

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


A__										=      logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
A__										=      {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

A__										=      {
    '''RUCAIBox/mvp''': 1024,
}





@lru_cache()
def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    (
        list(range(ord("!"						)			,							ord("~"						) + 1						)						) + list(range(ord("¡"						)			,							ord("¬"						) + 1						)						) + list(range(ord("®"						)			,							ord("ÿ"						) + 1						)						)
    )
    SCREAMING_SNAKE_CASE__         =    bs[:]
    SCREAMING_SNAKE_CASE__         =    0
    for b in range(2**8						):
        if b not in bs:
            bs.append(A__						)
            cs.append(2**8 + n						)
            n += 1
    SCREAMING_SNAKE_CASE__         =    [chr(A__						) for n in cs]
    return dict(zip(A__			,							A__						)						)





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    set()
    SCREAMING_SNAKE_CASE__         =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)						)
        SCREAMING_SNAKE_CASE__         =    char
    return pairs







class  UpperCamelCase_      (	_lowerCamelCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__					:			Any					    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   ["""input_ids""", """attention_mask"""]



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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      SCREAMING_SNAKE_CASE__         =    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     :					int						)       ->			Union[str, Any]:
      return len(self.encoder						)



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			str:
      return dict(self.encoder  ,	**self.added_tokens_encoder						)



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					int						)       ->			Optional[int]:







      if token in self.cache:
          return self.cache[token]
      SCREAMING_SNAKE_CASE__         =    tuple(__UpperCamelCase						)
      SCREAMING_SNAKE_CASE__         =    get_pairs(__UpperCamelCase						)

      if not pairs:
          return token

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



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					List[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    []
      for token in re.findall(self.pat  ,	__UpperCamelCase						):
          SCREAMING_SNAKE_CASE__         =    ''.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     :					Dict  ,	A_     :					Dict						)       ->			Optional[int]:
      return self.encoder.get(__UpperCamelCase  ,	self.encoder.get(self.unk_token						)						)



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					Any						)       ->			List[str]:
      return self.decoder.get(__UpperCamelCase						)



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					Any						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    ''.join(__UpperCamelCase						)
      SCREAMING_SNAKE_CASE__         =    bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"  ,	errors=self.errors						)
      return text



  def   _UpperCamelCase     (						self     :					List[str]  ,	A_     :					List[Any]  ,	A_     :					Tuple = None						)       ->			Tuple[str]:







      if not os.path.isdir(__UpperCamelCase						):
          logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
          return
      SCREAMING_SNAKE_CASE__         =    os.path.join(
          __UpperCamelCase  ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
      SCREAMING_SNAKE_CASE__         =    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__         =    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__         =    token_index
              writer.write(" ".join(__UpperCamelCase						) + "\n"						)
              index += 1

      return vocab_file, merge_file



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Union[str, Any] = None						)       ->			List[int]:







      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE__         =    [self.cls_token_id]
      SCREAMING_SNAKE_CASE__         =    [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					Any  ,	A_     :					Optional[int] = None  ,	A_     :					Tuple = False						)       ->			List[int]:







      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     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Optional[Any] = None						)       ->			List[int]:
      SCREAMING_SNAKE_CASE__         =    [self.sep_token_id]
      SCREAMING_SNAKE_CASE__         =    [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     :					Optional[int]  ,	A_     :					List[Any]  ,	A_     :					List[Any]=False  ,	**A_     :					Tuple						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    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__         =    ' ' + 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__	:  Union[str, Any]						=  False





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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

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

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			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      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

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

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


