



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

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

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         for timesteps in [1_00, 5_00, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,				[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=lowerCAmelCase			,				beta_end=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

         for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

         for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

         self.check_over_configs(thresholding=lowerCAmelCase)
         for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=lowerCAmelCase			,				prediction_type=lowerCAmelCase			,				sample_max_value=lowerCAmelCase			,				)


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				int:




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

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

         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20			,				4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87			,				4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

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

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  self.dummy_sample_deter + 0.1
         lowercase__     =  self.dummy_sample_deter - 0.1

         lowercase__     =  samplea.shape[0]
         lowercase__     =  torch.stack([samplea, samplea, samplea]			,				dim=0)
         lowercase__     =  torch.arange(lowerCAmelCase)[0:3, None].repeat(1			,				lowerCAmelCase)

         lowercase__     =  model(samples.flatten(0			,				1)			,				timesteps.flatten(0			,				1))
         lowercase__     =  scheduler.batch_step_no_noise(lowerCAmelCase			,				timesteps.flatten(0			,				1)			,				samples.flatten(0			,				1)			,				lowerCAmelCase)

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

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

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

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

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

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3





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

import regex as re

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


a__      :				Optional[int]   	=			logging.get_logger(__name__)


a__      :				Dict   	=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a__      :				str   	=			{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

a__      :				Any   	=			{"facebook/blenderbot_small-90M": 5_12}

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  set()
      lowercase__     =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            lowercase__     =  char

      lowercase__     =  set(A__	)
      return pairs



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  Tuple       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			int="__start__"			,				lowerCAmelCase  :			Dict="__end__"			,				lowerCAmelCase  :			Any="__unk__"			,				lowerCAmelCase  :			str="__null__"			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				List[str]:




         """simple docstring"""

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

         with open(lowerCAmelCase			,				encoding='utf-8') as vocab_handle:
               lowercase__     =  json.load(lowerCAmelCase)
         lowercase__     =  {v: k for k, v in self.encoder.items()}
         with open(lowerCAmelCase			,				encoding='utf-8') as merges_handle:
               lowercase__     =  merges_handle.read().split('\n')[1:-1]
         lowercase__     =  [tuple(merge.split()) for merge in merges]
         lowercase__     =  dict(zip(lowerCAmelCase			,				range(len(lowerCAmelCase))))
         lowercase__     =  {}


   @property
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return len(self.encoder)


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str)	->				str:




         """simple docstring"""

         if token in self.cache:
               return self.cache[token]
         lowercase__     =  re.sub('([.,!?()])'			,				R' \1'			,				lowerCAmelCase)
         lowercase__     =  re.sub('(\')'			,				R' \1 '			,				lowerCAmelCase)
         lowercase__     =  re.sub(R'\s{2,}'			,				' '			,				lowerCAmelCase)
         if "\n" in token:
               lowercase__     =  token.replace('\n'			,				' __newln__')

         lowercase__     =  token.split(' ')
         lowercase__     =  []
         for token in tokens:
               if not len(lowerCAmelCase):
                     continue

               lowercase__     =  token.lower()
               lowercase__     =  tuple(lowerCAmelCase)
               lowercase__     =  tuple(list(word[:-1]) + [word[-1] + '</w>'])
               lowercase__     =  get_pairs(lowerCAmelCase)

               if not pairs:
                     words.append(lowerCAmelCase)
                     continue

               while True:
                     lowercase__     =  min(lowerCAmelCase			,				key=lambda lowerCAmelCase: self.bpe_ranks.get(lowerCAmelCase			,				float('inf')))
                     if bigram not in self.bpe_ranks:
                           break
                     lowercase__,	lowercase__     =  bigram
                     lowercase__     =  []
                     lowercase__     =  0

                     while i < len(lowerCAmelCase):
                           try:
                                 lowercase__     =  word.index(lowerCAmelCase			,				lowerCAmelCase)
                                 new_word.extend(word[i:j])
                                 lowercase__     =  j
                           except ValueError:
                                 new_word.extend(word[i:])
                                 break

                           if word[i] == first and i < len(lowerCAmelCase) - 1 and word[i + 1] == second:
                                 new_word.append(first + second)
                                 i += 2
                           else:
                                 new_word.append(word[i])
                                 i += 1
                     lowercase__     =  tuple(lowerCAmelCase)
                     lowercase__     =  new_word
                     if len(lowerCAmelCase) == 1:
                           break
                     else:
                           lowercase__     =  get_pairs(lowerCAmelCase)
               lowercase__     =  '@@ '.join(lowerCAmelCase)
               lowercase__     =  word[:-4]

               lowercase__     =  word
               words.append(lowerCAmelCase)
         return " ".join(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  []

         lowercase__     =  re.findall(R'\S+\n?'			,				lowerCAmelCase)

         for token in words:
               split_tokens.extend(list(self.bpe(lowerCAmelCase).split(' ')))
         return split_tokens


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  token.lower()
         return self.encoder.get(lowerCAmelCase			,				self.encoder.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			List[str])	->				str:




         """simple docstring"""

         lowercase__     =  ' '.join(lowerCAmelCase).replace('@@ '			,				'').strip()
         return out_string



   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

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

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

         lowercase__     =  0
         with open(lowerCAmelCase			,				'w'			,				encoding='utf-8') as writer:
               writer.write('#version: 0.2\n')
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda lowerCAmelCase: 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!')
                           lowercase__     =  token_index
                     writer.write(' '.join(lowerCAmelCase) + '\n')
                     index += 1

         return vocab_file, merge_file
