



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__a      =				logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
__a      =				{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__a      =				{
    "allenai/led-base-16384": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    A_  (			):







						'''simple docstring'''


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







						'''simple docstring'''


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

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Dict						=     VOCAB_FILES_NAMES
					_A		:			Tuple						=     PRETRAINED_VOCAB_FILES_MAP
					_A		:			int						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A		:			List[str]						=     ["""input_ids""", """attention_mask"""]






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

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

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

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

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






					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											return len(self.encoder						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											return dict(self.encoder						,     **self.added_tokens_encoder						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Optional[Any]						)   ->	Union[str, Any]:
											if token in self.cache:
																	return self.cache[token]
											snake_case_ :Optional[Any]						=			tuple(snake_case						)
											snake_case_ :Any						=			get_pairs(snake_case						)

											if not pairs:
																	return token

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

																							if word[i] == first and i < len(snake_case						) - 1 and word[i + 1] == second:
																													new_word.append(first + second						)
																													i += 2
																							else:
																													new_word.append(word[i]						)
																													i += 1
																	snake_case_ :Dict						=			tuple(snake_case						)
																	snake_case_ :Optional[Any]						=			new_word
																	if len(snake_case						) == 1:
																							break
																	else:
																							snake_case_ :Optional[int]						=			get_pairs(snake_case						)
											snake_case_ :Optional[int]						=			""" """.join(snake_case						)
											snake_case_ :Optional[int]						=			word
											return word






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							List[str]						)   ->	str:
											snake_case_ :List[str]						=			[]
											for token in re.findall(self.pat						,     snake_case						):
																	snake_case_ :str						=			"""""".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(snake_case						).split(""" """						)						)
											return bpe_tokens






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple						)   ->	Optional[int]:
											return self.encoder.get(snake_case						,     self.encoder.get(self.unk_token						)						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							List[str]						)   ->	Tuple:
											return self.decoder.get(snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	str:
											snake_case_ :Optional[Any]						=			"""""".join(snake_case						)
											snake_case_ :Dict						=			bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""						,     errors=self.errors						)
											return text






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							str						,     snake_case:							Optional[str] = None						)   ->	Tuple[str]:
											if not os.path.isdir(snake_case						):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																	return
											snake_case_ :Dict						=			os.path.join(
											    snake_case						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
											snake_case_ :Dict						=			os.path.join(
											    snake_case						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

											with open(snake_case						,     """w"""						,     encoding="""utf-8"""						) as f:
																	f.write(json.dumps(self.encoder						,     indent=2						,     sort_keys=snake_case						,     ensure_ascii=snake_case						) + """\n"""						)

											snake_case_ :Optional[Any]						=			0
											with open(snake_case						,     """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 snake_case						: 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!"""						)
																													snake_case_ :Optional[int]						=			token_index
																							writer.write(""" """.join(snake_case						) + """\n"""						)
																							index += 1

											return vocab_file, merge_file






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						)   ->	List[int]:
											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											snake_case_ :List[str]						=			[self.cls_token_id]
											snake_case_ :List[str]						=			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						,     snake_case:							bool = False						)   ->	List[int]:
											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=snake_case						,     token_ids_a=snake_case						,     already_has_special_tokens=snake_case						)

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






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						)   ->	List[int]:
											snake_case_ :Optional[Any]						=			[self.sep_token_id]
											snake_case_ :Dict						=			[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    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple						,     snake_case:							Optional[int]=False						,     **snake_case:							List[Any]						)   ->	Dict:
											snake_case_ :List[Any]						=			kwargs.pop("""add_prefix_space"""						,     self.add_prefix_space						)
											if (is_split_into_words or add_prefix_space) and (len(snake_case						) > 0 and not text[0].isspace()):
																	snake_case_ :Any						=			""" """ + text
											return (text, kwargs)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Union[Dict[str, EncodedInput], BatchEncoding]						,     snake_case:							Optional[int] = None						,     snake_case:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     )   ->	dict:
											snake_case_ :Any						=			super()._pad(
											    encoded_inputs=snake_case						,     max_length=snake_case						,     padding_strategy=snake_case						,     pad_to_multiple_of=snake_case						,     return_attention_mask=snake_case						,     )

											# Load from model defaults
											if return_attention_mask is None:
																	snake_case_ :List[str]						=			"""attention_mask""" in self.model_input_names

											if return_attention_mask and "global_attention_mask" in encoded_inputs:
																	snake_case_ :str						=			encoded_inputs[self.model_input_names[0]]
																	# `global_attention_mask` need to have the same length as other (sequential) inputs.
																	snake_case_ :Tuple						=			len(encoded_inputs["""global_attention_mask"""]						) != len(snake_case						)

																	if needs_to_be_padded:
																							snake_case_ :Tuple						=			len(snake_case						) - len(encoded_inputs["""global_attention_mask"""]						)

																							if self.padding_side == "right":
																													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																													snake_case_ :Union[str, Any]						=			(
																													    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																													)
																							elif self.padding_side == "left":
																													snake_case_ :Optional[int]						=			[-1] * difference + encoded_inputs[
																													    """global_attention_mask"""
																													]
																							else:
																													raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

											return encoded_inputs


'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



