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 :     Tuple          =  logging.get_logger(__name__)

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

__lowerCamelCase :     Union[str, Any]          =  {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
}

__lowerCamelCase :     Any          =  {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}




@lru_cache()
def 				_snake_case     (       ):
  """simple docstring"""







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




def 				_snake_case     (       lowerCAmelCase							:       str     ):
  """simple docstring"""







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







class 		a__						( SCREAMING_SNAKE_CASE__ ):
     A     	=							VOCAB_FILES_NAMES
     A     	=							PRETRAINED_VOCAB_FILES_MAP
     A     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A     	=							['input_ids', 'attention_mask']

     def __init__(   self				:	str,_A				:	List[Any],_A				:	List[str],_A				:	Tuple="replace",_A				:	Optional[int]="<s>",_A				:	Optional[Any]="</s>",_A				:	Tuple="</s>",_A				:	int="<s>",_A				:	Union[str, Any]="<unk>",_A				:	str="<pad>",_A				:	Tuple="<mask>",_A				:	Tuple=False,**_A				:	int,):







       """simple docstring"""






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

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       SCREAMING_SNAKE_CASE_  :		Any    				=   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[int]   ):







       """simple docstring"""






       return len(self.encoder   )

     def     __UpperCamelCase       (   self				:	Optional[Any]   ):







       """simple docstring"""






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

     def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	Dict   ):







       """simple docstring"""






       if token in self.cache:
         return self.cache[token]
       SCREAMING_SNAKE_CASE_  :		Any    				=   tuple(_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_pairs(_UpperCAmelCase   )

       if not pairs:
         return token

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

     def     __UpperCamelCase       (   self				:	Dict,_A				:	Optional[Any]   ):







       """simple docstring"""






       SCREAMING_SNAKE_CASE_  :		Dict    				=   []
       for token in re.findall(self.pat,_UpperCAmelCase   ):
         SCREAMING_SNAKE_CASE_  :		Tuple    				=   "".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     __UpperCamelCase       (   self				:	List[str],_A				:	Any   ):







       """simple docstring"""






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

     def     __UpperCamelCase       (   self				:	str,_A				:	Optional[Any]   ):







       """simple docstring"""






       return self.decoder.get(_UpperCAmelCase   )

     def     __UpperCamelCase       (   self				:	Dict,_A				:	List[Any]   ):







       """simple docstring"""






       SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   "".join(_UpperCAmelCase   )
       SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8",errors=self.errors   )
       return text

     def     __UpperCamelCase       (   self				:	str,_A				:	Dict,_A				:	str = None   ):







       """simple docstring"""






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

       SCREAMING_SNAKE_CASE_  :		List[str]    				=   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!"   )
             SCREAMING_SNAKE_CASE_  :		int    				=   token_index
           writer.write(" ".join(_UpperCAmelCase   ) + "\n"   )
           index += 1

       return vocab_file, merge_file

     def     __UpperCamelCase       (   self				:	Optional[int],_A				:	Union[str, Any],_A				:	Any = None   ):







       """simple docstring"""






       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   [self.cls_token_id]
       SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep

     def     __UpperCamelCase       (   self				:	List[str],_A				:	Optional[int],_A				:	Union[str, Any] = None,_A				:	str = 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     __UpperCamelCase       (   self				:	Optional[Any],_A				:	Union[str, Any],_A				:	Optional[int] = None   ):







       """simple docstring"""






       SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE_  :		List[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     __UpperCamelCase       (   self				:	List[str],_A				:	List[Any],_A				:	Tuple=False,**_A				:	Union[str, Any]   ):







       """simple docstring"""






       SCREAMING_SNAKE_CASE_  :		Union[str, 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()):
         SCREAMING_SNAKE_CASE_  :		Dict    				=   " " + text
       return (text, kwargs)

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase :     List[Any]          =  False






@nightly
@require_torch_gpu
class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

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