


'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase          =     {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

_UpperCamelCase          =     {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}


@lru_cache()
def 		_lowercase   ():




     '''simple docstring'''





     __A  :					List[Any]						     =       (
         list(range(ord("!"       )					,				ord("~"       ) + 1       )       ) + list(range(ord("¡"       )					,				ord("¬"       ) + 1       )       ) + list(range(ord("®"       )					,				ord("ÿ"       ) + 1       )       )
     )
     __A  :					Tuple						     =       bs[:]
     __A  :					Dict						     =       0
     for b in range(2**8       ):
          if b not in bs:
               bs.append(SCREAMING_SNAKE_CASE       )
               cs.append(2**8 + n       )
               n += 1
     __A  :					List[Any]						     =       [chr(SCREAMING_SNAKE_CASE       ) for n in cs]
     return dict(zip(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Tuple						     =       set()
     __A  :					List[Any]						     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)       )
          __A  :					int						     =       char
     return pairs







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






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


     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase="replace" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase=None ,    lowerCamelCase=False ,    lowerCamelCase=False ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					int						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else bos_token
          __A  :					Union[str, Any]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else eos_token
          __A  :					Dict						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else unk_token
          __A  :					Dict						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else pad_token
          super().__init__(
              errors=lowerCamelCase ,    unk_token=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    add_prefix_space=lowerCamelCase ,    add_bos_token=lowerCamelCase ,    **lowerCamelCase ,    )
          __A  :					List[Any]						     =       add_bos_token

          with open(lowerCamelCase ,    encoding="utf-8"		) as vocab_handle:
               __A  :					Optional[int]						     =       json.load(lowerCamelCase		)
          __A  :					str						     =       {v: k for k, v in self.encoder.items()}
          __A  :					Optional[Any]						     =       errors  # how to handle errors in decoding
          __A  :					Tuple						     =       bytes_to_unicode()
          __A  :					Any						     =       {v: k for k, v in self.byte_encoder.items()}
          with open(lowerCamelCase ,    encoding="utf-8"		) as merges_handle:
               __A  :					Any						     =       merges_handle.read().split("\n"		)[1:-1]
          __A  :					List[str]						     =       [tuple(merge.split()		) for merge in bpe_merges]
          __A  :					Any						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)
          __A  :					Tuple						     =       {}
          __A  :					List[Any]						     =       add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __A  :					List[str]						     =       re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.encoder		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if token in self.cache:
               return self.cache[token]
          __A  :					Tuple						     =       tuple(lowerCamelCase		)
          __A  :					str						     =       get_pairs(lowerCamelCase		)

          if not pairs:
               return token

          while True:
               __A  :					List[Any]						     =       min(lowerCamelCase ,    key=lambda lowerCamelCase		: self.bpe_ranks.get(lowerCamelCase ,    float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                    break
               __A     ,__A  :					Optional[int]						     =       bigram
               __A  :					Tuple						     =       []
               __A  :					Optional[int]						     =       0
               while i < len(lowerCamelCase		):
                    try:
                         __A  :					str						     =       word.index(lowerCamelCase ,    lowerCamelCase		)
                    except ValueError:
                         new_word.extend(word[i:]		)
                         break
                    else:
                         new_word.extend(word[i:j]		)
                         __A  :					Dict						     =       j

                    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
               __A  :					int						     =       tuple(lowerCamelCase		)
               __A  :					Optional[Any]						     =       new_word
               if len(lowerCamelCase		) == 1:
                    break
               else:
                    __A  :					Optional[int]						     =       get_pairs(lowerCamelCase		)
          __A  :					str						     =       " ".join(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       word
          return word


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          if self.add_bos_token:
               __A  :					Union[str, Any]						     =       [self.bos_token_id]
          else:
               __A  :					Optional[int]						     =       []

          __A  :					List[Any]						     =       bos_token_ids + token_ids_a

          if token_ids_a is None:
               return output

          return output + bos_token_ids + token_ids_a


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

          if not self.add_bos_token:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

          if token_ids_a is None:
               return [1] + ([0] * len(lowerCamelCase		))
          return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		))


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[Any]						     =       []
          for token in re.findall(self.pat ,    lowerCamelCase		):
               __A  :					Optional[int]						     =       "".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase		).split(" "		)		)
          return bpe_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.decoder.get(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       "".join(lowerCamelCase		)
          __A  :					int						     =       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8" ,    errors=self.errors		)
          return text


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Union[str, Any]						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          __A  :					int						     =       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"		)

          __A  :					Optional[int]						     =       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!"		)
                         __A  :					Optional[int]						     =       token_index
                    writer.write(" ".join(lowerCamelCase		) + "\n"		)
                    index += 1

          return vocab_file, merge_file


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=False ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       kwargs.pop("add_prefix_space" ,    self.add_prefix_space		)
          if is_split_into_words or add_prefix_space:
               __A  :					Tuple						     =       " " + text
          return (text, kwargs)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       []
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(lowerCamelCase ,    add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)
          if len(lowerCamelCase		) > self.model_max_length:
               __A  :					Any						     =       input_ids[-self.model_max_length :]
          return input_ids





'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(ScoreSdeVeScheduler,)
     lowerCamelCase__						=							()


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       4
          __A  :					Union[str, Any]						     =       3
          __A  :					Optional[int]						     =       8
          __A  :					Dict						     =       8

          __A  :					Tuple						     =       torch.rand((batch_size, num_channels, height, width)		)

          return sample


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       4
          __A  :					Dict						     =       3
          __A  :					str						     =       8
          __A  :					List[Any]						     =       8

          __A  :					Dict						     =       batch_size * num_channels * height * width
          __A  :					List[str]						     =       torch.arange(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       sample.reshape(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[Any]						     =       sample / num_elems
          __A  :					Dict						     =       sample.permute(3 ,    0 ,    1 ,    2		)

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       dict(self.forward_default_kwargs		)

          for scheduler_class in self.scheduler_classes:
               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[Any]						     =       0.1 * sample

               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Tuple						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					List[str]						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					Dict						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[Any]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       dict(self.forward_default_kwargs		)
          kwargs.update(lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               __A  :					str						     =       self.get_scheduler_config()
               __A  :					Optional[Any]						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					List[str]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01] ,    [1, 100, 1000]		):
               self.check_over_configs(sigma_min=lowerCamelCase ,    sigma_max=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					Dict						     =       self.scheduler_classes[0]
          __A  :					str						     =       self.get_scheduler_config()
          __A  :					List[Any]						     =       scheduler_class(**lowerCamelCase		)

          __A  :					Union[str, Any]						     =       3

          __A  :					Union[str, Any]						     =       self.dummy_model()
          __A  :					Union[str, Any]						     =       self.dummy_sample_deter

          scheduler.set_sigmas(lowerCamelCase		)
          scheduler.set_timesteps(lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.manual_seed(0		)

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

               for _ in range(scheduler.config.correct_steps		):
                    with torch.no_grad():
                         __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Optional[int]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		).prev_sample

               with torch.no_grad():
                    __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		)
               __A     ,__A  :					int						     =       output.prev_sample, output.prev_sample_mean

          __A  :					Union[str, Any]						     =       torch.sum(torch.abs(lowerCamelCase		)		)
          __A  :					str						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert np.isclose(result_sum.item() ,    143_7275_8528.0		)
          assert np.isclose(result_mean.item() ,    1871_4530.0		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					List[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					int						     =       self.get_scheduler_config()
               __A  :					Any						     =       scheduler_class(**lowerCamelCase		)

               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[str]						     =       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"		):
                    __A  :					Any						     =       num_inference_steps

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    0 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       scheduler.step_pred(lowerCamelCase ,    1 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

