.config.clip_sample_range					,				self.config.clip_sample_range				)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           lowerCAmelCase					:	int       =   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           lowerCAmelCase					:	List[Any]       =   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           lowerCAmelCase					:	str       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           lowerCAmelCase					:	int       =   0
           if t > 0:
                 lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
                     model_output.shape					,				dtype=model_output.dtype					,				generator=UpperCamelCase_					,				device=model_output.device				)

                 lowerCAmelCase					:	Any       =   self._get_variance(
                     UpperCamelCase_					,				predicted_variance=UpperCamelCase_					,				prev_timestep=UpperCamelCase_					,				)

                 if self.variance_type == "fixed_small_log":
                       lowerCAmelCase					:	str       =   variance
                 elif self.variance_type == "learned_range":
                       lowerCAmelCase					:	Optional[Any]       =   (0.5 * variance).exp()
                 else:
                       raise ValueError(
                           F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           ''' for the UnCLIPScheduler.'''				)

                 lowerCAmelCase					:	List[Any]       =   variance * variance_noise

           lowerCAmelCase					:	int       =   pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_					,				pred_original_sample=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.IntTensor					,				):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCAmelCase					:	Tuple       =   self.alphas_cumprod.to(device=original_samples.device					,				dtype=original_samples.dtype				)
           lowerCAmelCase					:	int       =   timesteps.to(original_samples.device				)

           lowerCAmelCase					:	Dict       =   alphas_cumprod[timesteps] ** 0.5
           lowerCAmelCase					:	str       =   sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	Any       =   sqrt_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	List[str]       =   (1 - alphas_cumprod[timesteps]) ** 0.5
           lowerCAmelCase					:	Tuple       =   sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	int       =   sqrt_one_minus_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	Dict       =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples


