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       =		logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase       =		{
    "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",
    },
}

_lowerCAmelCase       =		{
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}
@lru_cache()
def 					_snake_case	(  ):
					_UpperCamelCase			     =     (
					    list(range(ord('''!'''						)  ,     ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						)  ,     ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						)  ,     ord('''ÿ'''						) + 1						)						)
					)
					_UpperCamelCase			     =     bs[:]
					_UpperCamelCase			     =     0
					for b in range(2**8						):
										if b not in bs:
															bs.append(__snake_case						)
															cs.append(2**8 + n						)
															n += 1
					_UpperCamelCase			     =     [chr(__snake_case						) for n in cs]
					return dict(zip(__snake_case  ,     __snake_case						)						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     set()
					_UpperCamelCase			     =     word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										_UpperCamelCase			     =     char
					return pairs







class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     VOCAB_FILES_NAMES
							UpperCAmelCase				   =     PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase				   =     ["input_ids", "attention_mask"]







							def __init__(   self      :     Dict ,			_A      :     Optional[int] ,			_A      :     List[Any] ,			_A      :     List[str]="replace" ,			_A      :     Optional[int]="<s>" ,			_A      :     Optional[int]="</s>" ,			_A      :     str="</s>" ,			_A      :     Any="<s>" ,			_A      :     Tuple="<unk>" ,			_A      :     Dict="<pad>" ,			_A      :     List[Any]="<mask>" ,			_A      :     List[Any]=False ,			**_A      :     Tuple ,			):
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else bos_token
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else eos_token
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else sep_token
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else cls_token
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else unk_token
												_UpperCamelCase			     =     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
												_UpperCamelCase			     =     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:
																	_UpperCamelCase			     =     json.load(_A		)
												_UpperCamelCase			     =     {v: k for k, v in self.encoder.items()}
												_UpperCamelCase			     =     errors  # how to handle errors in decoding
												_UpperCamelCase			     =     bytes_to_unicode()
												_UpperCamelCase			     =     {v: k for k, v in self.byte_encoder.items()}
												with open(_A ,			encoding='''utf-8'''		) as merges_handle:
																	_UpperCamelCase			     =     merges_handle.read().split('''\n'''		)[1:-1]
												_UpperCamelCase			     =     [tuple(merge.split()		) for merge in bpe_merges]
												_UpperCamelCase			     =     dict(zip(_A ,			range(len(_A		)		)		)		)
												_UpperCamelCase			     =     {}
												_UpperCamelCase			     =     add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												_UpperCamelCase			     =     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[Any]		):
												return len(self.encoder		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												return dict(self.encoder ,			**self.added_tokens_encoder		)







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Dict		):
												if token in self.cache:
																	return self.cache[token]
												_UpperCamelCase			     =     tuple(_A		)
												_UpperCamelCase			     =     get_pairs(_A		)

												if not pairs:
																	return token

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







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Tuple		):
												_UpperCamelCase			     =     []
												for token in re.findall(self.pat ,			_A		):
																	_UpperCamelCase			     =     ''''''.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  UpperCamelCase_						(   self      :     Any ,			_A      :     List[str]		):
												return self.encoder.get(_A ,			self.encoder.get(self.unk_token		)		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Tuple		):
												return self.decoder.get(_A		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     List[Any]		):
												_UpperCamelCase			     =     ''''''.join(_A		)
												_UpperCamelCase			     =     bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8''' ,			errors=self.errors		)
												return text







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     str ,			_A      :     Optional[str] = None		):
												if not os.path.isdir(_A		):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																	return
												_UpperCamelCase			     =     os.path.join(
												    _A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
												_UpperCamelCase			     =     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'''		)

												_UpperCamelCase			     =     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!'''		)
																											_UpperCamelCase			     =     token_index
																						writer.write(''' '''.join(_A		) + '''\n'''		)
																						index += 1

												return vocab_file, merge_file







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												_UpperCamelCase			     =     [self.cls_token_id]
												_UpperCamelCase			     =     [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None ,			_A      :     bool = 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  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												_UpperCamelCase			     =     [self.sep_token_id]
												_UpperCamelCase			     =     [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      :     Dict ,			_A      :     int ,			_A      :     List[Any]=False ,			**_A      :     List[Any]		):
												_UpperCamelCase			     =     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()):
																	_UpperCamelCase			     =     ''' ''' + text
												return (text, kwargs)

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase       =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase       =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase       =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase       =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase       =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase       =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase       =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase       =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase       =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase       =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase       =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase       =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase       =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase       =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowerCAmelCase       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

										print(f'Started running {mod.modelId}!!!')

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

										torch.manual_seed(0)
										random.seed(0)

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		model(noise, time_step).sample

										assert torch.allclose(
										    logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
										)
										print(f'{mod.modelId} has passed successfully!!!')
