




import json
import os
from typing import Optional, Tuple

import regex as re

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


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

_lowerCAmelCase						:   List[str]			=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase						:   Dict			=   {
    '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'},
}

_lowerCAmelCase						:   Tuple			=   {
    'ctrl': 2_5_6,
}

_lowerCAmelCase						:   Any			=   {
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







 UpperCAmelCase__							 =						set()
 UpperCAmelCase__							 =						word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)	)
  UpperCAmelCase__							 =						char

 UpperCAmelCase__							 =						set(A_	)
 return pairs


class 			_UpperCamelCase      (				_A		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		CONTROL_CODES
      def __init__(			self   :int ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Any="<unk>" ,      **lowerCamelCase   :Dict      )     ->			List[Any]:
       super().__init__(unk_token=UpperCamelCase__ ,      **UpperCamelCase__      )

       with open(UpperCamelCase__ ,      encoding="utf-8"      ) as vocab_handle:
        UpperCAmelCase__							 =						json.load(UpperCamelCase__      )
       UpperCAmelCase__							 =						{v: k for k, v in self.encoder.items()}
       with open(UpperCamelCase__ ,      encoding="utf-8"      ) as merges_handle:
        UpperCAmelCase__							 =						merges_handle.read().split("\n"      )[1:-1]
       UpperCAmelCase__							 =						[tuple(merge.split()      ) for merge in merges]
       UpperCAmelCase__							 =						dict(zip(UpperCamelCase__ ,      range(len(UpperCamelCase__      )      )      )      )
       UpperCAmelCase__							 =						{}
      @property
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			int:
       return len(self.encoder      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       return dict(self.encoder ,      **self.added_tokens_encoder      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :List[Any]      )     ->			Any:






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

       if not pairs:
        return token

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

         if word[i] == first and i < len(UpperCamelCase__      ) - 1 and word[i + 1] == second:
          new_word.append(first + second      )
          i += 2
         else:
          new_word.append(word[i]      )
          i += 1
        UpperCAmelCase__							 =						tuple(UpperCamelCase__      )
        UpperCAmelCase__							 =						new_word
        if len(UpperCamelCase__      ) == 1:
         break
        else:
         UpperCAmelCase__							 =						get_pairs(UpperCamelCase__      )
       UpperCAmelCase__							 =						"@@ ".join(UpperCamelCase__      )
       UpperCAmelCase__							 =						word[:-4]
       UpperCAmelCase__							 =						word
       return word
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						[]

       UpperCAmelCase__							 =						re.findall(r"\S+\n?" ,      UpperCamelCase__      )

       for token in words:
        split_tokens.extend(list(self.bpe(UpperCamelCase__      ).split(" "      )      )      )
       return split_tokens
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :str      )     ->			Dict:
       return self.encoder.get(UpperCamelCase__ ,      self.encoder.get(self.unk_token      )      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[Any]      )     ->			Optional[Any]:
       return self.decoder.get(UpperCamelCase__ ,      self.unk_token      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						" ".join(UpperCamelCase__      ).replace("@@ " ,      ""      ).strip()
       return out_string







      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:






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

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

       UpperCAmelCase__							 =						0
       with open(UpperCamelCase__ ,      "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!"      )
          UpperCAmelCase__							 =						token_index
         writer.write(" ".join(UpperCamelCase__      ) + "\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 IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(IPNDMScheduler,)
      UpperCAmelCase_        =		(("""num_inference_steps""", 50),)
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						{"num_train_timesteps": 1000}
       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any]=0 ,      **lowerCamelCase   :str      )     ->			str:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )
        # copy over dummy past residuals
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

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

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

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

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str]=0 ,      **lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

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

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

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

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :int ,      **lowerCamelCase   :Any      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						10
       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase      )

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

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

       return sample
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )

       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

        UpperCAmelCase__							 =						self.dummy_sample
        UpperCAmelCase__							 =						0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase ,      "set_timesteps"      ):
         scheduler.set_timesteps(lowerCamelCase      )
        elif num_inference_steps is not None and not hasattr(lowerCamelCase ,      "set_timesteps"      ):
         UpperCAmelCase__							 =						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.timesteps[5]
        UpperCAmelCase__							 =						scheduler.timesteps[6]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

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

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase ,      time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10] ,      [10, 50, 100]      ):
        self.check_over_forward(num_inference_steps=lowerCamelCase ,      time_step=lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.full_loop()
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_mean.item() - 254_0529      ) < 10





