




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

import regex as re

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


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

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

__lowerCAmelCase		:    str	      =		{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

__lowerCAmelCase		:    Tuple	      =		{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}




@lru_cache()
def       __magic_name__    (			):






			'''simple docstring'''







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




def       __magic_name__    (			A		: Optional[Any]       ):






			'''simple docstring'''







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






class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_	:							List[Any]										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= ["""input_ids""", """attention_mask"""]





				def __init__(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Optional[Any]="replace" ,				__lowerCamelCase    :					Union[str, Any]="<s>" ,				__lowerCamelCase    :					List[Any]="</s>" ,				__lowerCamelCase    :					Tuple="</s>" ,				__lowerCamelCase    :					Optional[Any]="<s>" ,				__lowerCamelCase    :					Optional[Any]="<unk>" ,				__lowerCamelCase    :					int="<pad>" ,				__lowerCamelCase    :					Optional[int]="<mask>" ,				__lowerCamelCase    :					int=False ,				**__lowerCamelCase    :					Union[str, Any] ,				) ->					Dict:
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else bos_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else eos_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else sep_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else cls_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else unk_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else mask_token

							super().__init__(
							    errors=__lowerCamelCase ,				bos_token=__lowerCamelCase ,				eos_token=__lowerCamelCase ,				unk_token=__lowerCamelCase ,				sep_token=__lowerCamelCase ,				cls_token=__lowerCamelCase ,				pad_token=__lowerCamelCase ,				mask_token=__lowerCamelCase ,				add_prefix_space=__lowerCamelCase ,				**__lowerCamelCase ,				)

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

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





				@property
				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Dict:
							return len(self.encoder							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Any:
							return dict(self.encoder ,				**self.added_tokens_encoder							)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					Union[str, Any]							) ->					List[Any]:
							if token in self.cache:
										return self.cache[token]
							a   =      tuple(__lowerCamelCase							)
							a   =      get_pairs(__lowerCamelCase							)

							if not pairs:
										return token

							while True:
										a   =      min(__lowerCamelCase ,				key=lambda __lowerCamelCase							: self.bpe_ranks.get(__lowerCamelCase ,				float("inf"							)							)							)
										if bigram not in self.bpe_ranks:
													break
										a   ,		a   =      bigram
										a   =      []
										a   =      0
										while i < len(__lowerCamelCase							):
													try:
																a   =      word.index(__lowerCamelCase ,				__lowerCamelCase							)
													except ValueError:
																new_word.extend(word[i:]							)
																break
													else:
																new_word.extend(word[i:j]							)
																a   =      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   =      tuple(__lowerCamelCase							)
										a   =      new_word
										if len(__lowerCamelCase							) == 1:
													break
										else:
													a   =      get_pairs(__lowerCamelCase							)
							a   =      " ".join(__lowerCamelCase							)
							a   =      word
							return word





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Union[str, Any]							) ->					int:
							a   =      []
							for token in re.findall(self.pat ,				__lowerCamelCase							):
										a   =      "".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 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					str							) ->					List[str]:
							return self.encoder.get(__lowerCamelCase ,				self.encoder.get(self.unk_token							)							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[str]							) ->					Dict:
							return self.decoder.get(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					List[str]							) ->					List[str]:
							a   =      "".join(__lowerCamelCase							)
							a   =      bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8" ,				errors=self.errors							)
							return text





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[str] = None							) ->					Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a   =      os.path.join(
							    __lowerCamelCase ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
							a   =      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   =      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   =      token_index
													writer.write(" ".join(__lowerCamelCase							) + "\n"							)
													index += 1

							return vocab_file, merge_file





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					List[int] ,				__lowerCamelCase    :					Optional[List[int]] = None							) ->					List[int]:
							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a   =      [self.cls_token_id]
							a   =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					List[int] ,				__lowerCamelCase    :					Optional[List[int]] = None ,				__lowerCamelCase    :					bool = False							) ->					List[int]:
							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 token_ids_a is None:
										return [1] + ([0] * len(__lowerCamelCase							)) + [1]
							return [1] + ([0] * len(__lowerCamelCase							)) + [1, 1] + ([0] * len(__lowerCamelCase							)) + [1]





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					List[int] ,				__lowerCamelCase    :					Optional[List[int]] = None							) ->					List[int]:
							a   =      [self.sep_token_id]
							a   =      [self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[Any]=False ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							a   =      kwargs.pop("add_prefix_space" ,				self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase							) > 0 and not text[0].isspace()):
										a   =      " " + text
							return (text, kwargs)












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= (VQDiffusionScheduler,)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      {
							    "num_vec_classes": 40_97,
							    "num_train_timesteps": 1_00,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Union[str, Any]:
							a   =      4
							a   =      8
							a   =      8

							a   =      torch.randint(0 ,				__lowerCamelCase ,				(batch_size, height * width)							)

							return sample





				@property
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							assert False





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					int:
							def model(__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				*__lowerCamelCase    :					Optional[Any]							):
										a   ,		a   =      sample.shape
										a   =      torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
										a   =      F.log_softmax(logits.double() ,				dim=1							).float()
										return return_value

							return model





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							for timesteps in [2, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							for num_vec_classes in [5, 1_00, 10_00, 40_00]:
										self.check_over_configs(num_vec_classes=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
							pass






