

'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase :      Tuple					 =     logging.get_logger(__name__)

lowerCamelCase :      List[str]					 =     "▁"

lowerCamelCase :      Union[str, Any]					 =     {"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase :      Tuple					 =     {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase :      str					 =     {
    "facebook/mbart-large-50-one-to-many-mmt": 1_0_2_4,
}

# fmt: off
lowerCamelCase :      Dict					 =     ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				['input_ids', 'attention_mask']

  A__		 =				[]
  A__		 =				[]





  def __init__(		self      :	int							,					_a      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	str=None							,					_a      :	Tuple="</s>"							,					_a      :	List[str]="</s>"							,					_a      :	Any="<s>"							,					_a      :	Dict="<unk>"							,					_a      :	Optional[Any]="<pad>"							,					_a      :	Optional[int]="<mask>"							,					_a      :	Optional[Dict[str, Any]] = None							,					**_a      :	List[Any]							,					)	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else mask_token

       _SCREAMING_SNAKE_CASE							    ={} if sp_model_kwargs is None else sp_model_kwargs

       _SCREAMING_SNAKE_CASE							    =kwargs.get('additional_special_tokens'							,					[] )
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=_a							,					tgt_lang=_a							,					eos_token=_a							,					unk_token=_a							,					sep_token=_a							,					cls_token=_a							,					pad_token=_a							,					mask_token=_a							,					sp_model_kwargs=self.sp_model_kwargs							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(str(_a ) )
       _SCREAMING_SNAKE_CASE							    =vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       _SCREAMING_SNAKE_CASE							    ={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       _SCREAMING_SNAKE_CASE							    =1

       _SCREAMING_SNAKE_CASE							    =len(self.sp_model )
       _SCREAMING_SNAKE_CASE							    ={
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_a )
       }
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.lang_code_to_id.items()}
       _SCREAMING_SNAKE_CASE							    =len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.fairseq_tokens_to_ids.items()}

       _SCREAMING_SNAKE_CASE							    =src_lang if src_lang is not None else 'en_XX'
       _SCREAMING_SNAKE_CASE							    =self.lang_code_to_id[self._src_lang]
       _SCREAMING_SNAKE_CASE							    =tgt_lang
       self.set_src_lang_special_tokens(self._src_lang )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token





  @property
  def 						A  (		self      :	List[Any] )	->    str:





       '''simple docstring'''






       return self._src_lang





  @src_lang.setter
  def 						A  (		self      :	Optional[Any]							,					_a      :	str )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =new_src_lang
       self.set_src_lang_special_tokens(self._src_lang )





  def __getstate__(		self      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.__dict__.copy()
       _SCREAMING_SNAKE_CASE							    =None
       return state





  def __setstate__(		self      :	Dict							,					_a      :	Dict )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =d

       # for backward compatibility
       if not hasattr(self							,					'sp_model_kwargs' ):
            _SCREAMING_SNAKE_CASE							    ={}

       _SCREAMING_SNAKE_CASE							    =spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )





  def 						A  (		self      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ={self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab





  def 						A  (		self      :	Dict							,					_a      :	str )	->    List[str]:





       '''simple docstring'''






       return self.sp_model.encode(_a							,					out_type=_a )





  def 						A  (		self      :	str							,					_a      :	str )	->    int:





       '''simple docstring'''






       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       _SCREAMING_SNAKE_CASE							    =self.sp_model.PieceToId(_a )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





  def 						A  (		self      :	Optional[int]							,					_a      :	int )	->    str:





       '''simple docstring'''






       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset )





  def 						A  (		self      :	List[Any]							,					_a      :	str )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_SNAKE_CASE							    =''
       _SCREAMING_SNAKE_CASE							    =False
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 if not prev_is_special:
                      out_string += " "
                 out_string += self.sp_model.decode(_a ) + token
                 _SCREAMING_SNAKE_CASE							    =True
                 _SCREAMING_SNAKE_CASE							    =[]
            else:
                 current_sub_tokens.append(_a )
                 _SCREAMING_SNAKE_CASE							    =False
       out_string += self.sp_model.decode(_a )
       return out_string.strip()





  def 						A  (		self      :	Optional[int]							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






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

       if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
            copyfile(self.vocab_file							,					_a )
       elif not os.path.isfile(self.vocab_file ):
            with open(_a							,					'wb' ) as fi:
                 _SCREAMING_SNAKE_CASE							    =self.sp_model.serialized_model_proto()
                 fi.write(_a )

       return (out_vocab_file,)





  def 						A  (		self      :	str							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None							,					_a      :	bool = False )	->    List[int]:





       '''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 )

       _SCREAMING_SNAKE_CASE							    =[1] * len(self.prefix_tokens )
       _SCREAMING_SNAKE_CASE							    =[1] * len(self.suffix_tokens )
       if token_ids_a is None:
            return prefix_ones + ([0] * len(_a )) + suffix_ones
       return prefix_ones + ([0] * len(_a )) + ([0] * len(_a )) + suffix_ones





  def 						A  (		self      :	List[str]							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any]							,					_a      :	str							,					_a      :	Optional[str]							,					_a      :	Optional[str]							,					**_a      :	Optional[Any] )	->    int:





       '''simple docstring'''






       if src_lang is None or tgt_lang is None:
            raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
       _SCREAMING_SNAKE_CASE							    =src_lang
       _SCREAMING_SNAKE_CASE							    =self(_a							,					add_special_tokens=_a							,					return_tensors=_a							,					**_a )
       _SCREAMING_SNAKE_CASE							    =self.convert_tokens_to_ids(_a )
       _SCREAMING_SNAKE_CASE							    =tgt_lang_id
       return inputs





  def 						A  (		self      :	Optional[Any]							,					_a      :	List[str]							,					_a      :	str = "en_XX"							,					_a      :	Optional[List[str]] = None							,					_a      :	str = "ro_RO"							,					**_a      :	Optional[Any]							,					)	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =src_lang
       _SCREAMING_SNAKE_CASE							    =tgt_lang
       return super().prepare_seqaseq_batch(_a							,					_a							,					**_a )





  def 						A  (		self      :	int )	->    List[Any]:





       '''simple docstring'''






       return self.set_src_lang_special_tokens(self.src_lang )





  def 						A  (		self      :	Optional[Any] )	->    Optional[Any]:





       '''simple docstring'''






       return self.set_tgt_lang_special_tokens(self.tgt_lang )





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.lang_code_to_id[src_lang]
       _SCREAMING_SNAKE_CASE							    =[self.cur_lang_code_id]
       _SCREAMING_SNAKE_CASE							    =[self.eos_token_id]





  def 						A  (		self      :	Optional[int]							,					_a      :	str )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.lang_code_to_id[tgt_lang]
       _SCREAMING_SNAKE_CASE							    =[self.cur_lang_code_id]
       _SCREAMING_SNAKE_CASE							    =[self.eos_token_id]




"""simple docstring"""







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 						_A  (  _a    ,_a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Dict):
      torch.manual_seed(0)
      a						:		List[Any]      							=			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=__UpperCAmelCase				, )
      a						:		Tuple      							=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      torch.manual_seed(0)
      a						:		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)
      a						:		List[str]      							=			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=1000				, hidden_act="gelu"				, projection_dim=512				, )
      a						:		Any      							=			CLIPTextModel(__UpperCAmelCase)
      a						:		List[str]      							=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

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






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Union[str, Any]=0):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      a						:		Dict      							=			floats_tensor((1, 3, 32, 32)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			image.cpu().permute(0				, 2				, 3				, 1)[0]
      a						:		Union[str, Any]      							=			Image.fromarray(np.uinta(__UpperCAmelCase)).convert("RGB").resize((64, 64))
      a						:		Dict      							=			Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Tuple      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		Tuple      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Optional[Any]      							=			{
          "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 	__snake_case							(					self				:    List[str]):
      a						:		Union[str, Any]      							=			"cpu"  # ensure determinism for the device-dependent torch.Generator
      a						:		Tuple      							=			self.get_dummy_components()
      a						:		Optional[int]      							=			StableDiffusionInpaintPipeline(**__UpperCAmelCase)
      a						:		int      							=			sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Any      							=			self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[int]      							=			sd_pipe(**__UpperCAmelCase).images
      a						:		Optional[int]      							=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a						:		int      							=			np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])

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






    def 	__snake_case							(					self				:    str):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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







    """simple docstring"""




    def 	__snake_case							(					self				:    Union[str, Any]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench.npy")

      a						:		Tuple      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[Any]      							=			StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase				, safety_checker=__UpperCAmelCase)
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Any      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		str      							=			torch.manual_seed(0)
      a						:		Union[str, Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		List[str]      							=			output.images[0]

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






    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		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")

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

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

      a						:		Dict      							=			torch.manual_seed(0)
      a						:		List[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		Optional[Any]      							=			output.images[0]

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






    def 	__snake_case							(					self				:    int):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

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

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

      a						:		Optional[int]      							=			torch.manual_seed(0)
      a						:		str      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=2				, output_type="np"				, )

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






