






"""simple docstring"""
import json
import os
from typing import Optional, Tuple

import regex as re

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': {'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_					=      {
    'ctrl': 256,
}

lowerCamelCase_					=      {
    'Pregnancy': 16_8629,
    'Christianity': 7675,
    'Explain': 10_6423,
    'Fitness': 6_3440,
    'Saving': 6_3163,
    'Ask': 2_7171,
    'Ass': 9_5985,
    'Joke': 16_3509,
    'Questions': 4_5622,
    'Thoughts': 4_9605,
    'Retail': 5_2342,
    'Feminism': 16_4338,
    'Writing': 1_1992,
    'Atheism': 19_2263,
    'Netflix': 4_8616,
    'Computing': 3_9639,
    'Opinion': 4_3213,
    'Alone': 4_4967,
    'Funny': 5_8917,
    'Gaming': 4_0358,
    'Human': 4088,
    'India': 1331,
    'Joker': 7_7138,
    'Diet': 3_6206,
    'Legal': 1_1859,
    'Norman': 4939,
    'Tip': 7_2689,
    'Weight': 5_2343,
    'Movies': 4_6273,
    'Running': 2_3425,
    'Science': 2090,
    'Horror': 3_7793,
    'Confession': 6_0572,
    'Finance': 1_2250,
    'Politics': 1_6360,
    'Scary': 19_1985,
    'Support': 1_2654,
    'Technologies': 3_2516,
    'Teenage': 6_6160,
    'Event': 3_2769,
    'Learned': 6_7460,
    'Notion': 18_2770,
    'Wikipedia': 3_7583,
    'Books': 6665,
    'Extract': 7_6050,
    'Confessions': 10_2701,
    'Conspiracy': 7_5932,
    'Links': 6_3674,
    'Narcissus': 15_0425,
    'Relationship': 5_4766,
    'Relationships': 13_4796,
    'Reviews': 4_1671,
    'News': 4256,
    'Translation': 2_6820,
    'multilingual': 12_8406,
}

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Tuple			     =					set()
			UpperCAmelCase_     : Tuple			     =					word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)						)
						UpperCAmelCase_     : List[Any]			     =					char

			UpperCAmelCase_     : Dict			     =					set(__snake_case						)
			return pairs







class 			UpperCamelCase_       (a_     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				CONTROL_CODES





	def __init__(      self					: List[str]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Dict="<unk>"	,	**lowerCAmelCase_					: List[str]						)						->			List[Any]:
				super().__init__(unk_token=_lowerCamelCase	,	**_lowerCamelCase						)

				with open(_lowerCamelCase	,	encoding="utf-8"						) as vocab_handle:
							UpperCAmelCase_     : Optional[int]			     =					json.load(_lowerCamelCase						)
				UpperCAmelCase_     : Optional[int]			     =					{v: k for k, v in self.encoder.items()}
				with open(_lowerCamelCase	,	encoding="utf-8"						) as merges_handle:
							UpperCAmelCase_     : List[str]			     =					merges_handle.read().split("\n"						)[1:-1]
				UpperCAmelCase_     : Union[str, Any]			     =					[tuple(merge.split()						) for merge in merges]
				UpperCAmelCase_     : Union[str, Any]			     =					dict(zip(_lowerCamelCase	,	range(len(_lowerCamelCase						)						)						)						)
				UpperCAmelCase_     : int			     =					{}





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Dict:
				return len(self.encoder						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Union[str, Any]:
				return dict(self.encoder	,	**self.added_tokens_encoder						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]						)						->			Any:
				if token in self.cache:
							return self.cache[token]
				UpperCAmelCase_     : Optional[int]			     =					tuple(_lowerCamelCase						)
				UpperCAmelCase_     : Union[str, Any]			     =					tuple(list(word[:-1]						) + [word[-1] + "</w>"]						)
				UpperCAmelCase_     : Any			     =					get_pairs(_lowerCamelCase						)

				if not pairs:
							return token

				while True:
							UpperCAmelCase_     : Optional[Any]			     =					min(_lowerCamelCase	,	key=lambda lowerCAmelCase_						: self.bpe_ranks.get(_lowerCamelCase	,	float("inf"						)						)						)
							if bigram not in self.bpe_ranks:
										break
							UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					bigram
							UpperCAmelCase_     : Dict			     =					[]
							UpperCAmelCase_     : Union[str, Any]			     =					0
							while i < len(_lowerCamelCase						):
										try:
													UpperCAmelCase_     : Any			     =					word.index(_lowerCamelCase	,	_lowerCamelCase						)
										except ValueError:
													new_word.extend(word[i:]						)
													break
										else:
													new_word.extend(word[i:j]						)
													UpperCAmelCase_     : str			     =					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
							UpperCAmelCase_     : Optional[Any]			     =					tuple(_lowerCamelCase						)
							UpperCAmelCase_     : List[str]			     =					new_word
							if len(_lowerCamelCase						) == 1:
										break
							else:
										UpperCAmelCase_     : str			     =					get_pairs(_lowerCamelCase						)
				UpperCAmelCase_     : str			     =					"@@ ".join(_lowerCamelCase						)
				UpperCAmelCase_     : Dict			     =					word[:-4]
				UpperCAmelCase_     : List[Any]			     =					word
				return word





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : int			     =					[]

				UpperCAmelCase_     : List[Any]			     =					re.findall(R"\S+\n?"	,	_lowerCamelCase						)

				for token in words:
							split_tokens.extend(list(self.bpe(_lowerCamelCase						).split(" "						)						)						)
				return split_tokens





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: int						)						->			Optional[int]:
				return self.encoder.get(_lowerCamelCase	,	self.encoder.get(self.unk_token						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: Union[str, Any]						)						->			Optional[Any]:
				return self.decoder.get(_lowerCamelCase	,	self.unk_token						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: Optional[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : int			     =					" ".join(_lowerCamelCase						).replace("@@ "	,	""						).strip()
				return out_string





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int = None						)						->			Any:
				if not os.path.isdir(_lowerCamelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
				UpperCAmelCase_     : List[str]			     =					os.path.join(
				    _lowerCamelCase	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
				UpperCAmelCase_     : 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"						)

				UpperCAmelCase_     : 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!"						)
													UpperCAmelCase_     : Any			     =					token_index
										writer.write(" ".join(_lowerCamelCase						) + "\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)











"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


