


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_							=      (ScoreSdeVeScheduler,)
 UpperCAmelCase_							=      ()




 @property
 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      4
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      torch.rand((batch_size, num_channels, height, width))

        return sample




 @property
 def 	__snake_case		(    self      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      4
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      batch_size * num_channels * height * width
        lowerCAmelCase__  			=      torch.arange(lowercase__)
        lowerCAmelCase__  			=      sample.reshape(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__)
        lowerCAmelCase__  			=      sample / num_elems
        lowerCAmelCase__  			=      sample.permute(3    ,					0    ,					1    ,					2)

        return sample




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        def model(lowercase__      :						str    ,					lowercase__      :						str    ,					*lowercase__      :						str):
               return sample * t / (t + 1)

        return model




 def 	__snake_case		(    self      :						Optional[int]    ,					**lowercase__      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      {
            "num_train_timesteps": 2_000,
            "snr": 0.15,
            "sigma_min": 0.01,
            "sigma_max": 1_348,
            "sampling_eps": 1e-5,
        }

        config.update(**lowercase__)
        return config




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						Union[str, Any]=0    ,					**lowercase__      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      dict(self.forward_default_kwargs)

        for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__  			=      self.dummy_sample
               lowerCAmelCase__  			=      0.1 * sample

               lowerCAmelCase__  			=      self.get_scheduler_config(**lowercase__)
               lowerCAmelCase__  			=      scheduler_class(**lowercase__)

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowercase__)
                      lowerCAmelCase__  			=      scheduler_class.from_pretrained(lowercase__)

               lowerCAmelCase__  			=      scheduler.step_pred(
                   lowercase__    ,					lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample
               lowerCAmelCase__  			=      new_scheduler.step_pred(
                   lowercase__    ,					lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

               lowerCAmelCase__  			=      scheduler.step_correct(lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample
               lowerCAmelCase__  			=      new_scheduler.step_correct(
                   lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						Dict=0    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      dict(self.forward_default_kwargs)
        kwargs.update(lowercase__)

        for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__  			=      self.dummy_sample
               lowerCAmelCase__  			=      0.1 * sample

               lowerCAmelCase__  			=      self.get_scheduler_config()
               lowerCAmelCase__  			=      scheduler_class(**lowercase__)

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowercase__)
                      lowerCAmelCase__  			=      scheduler_class.from_pretrained(lowercase__)

               lowerCAmelCase__  			=      scheduler.step_pred(
                   lowercase__    ,					lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample
               lowerCAmelCase__  			=      new_scheduler.step_pred(
                   lowercase__    ,					lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

               lowerCAmelCase__  			=      scheduler.step_correct(lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample
               lowerCAmelCase__  			=      new_scheduler.step_correct(
                   lowercase__    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"




 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



        for timesteps in [10, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase__)




 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]    ,					[1, 100, 1_000]):
               self.check_over_configs(sigma_min=lowercase__    ,					sigma_max=lowercase__)




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=lowercase__)




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      dict(self.forward_default_kwargs)

        lowerCAmelCase__  			=      self.scheduler_classes[0]
        lowerCAmelCase__  			=      self.get_scheduler_config()
        lowerCAmelCase__  			=      scheduler_class(**lowercase__)

        lowerCAmelCase__  			=      3

        lowerCAmelCase__  			=      self.dummy_model()
        lowerCAmelCase__  			=      self.dummy_sample_deter

        scheduler.set_sigmas(lowercase__)
        scheduler.set_timesteps(lowercase__)
        lowerCAmelCase__  			=      torch.manual_seed(0)

        for i, t in enumerate(scheduler.timesteps):
               lowerCAmelCase__  			=      scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps):
                      with torch.no_grad():
                             lowerCAmelCase__  			=      model(lowercase__    ,					lowercase__)
                      lowerCAmelCase__  			=      scheduler.step_correct(lowercase__    ,					lowercase__    ,					generator=lowercase__    ,					**lowercase__).prev_sample

               with torch.no_grad():
                      lowerCAmelCase__  			=      model(lowercase__    ,					lowercase__)

               lowerCAmelCase__  			=      scheduler.step_pred(lowercase__    ,					lowercase__    ,					lowercase__    ,					generator=lowercase__    ,					**lowercase__)
               lowerCAmelCase__  			=      output.prev_sample, output.prev_sample_mean

        lowerCAmelCase__  			=      torch.sum(torch.abs(lowercase__))
        lowerCAmelCase__  			=      torch.mean(torch.abs(lowercase__))

        assert np.isclose(result_sum.item()    ,					14_372_758_528.0)
        assert np.isclose(result_mean.item()    ,					18_714_530.0)




 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        lowerCAmelCase__  			=      dict(self.forward_default_kwargs)

        lowerCAmelCase__  			=      kwargs.pop('num_inference_steps'    ,					lowercase__)

        for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__  			=      self.get_scheduler_config()
               lowerCAmelCase__  			=      scheduler_class(**lowercase__)

               lowerCAmelCase__  			=      self.dummy_sample
               lowerCAmelCase__  			=      0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase__    ,					'set_timesteps'):
                      scheduler.set_timesteps(lowercase__)
               elif num_inference_steps is not None and not hasattr(lowercase__    ,					'set_timesteps'):
                      lowerCAmelCase__  			=      num_inference_steps

               lowerCAmelCase__  			=      scheduler.step_pred(lowercase__    ,					0    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample
               lowerCAmelCase__  			=      scheduler.step_pred(lowercase__    ,					1    ,					lowercase__    ,					generator=torch.manual_seed(0)    ,					**lowercase__).prev_sample

               self.assertEqual(output_a.shape    ,					sample.shape)
               self.assertEqual(output_a.shape    ,					output_a.shape)

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__					=				logging.get_logger(__name__)

lowerCAmelCase__					=				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__					=				{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

lowerCAmelCase__					=				{
    'microsoft/biogpt': 1024,
}






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      set()
       lowerCAmelCase__  			=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)			)
              lowerCAmelCase__  			=      char
       return pairs



class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      VOCAB_FILES_NAMES
 UpperCAmelCase_							=      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_							=      ['input_ids', 'attention_mask']




 def __init__(    self      :						List[str]    ,					lowercase__      :						Any    ,					lowercase__      :						int    ,					lowercase__      :						Union[str, Any]="<unk>"    ,					lowercase__      :						Optional[Any]="<s>"    ,					lowercase__      :						Dict="</s>"    ,					lowercase__      :						Tuple="</s>"    ,					lowercase__      :						Optional[Any]="<pad>"    ,					**lowercase__      :						Optional[int]    ,					):







        '''simple docstring'''



        super().__init__(
            bos_token=lowercase__    ,					eos_token=lowercase__    ,					sep_token=lowercase__    ,					unk_token=lowercase__    ,					pad_token=lowercase__    ,					**lowercase__    ,					)

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use BioGptTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      'en'
        lowerCAmelCase__  			=      sacremoses
        # cache of sm.MosesTokenizer instance
        lowerCAmelCase__  			=      {}
        lowerCAmelCase__  			=      {}
        with open(lowercase__    ,					encoding='utf-8') as vocab_handle:
               lowerCAmelCase__  			=      json.load(lowercase__)
        lowerCAmelCase__  			=      {v: k for k, v in self.encoder.items()}
        with open(lowercase__    ,					encoding='utf-8') as merges_handle:
               lowerCAmelCase__  			=      merges_handle.read().split('\n')[:-1]
        lowerCAmelCase__  			=      [tuple(merge.split()[:2]) for merge in merges]
        lowerCAmelCase__  			=      dict(zip(lowercase__    ,					range(len(lowercase__))))
        lowerCAmelCase__  			=      {}




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return len(self.encoder)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        return dict(self.encoder    ,					**self.added_tokens_encoder)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						int):







        '''simple docstring'''



        if lang not in self.cache_moses_tokenizer:
               lowerCAmelCase__  			=      self.sm.MosesTokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            lowercase__    ,					aggressive_dash_splits=lowercase__    ,					return_str=lowercase__    ,					escape=lowercase__)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Tuple    ,					lowercase__      :						Any):







        '''simple docstring'''



        if lang not in self.cache_moses_detokenizer:
               lowerCAmelCase__  			=      self.sm.MosesDetokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(lowercase__)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      tuple(token[:-1]) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        lowerCAmelCase__  			=      get_pairs(lowercase__)

        if not pairs:
               return token + "</w>"

        while True:
               lowerCAmelCase__  			=      min(lowercase__    ,					key=lambda lowercase__: self.bpe_ranks.get(lowercase__    ,					float('inf')))
               if bigram not in self.bpe_ranks:
                      break
               lowerCAmelCase__			,      lowerCAmelCase__  			=      bigram
               lowerCAmelCase__  			=      []
               lowerCAmelCase__  			=      0
               while i < len(lowercase__):
                      try:
                             lowerCAmelCase__  			=      word.index(lowercase__    ,					lowercase__)
                      except ValueError:
                             new_word.extend(word[i:])
                             break
                      else:
                             new_word.extend(word[i:j])
                             lowerCAmelCase__  			=      j

                      if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
                             new_word.append(first + second)
                             i += 2
                      else:
                             new_word.append(word[i])
                             i += 1
               lowerCAmelCase__  			=      tuple(lowercase__)
               lowerCAmelCase__  			=      new_word
               if len(lowercase__) == 1:
                      break
               else:
                      lowerCAmelCase__  			=      get_pairs(lowercase__)
        lowerCAmelCase__  			=      ' '.join(lowercase__)
        if word == "\n  </w>":
               lowerCAmelCase__  			=      '\n</w>'
        lowerCAmelCase__  			=      word
        return word




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Dict=False):







        '''simple docstring'''



        if bypass_tokenizer:
               lowerCAmelCase__  			=      text.split()
        else:
               lowerCAmelCase__  			=      self.moses_tokenize(lowercase__    ,					self.lang)

        lowerCAmelCase__  			=      []
        for token in text:
               if token:
                      split_tokens.extend(list(self.bpe(lowercase__).split(' ')))

        return split_tokens




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.encoder.get(lowercase__    ,					self.encoder.get(self.unk_token))




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        return self.decoder.get(lowercase__    ,					self.unk_token)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      [t.replace(' '    ,					'').replace('</w>'    ,					' ') for t in tokens]
        lowerCAmelCase__  			=      ''.join(lowercase__).split()
        # detokenize
        lowerCAmelCase__  			=      self.moses_detokenize(lowercase__    ,					self.lang)
        return text




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
        lowerCAmelCase__  			=      [self.sep_token_id]
        return sep + token_ids_a + sep + token_ids_a




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None    ,					lowercase__      :						bool = False):







        '''simple docstring'''



        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowercase__    ,					token_ids_a=lowercase__    ,					already_has_special_tokens=lowercase__)
        # no bos used in fairseq
        if token_ids_a is not None:
               return [1] + ([0] * len(lowercase__)) + [1] + ([0] * len(lowercase__))
        return [1] + ([0] * len(lowercase__))




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      [self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
               return len(token_ids_a + sep) * [0]
        return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




 def 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						str    ,					lowercase__      :						Optional[str] = None):







        '''simple docstring'''



        if not os.path.isdir(lowercase__):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
               return
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as f:
               f.write(json.dumps(self.encoder    ,					indent=2    ,					sort_keys=lowercase__    ,					ensure_ascii=lowercase__) + '\n')

        lowerCAmelCase__  			=      0
        with open(lowercase__    ,					'w'    ,					encoding='utf-8') as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,					key=lambda lowercase__: kv[1]):
                      if index != token_index:
                             logger.warning(
                                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                 ' Please check that the tokenizer is not corrupted!')
                             lowerCAmelCase__  			=      token_index
                      writer.write(' '.join(lowercase__) + '\n')
                      index += 1

        return vocab_file, merge_file




 def __getstate__(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.__dict__.copy()
        lowerCAmelCase__  			=      None
        return state




 def __setstate__(    self      :						List[str]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      d

        try:
               import sacremoses
        except ImportError:
               raise ImportError(
                   'You need to install sacremoses to use XLMTokenizer. '
                   'See https://pypi.org/project/sacremoses/ for installation.')

        lowerCAmelCase__  			=      sacremoses
