import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)


UpperCAmelCase_			    =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCAmelCase_			    =							{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

UpperCAmelCase_			    =							{'''facebook/blenderbot_small-90M''': 5_1_2}




def       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= set()
 _UpperCamelCase:     Any							= word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  _UpperCamelCase:     Any							= char

 _UpperCamelCase:     str							= set(lowercase		)
 return pairs





class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    Any  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    List[Any]  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    List[str]  			=   ['''input_ids''', '''attention_mask''']



   def __init__(	self					:  Any		,  _lowercase					:  List[str]		,  _lowercase					:  Tuple		,  _lowercase					:  List[Any]="__start__"		,  _lowercase					:  int="__end__"		,  _lowercase					:  List[str]="__unk__"		,  _lowercase					:  Tuple="__null__"		,  **_lowercase					:  Dict		,  ):



    """simple docstring"""



    super().__init__(unk_token=_lowercase		,  bos_token=_lowercase		,  eos_token=_lowercase		,  pad_token=_lowercase		,  **_lowercase							)

    with open(_lowercase		,  encoding='''utf-8'''							) as vocab_handle:
     _UpperCamelCase:     Dict							= json.load(_lowercase							)
    _UpperCamelCase:     Optional[Any]							= {v: k for k, v in self.encoder.items()}
    with open(_lowercase		,  encoding='''utf-8'''							) as merges_handle:
     _UpperCamelCase:     Any							= merges_handle.read().split('''\n'''							)[1:-1]
    _UpperCamelCase:     int							= [tuple(merge.split()							) for merge in merges]
    _UpperCamelCase:     str							= dict(zip(_lowercase		,  range(len(_lowercase							)							)							)							)
    _UpperCamelCase:     Tuple							= {}



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    return len(self.encoder							)



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  str							):



    """simple docstring"""



    if token in self.cache:
     return self.cache[token]
    _UpperCamelCase:     Tuple							= re.sub('''([.,!?()])'''		,  R''' \1'''		,  _lowercase							)
    _UpperCamelCase:     List[Any]							= re.sub('''(\')'''		,  R''' \1 '''		,  _lowercase							)
    _UpperCamelCase:     Dict							= re.sub(R'''\s{2,}'''		,  ''' '''		,  _lowercase							)
    if "\n" in token:
     _UpperCamelCase:     Union[str, Any]							= token.replace('''\n'''		,  ''' __newln__'''							)

    _UpperCamelCase:     Tuple							= token.split(''' '''							)
    _UpperCamelCase:     str							= []
    for token in tokens:
     if not len(_lowercase							):
      continue

     _UpperCamelCase:     Any							= token.lower()
     _UpperCamelCase:     Union[str, Any]							= tuple(_lowercase							)
     _UpperCamelCase:     Union[str, Any]							= tuple(list(word[:-1]							) + [word[-1] + '''</w>''']							)
     _UpperCamelCase:     str							= get_pairs(_lowercase							)

     if not pairs:
      words.append(_lowercase							)
      continue

     while True:
      _UpperCamelCase:     Tuple							= min(_lowercase		,  key=lambda _lowercase							: self.bpe_ranks.get(_lowercase		,  float('''inf'''							)							)							)
      if bigram not in self.bpe_ranks:
       break
      _UpperCamelCase	,							_UpperCamelCase:     int							= bigram
      _UpperCamelCase:     Dict							= []
      _UpperCamelCase:     str							= 0

      while i < len(_lowercase							):
       try:
        _UpperCamelCase:     Optional[Any]							= word.index(_lowercase		,  _lowercase							)
        new_word.extend(word[i:j]							)
        _UpperCamelCase:     int							= j
       except ValueError:
        new_word.extend(word[i:]							)
        break

       if word[i] == first and i < len(_lowercase							) - 1 and word[i + 1] == second:
        new_word.append(first + second							)
        i += 2
       else:
        new_word.append(word[i]							)
        i += 1
      _UpperCamelCase:     Optional[Any]							= tuple(_lowercase							)
      _UpperCamelCase:     Optional[Any]							= new_word
      if len(_lowercase							) == 1:
       break
      else:
       _UpperCamelCase:     str							= get_pairs(_lowercase							)
     _UpperCamelCase:     int							= '''@@ '''.join(_lowercase							)
     _UpperCamelCase:     List[Any]							= word[:-4]

     _UpperCamelCase:     str							= word
     words.append(_lowercase							)
    return " ".join(_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= []

    _UpperCamelCase:     Optional[Any]							= re.findall(R'''\S+\n?'''		,  _lowercase							)

    for token in words:
     split_tokens.extend(list(self.bpe(_lowercase							).split(''' '''							)							)							)
    return split_tokens



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    _UpperCamelCase:     int							= token.lower()
    return self.encoder.get(_lowercase		,  self.encoder.get(self.unk_token							)							)



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  int							):



    """simple docstring"""



    return self.decoder.get(_lowercase		,  self.unk_token							)



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= ''' '''.join(_lowercase							).replace('''@@ '''		,  ''''''							).strip()
    return out_string



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



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

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

    _UpperCamelCase:     str							= 0
    with open(_lowercase		,  '''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 _lowercase							: 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!'''							)
       _UpperCamelCase:     List[str]							= token_index
      writer.write(''' '''.join(_lowercase							) + '''\n'''							)
      index += 1

    return vocab_file, merge_file
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   StableDiffusionInpaintPipeline
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase      :    str  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    str  			=   frozenset(
       []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCAmelCase      :    Tuple  			=   frozenset([]    )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= UNetaDConditionModel(
        block_out_channels=(32, 64)		,  layers_per_block=2		,  sample_size=32		,  in_channels=9		,  out_channels=4		,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,  cross_attention_dim=32		,  attention_head_dim=(2, 4)		,  use_linear_projection=_lowercase		,  )
    _UpperCamelCase:     List[str]							= PNDMScheduler(skip_prk_steps=_lowercase							)
    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= AutoencoderKL(
        block_out_channels=[32, 64]		,  in_channels=3		,  out_channels=3		,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,  latent_channels=4		,  sample_size=128		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     Optional[int]							= CLIPTextConfig(
        bos_token_id=0		,  eos_token_id=2		,  hidden_size=32		,  intermediate_size=37		,  layer_norm_eps=1E-05		,  num_attention_heads=4		,  num_hidden_layers=5		,  pad_token_id=1		,  vocab_size=1_000		,  hidden_act='''gelu'''		,  projection_dim=512		,  )
    _UpperCamelCase:     Optional[int]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     Optional[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  int=0							):



    """simple docstring"""



    _UpperCamelCase:     Any							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							)[0]
    _UpperCamelCase:     Optional[int]							= Image.fromarray(np.uinta(_lowercase							)							).convert('''RGB'''							).resize((64, 64)							)
    _UpperCamelCase:     Optional[Any]							= Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Dict							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Union[str, Any]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _UpperCamelCase:     str							= self.get_dummy_components()
    _UpperCamelCase:     Any							= StableDiffusionInpaintPipeline(**_lowercase							)
    _UpperCamelCase:     Any							= sd_pipe.to(_lowercase							)
    sd_pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Tuple							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     List[Any]							= sd_pipe(**_lowercase							).images
    _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    _UpperCamelCase:     List[str]							= np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3							)




@slow
@require_torch_gpu
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Tuple							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     Dict							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Optional[Any]							= StableDiffusionInpaintPipeline.from_pretrained(_lowercase		,  safety_checker=_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     List[str]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     str							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 9E-3



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     str							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     int							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  torch_dtype=torch.floataa		,  safety_checker=_lowercase		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     Optional[Any]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)
    _UpperCamelCase:     int							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 5E-1



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

    _UpperCamelCase:     Optional[int]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Union[str, Any]							= PNDMScheduler.from_pretrained(_lowercase		,  subfolder='''scheduler'''							)
    _UpperCamelCase:     Optional[int]							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  safety_checker=_lowercase		,  scheduler=_lowercase		,  torch_dtype=torch.floataa		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    _UpperCamelCase:     Optional[int]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Union[str, Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     Union[str, Any]							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  num_inference_steps=2		,  output_type='''np'''		,  )

    _UpperCamelCase:     Dict							= torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9