





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


_lowerCamelCase      							=   logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
_lowerCamelCase      							=   {
    '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',
    },
}

_lowerCamelCase      							=   {
    'allenai/led-base-16384': 1_6384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Tuple						=		(
           list(range(ord('''!'''   )			,     ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   )			,     ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   )			,     ord('''ÿ'''   ) + 1   )   )
       )
       _lowerCamelCase				:  int						=		bs[:]
       _lowerCamelCase				:  Optional[int]						=		0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowercase_   )
                     cs.append(2**8 + n   )
                     n += 1
       _lowerCamelCase				:  List[Any]						=		[chr(lowercase_   ) for n in cs]
       return dict(zip(lowercase_			,     lowercase_   )   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Any						=		set()
       _lowerCamelCase				:  Union[str, Any]						=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              _lowerCamelCase				:  int						=		char
       return pairs







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__							=   ["""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				:  Dict						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else bos_token
             _lowerCamelCase				:  List[str]						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else eos_token
             _lowerCamelCase				:  Union[str, Any]						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else sep_token
             _lowerCamelCase				:  Any						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else cls_token
             _lowerCamelCase				:  Dict						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else unk_token
             _lowerCamelCase				:  Tuple						=		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				:  Tuple						=		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				:  Optional[Any]						=		json.load(a__)
             _lowerCamelCase				:  Tuple						=		{v: k for k, v in self.encoder.items()}
             _lowerCamelCase				:  List[str]						=		errors  # how to handle errors in decoding
             _lowerCamelCase				:  str						=		bytes_to_unicode()
             _lowerCamelCase				:  List[Any]						=		{v: k for k, v in self.byte_encoder.items()}
             with open(a__   ,				encoding='''utf-8''') as merges_handle:
                    _lowerCamelCase				:  Optional[Any]						=		merges_handle.read().split('''\n''')[1:-1]
             _lowerCamelCase				:  Optional[int]						=		[tuple(merge.split()) for merge in bpe_merges]
             _lowerCamelCase				:  Tuple						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  Any						=		{}
             _lowerCamelCase				:  Any						=		add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             _lowerCamelCase				:  Dict						=		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 					__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(a__)
             _lowerCamelCase				:  int						=		get_pairs(a__)

             if not pairs:
                    return token

             while True:
                    _lowerCamelCase				:  Optional[int]						=		min(a__   ,				key=lambda a__: self.bpe_ranks.get(a__   ,				float('''inf''')))
                    if bigram not in self.bpe_ranks:
                           break
                    _lowerCamelCase,	_lowerCamelCase				:  Optional[Any]						=		bigram
                    _lowerCamelCase				:  List[str]						=		[]
                    _lowerCamelCase				:  Union[str, Any]						=		0
                    while i < len(a__):
                           try:
                                  _lowerCamelCase				:  str						=		word.index(a__   ,				a__)
                           except ValueError:
                                  new_word.extend(word[i:])
                                  break
                           else:
                                  new_word.extend(word[i:j])
                                  _lowerCamelCase				:  Optional[Any]						=		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				:  Dict						=		tuple(a__)
                    _lowerCamelCase				:  Any						=		new_word
                    if len(a__) == 1:
                           break
                    else:
                           _lowerCamelCase				:  List[Any]						=		get_pairs(a__)
             _lowerCamelCase				:  Union[str, Any]						=		''' '''.join(a__)
             _lowerCamelCase				:  Dict						=		word
             return word


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             for token in re.findall(self.pat   ,				a__):
                    _lowerCamelCase				:  int						=		''''''.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__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.decoder.get(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		''''''.join(a__)
             _lowerCamelCase				:  Tuple						=		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(a__):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
                    return
             _lowerCamelCase				:  Optional[Any]						=		os.path.join(
                 a__   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  List[Any]						=		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				:  Optional[Any]						=		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				:  Optional[int]						=		token_index
                           writer.write(''' '''.join(a__) + '''\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				:  str						=		[self.cls_token_id]
             _lowerCamelCase				:  Optional[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=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):



             """simple docstring"""





             _lowerCamelCase				:  int						=		[self.sep_token_id]
             _lowerCamelCase				:  Union[str, Any]						=		[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				:  int						=		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				:  List[Any]						=		''' ''' + text
             return (text, kwargs)



      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = PaddingStrategy.DO_NOT_PAD   ,				a__ = None   ,				a__ = None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		super()._pad(
                 encoded_inputs=a__   ,				max_length=a__   ,				padding_strategy=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=a__   ,				)

             # Load from model defaults
             if return_attention_mask is None:
                    _lowerCamelCase				:  Optional[int]						=		'''attention_mask''' in self.model_input_names

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

                    if needs_to_be_padded:
                           _lowerCamelCase				:  Tuple						=		len(a__) - 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`
                                  _lowerCamelCase				:  int						=		(
                                      encoded_inputs['''global_attention_mask'''] + [-1] * difference
                                  )
                           elif self.padding_side == "left":
                                  _lowerCamelCase				:  Optional[int]						=		[-1] * difference + encoded_inputs[
                                      '''global_attention_mask'''
                                  ]
                           else:
                                  raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

             return encoded_inputs








from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase      							=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

             self.register_modules(unet=a__   ,				scheduler=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if strength < 0 or strength > 1:
                    raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

             _lowerCamelCase				:  Tuple						=		max(num_inference_steps - init_timestep   ,				0)
             _lowerCamelCase				:  int						=		self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(torch.Tensor, PIL.Image.Image, list)):
                    raise ValueError(
                        F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

             if isinstance(a__   ,				a__) and len(a__) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(a__)}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).sample

                    # 2. predict previous mean of image x_t-1 and add variance depending on eta
                    # eta corresponds to η in paper and should be between [0, 1]
                    # do x_t -> x_t-1
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image, latent_timestep.item())

             return ImagePipelineOutput(images=a__)

