





import json
import os
from typing import Optional, Tuple

import regex as re

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


snake_case_           =      logging.get_logger(__name__)

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

snake_case_           =      {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

snake_case_           =      {
    'ctrl': 256,
}

snake_case_           =      {
    'Pregnancy': 168629,
    'Christianity': 7675,
    'Explain': 106423,
    'Fitness': 63440,
    'Saving': 63163,
    'Ask': 27171,
    'Ass': 95985,
    'Joke': 163509,
    'Questions': 45622,
    'Thoughts': 49605,
    'Retail': 52342,
    'Feminism': 164338,
    'Writing': 11992,
    'Atheism': 192263,
    'Netflix': 48616,
    'Computing': 39639,
    'Opinion': 43213,
    'Alone': 44967,
    'Funny': 58917,
    'Gaming': 40358,
    'Human': 4088,
    'India': 1331,
    'Joker': 77138,
    'Diet': 36206,
    'Legal': 11859,
    'Norman': 4939,
    'Tip': 72689,
    'Weight': 52343,
    'Movies': 46273,
    'Running': 23425,
    'Science': 2090,
    'Horror': 37793,
    'Confession': 60572,
    'Finance': 12250,
    'Politics': 16360,
    'Scary': 191985,
    'Support': 12654,
    'Technologies': 32516,
    'Teenage': 66160,
    'Event': 32769,
    'Learned': 67460,
    'Notion': 182770,
    'Wikipedia': 37583,
    'Books': 6665,
    'Extract': 76050,
    'Confessions': 102701,
    'Conspiracy': 75932,
    'Links': 63674,
    'Narcissus': 150425,
    'Relationship': 54766,
    'Relationships': 134796,
    'Reviews': 41671,
    'News': 4256,
    'Translation': 26820,
    'multilingual': 128406,
}






def lowerCamelCase__   (							snake_case_     :  List[Any]	)				->    str:
   __snake_case								=       set()
   __snake_case								=       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      __snake_case								=       char

   __snake_case								=       set(snake_case_	)
   return pairs







class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[Any]							=   VOCAB_FILES_NAMES
      A_						:    str							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    Union[str, Any]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    Tuple							=   CONTROL_CODES


      def __init__(self	:				Dict    ,      a__	:				Dict    ,      a__	:				Optional[int]    ,      a__	:				Union[str, Any]="<unk>"    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(unk_token=a__    ,      **a__				)

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


      @property
      def        a			(self	:				Dict				):





         """simple docstring"""



         return len(self.encoder				)


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



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


      def        a			(self	:				str    ,      a__	:				str				):





         """simple docstring"""



         if token in self.cache:
            return self.cache[token]
         __snake_case								=       tuple(a__				)
         __snake_case								=       tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
         __snake_case								=       get_pairs(a__				)

         if not pairs:
            return token

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

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


      def        a			(self	:				Optional[Any]    ,      a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       []

         __snake_case								=       re.findall(R'''\S+\n?'''    ,      a__				)

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


      def        a			(self	:				Optional[Any]    ,      a__	:				Dict				):





         """simple docstring"""



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


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



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


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       ''' '''.join(a__				).replace('''@@ '''    ,      ''''''				).strip()
         return out_string


      def        a			(self	:				int    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



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

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

         __snake_case								=       0
         with open(a__    ,      '''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 a__				: 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!'''				)
                  __snake_case								=       token_index
               writer.write(''' '''.join(a__				) + '''\n'''				)
               index += 1

         return vocab_file, merge_file

      # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
      #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
      #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
      #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
      #     return ''.join(tokens_generated_so_far)








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (DPMSolverSinglestepScheduler,)
      A_						:    Union[str, Any]							=   (('num_inference_steps', 25),)


      def        a			(self	:				Dict    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float('''inf'''				),
             '''variance_type''': None,
         }

         config.update(**a__				)
         return config


      def        a			(self	:				str    ,      a__	:				Any=0    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)
            # copy over dummy past residuals
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               new_scheduler.set_timesteps(a__				)
               # copy over dummy past residuals
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         pass


      def        a			(self	:				List[Any]    ,      a__	:				Dict=0    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

            # copy over dummy past residuals (must be after setting timesteps)
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__				)

               # copy over dummy past residual (must be after setting timesteps)
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				int    ,      a__	:				Tuple=None    ,      **a__	:				List[str]				):





         """simple docstring"""



         if scheduler is None:
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       50
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_5_7_4				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3

         __snake_case								=       DEISMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       UniPCMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=a__    ,      prediction_type=a__    ,      sample_max_value=a__    ,      algorithm_type='''dpmsolver++'''    ,      solver_order=a__    ,      solver_type=a__    ,      )


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     __snake_case								=       self.full_loop(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         self.check_over_configs(lambda_min_clipped=-float('''inf'''				)				)
         self.check_over_configs(lambda_min_clipped=-5.1				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.check_over_configs(variance_type=a__				)
         self.check_over_configs(variance_type='''learned_range'''				)


      def        a			(self	:				int				):





         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.full_loop(use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_2_4_8				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_4_5_3				) < 1E-3


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''    ,      use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.0_6_4_9				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         assert sample.dtype == torch.floataa

