


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


_snake_case			 =	logging.get_logger(__name__)


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

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

_snake_case			 =	{
    '''facebook/bart-base''': 10_24,
    '''facebook/bart-large''': 10_24,
    '''facebook/bart-large-mnli''': 10_24,
    '''facebook/bart-large-cnn''': 10_24,
    '''facebook/bart-large-xsum''': 10_24,
    '''yjernite/bart_eli5''': 10_24,
}






@lru_cache()
def 						lowercase_(    ):


  '''simple docstring'''

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






def 						lowercase_(    SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

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







class       UpperCAmelCase_   (   _snake_case       ):

   '''simple docstring'''




   __A					: Any							=						VOCAB_FILES_NAMES
   __A					: List[str]							=						PRETRAINED_VOCAB_FILES_MAP
   __A					: Dict							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A					: str							=						["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		,							):






     """simple docstring"""





     lowerCamelCase					:   str       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else bos_token
     lowerCamelCase					:   List[str]       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else eos_token
     lowerCamelCase					:   str       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else sep_token
     lowerCamelCase					:   Optional[Any]       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else cls_token
     lowerCamelCase					:   Optional[int]       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else unk_token
     lowerCamelCase					:   Optional[Any]       =							AddedToken(UpperCamelCase__		,							lstrip=UpperCamelCase__		,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__		,							UpperCamelCase__					) else pad_token

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

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

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

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     lowerCamelCase					:   Tuple       =							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					):






     """simple docstring"""





     return len(self.encoder					)






   def _snake_case       (						self					):






     """simple docstring"""





     return dict(self.encoder		,							**self.added_tokens_encoder					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     if token in self.cache:
       return self.cache[token]
     lowerCamelCase					:   Optional[int]       =							tuple(UpperCamelCase__					)
     lowerCamelCase					:   Optional[Any]       =							get_pairs(UpperCamelCase__					)

     if not pairs:
       return token

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

         if word[i] == first and i < len(UpperCamelCase__					) - 1 and word[i + 1] == second:
           new_word.append(first + second					)
           i += 2
         else:
           new_word.append(word[i]					)
           i += 1
       lowerCamelCase					:   Optional[Any]       =							tuple(UpperCamelCase__					)
       lowerCamelCase					:   Any       =							new_word
       if len(UpperCamelCase__					) == 1:
         break
       else:
         lowerCamelCase					:   List[str]       =							get_pairs(UpperCamelCase__					)
     lowerCamelCase					:   Any       =							" ".join(UpperCamelCase__					)
     lowerCamelCase					:   Any       =							word
     return word






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   str       =							[]
     for token in re.findall(self.pat		,							UpperCamelCase__					):
       lowerCamelCase					:   List[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(UpperCamelCase__					).split(" "					)					)
     return bpe_tokens






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     return self.encoder.get(UpperCamelCase__		,							self.encoder.get(self.unk_token					)					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     return self.decoder.get(UpperCamelCase__					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							"".join(UpperCamelCase__					)
     lowerCamelCase					:   Dict       =							bytearray([self.byte_decoder[c] for c in text]					).decode("utf-8"		,							errors=self.errors					)
     return text






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     if not os.path.isdir(UpperCamelCase__					):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
       return
     lowerCamelCase					:   str       =							os.path.join(
         UpperCamelCase__		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
     lowerCamelCase					:   List[Any]       =							os.path.join(
         UpperCamelCase__		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

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

     lowerCamelCase					:   List[Any]       =							0
     with open(UpperCamelCase__		,							"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					:   Union[str, Any]       =							token_index
         writer.write(" ".join(UpperCamelCase__					) + "\n"					)
         index += 1

     return vocab_file, merge_file






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowerCamelCase					:   Optional[Any]       =							[self.cls_token_id]
     lowerCamelCase					:   List[Any]       =							[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep






   def _snake_case       (						self		,							__A		,							__A = None		,							__A = False					):






     """simple docstring"""





     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=UpperCamelCase__		,							token_ids_a=UpperCamelCase__		,							already_has_special_tokens=UpperCamelCase__					)

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






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     lowerCamelCase					:   Union[str, Any]       =							[self.sep_token_id]
     lowerCamelCase					:   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 _snake_case       (						self		,							__A		,							__A=False		,							**__A					):






     """simple docstring"""





     lowerCamelCase					:   Optional[Any]       =							kwargs.pop("add_prefix_space"		,							self.add_prefix_space					)
     if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__					) > 0 and not text[0].isspace()):
       lowerCamelCase					:   int       =							" " + text
     return (text, kwargs)






'''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        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   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        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   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        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

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