


"""simple docstring"""


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


A	:  str            =     logging.get_logger(__name__)


A	:  Union[str, Any]            =     {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
A	:  str            =     {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

A	:  int            =     {
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}


@lru_cache()
def _lowerCamelCase      (						):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       List[Any]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Optional[Any]							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       Tuple							 =["""input_ids""", """attention_mask"""]

							def __init__(	self       ,  __a       ,  __a       ,  __a="replace"       ,  __a="<s>"       ,  __a="</s>"       ,  __a="</s>"       ,  __a="<s>"       ,  __a="<unk>"       ,  __a="<pad>"       ,  __a="<mask>"       ,  __a=False       ,  **__a       ,  ):
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else bos_token
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else eos_token
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else sep_token
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else cls_token
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else unk_token
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else pad_token

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

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

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

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

							@property
							def      snake_case							(	self      ):
										return len(self.encoder      )

							def      snake_case							(	self      ):
										return dict(self.encoder       ,  **self.added_tokens_encoder      )

							def      snake_case							(	self       ,  __a      ):
										if token in self.cache:
													return self.cache[token]
										__lowerCAmelCase							       =     tuple(__a      )
										__lowerCAmelCase							       =     get_pairs(__a      )

										if not pairs:
													return token

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

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     []
										for token in re.findall(self.pat       ,  __a      ):
													__lowerCAmelCase							       =     "".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(__a      ).split(" "      )      )
										return bpe_tokens

							def      snake_case							(	self       ,  __a      ):
										return self.encoder.get(__a       ,  self.encoder.get(self.unk_token      )      )

							def      snake_case							(	self       ,  __a      ):
										return self.decoder.get(__a      )

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "".join(__a      )
										__lowerCAmelCase							       =     bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"       ,  errors=self.errors      )
										return text

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										if not os.path.isdir(__a      ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
													return
										__lowerCAmelCase							       =     os.path.join(
										    __a       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
										__lowerCAmelCase							       =     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"      )

										__lowerCAmelCase							       =     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!"      )
																			__lowerCAmelCase							       =     token_index
																writer.write(" ".join(__a      ) + "\n"      )
																index += 1

										return vocab_file, merge_file

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										__lowerCAmelCase							       =     [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

							def      snake_case							(	self       ,  __a       ,  __a = None       ,  __a = False      ):
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__a       ,  token_ids_a=__a       ,  already_has_special_tokens=__a      )

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

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [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      snake_case							(	self       ,  __a       ,  __a=False       ,  **__a      ):
										__lowerCAmelCase							       =     kwargs.pop("add_prefix_space"       ,  self.add_prefix_space      )
										if (is_split_into_words or add_prefix_space) and (len(__a      ) > 0 and not text[0].isspace()):
													__lowerCAmelCase							       =     " " + text
										return (text, kwargs)








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =(KDPMaDiscreteScheduler,)
							__UpperCAmelCase		:       Optional[Any]							 =1_0

							def      snake_case							(	self       ,  **__a      ):
										__lowerCAmelCase							       =     {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__a      )
										return config

							def      snake_case							(	self      ):
										for timesteps in [10, 50, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a      )

							def      snake_case							(	self      ):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
													self.check_over_configs(beta_start=__a       ,  beta_end=__a      )

							def      snake_case							(	self      ):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__a      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config(prediction_type="v_prediction"      )
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 6.1_1_1_2e-1_0      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7      ) < 1e-2
													assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3

							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(__a      )

										for i, t in enumerate(scheduler.timesteps      ):
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3



							def      snake_case							(	self      ):
										if torch_device == "mps":
													return
										__lowerCAmelCase							       =     self.scheduler_classes[0]
										__lowerCAmelCase							       =     self.get_scheduler_config()
										__lowerCAmelCase							       =     scheduler_class(**__a      )

										scheduler.set_timesteps(self.num_inference_steps       ,  device=__a      )

										__lowerCAmelCase							       =     self.dummy_model()
										__lowerCAmelCase							       =     self.dummy_sample_deter.to(__a      ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													__lowerCAmelCase							       =     scheduler.scale_model_input(__a       ,  __a      )

													__lowerCAmelCase							       =     model(__a       ,  __a      )

													__lowerCAmelCase							       =     scheduler.step(__a       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     output.prev_sample

										__lowerCAmelCase							       =     torch.sum(torch.abs(__a      )      )
										__lowerCAmelCase							       =     torch.mean(torch.abs(__a      )      )

										if str(__a      ).startswith("cpu"      ):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
													assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




