



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




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



       @property
       def 	lowercase__							(				self     :    int       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				1
             UpperCAmelCase_     					=				3
             UpperCAmelCase_     					=				(32, 32)

             UpperCAmelCase_     					=				floats_tensor((batch_size, num_channels) + sizes						,							rng=random.Random(0       )       ).to(_UpperCAmelCase       )
             return image



       @property
       def 	lowercase__							(				self     :    int       )					->		Tuple:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDConditionModel(
                 block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=4						,							out_channels=4						,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,							cross_attention_dim=32						,							)
             return model



       @property
       def 	lowercase__							(				self     :    Dict       )					->		Any:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				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						,							)
             return model



       @property
       def 	lowercase__							(				self     :    str       )					->		Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				RobertaSeriesConfig(
                 hidden_size=32						,							project_dim=32						,							intermediate_size=37						,							layer_norm_eps=1e-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=5006						,							)
             return RobertaSeriesModelWithTransformation(_UpperCAmelCase       )



       @property
       def 	lowercase__							(				self     :    Optional[Any]       )					->		Dict:


             '''simple docstring'''




             def extract(*_UpperCAmelCase     :    Union[str, Any]						,							**_UpperCAmelCase     :    Union[str, Any]       ):
                   class  lowercase__  :







                          '''simple docstring'''


                          def __init__(				self     :    Tuple       )					->		Optional[int]:


                                '''simple docstring'''




                                UpperCAmelCase_     					=				torch.ones([0]       )







                          def 	lowercase__							(				self     :    Dict						,							_UpperCAmelCase     :    Union[str, Any]       )					->		str:


                                '''simple docstring'''




                                self.pixel_values.to(_UpperCAmelCase       )
                                return self



                   return Out()

             return extract



       def 	lowercase__							(				self     :    List[str]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.dummy_cond_unet
             UpperCAmelCase_     					=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
             UpperCAmelCase_     					=				77

             UpperCAmelCase_     					=				self.dummy_image.to(_UpperCAmelCase       )
             UpperCAmelCase_     					=				init_image / 2 + 0.5

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							feature_extractor=self.dummy_extractor						,							)
             UpperCAmelCase_     					=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,							do_normalize=_UpperCAmelCase       )
             UpperCAmelCase_     					=				alt_pipe.to(_UpperCAmelCase       )
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							)

             UpperCAmelCase_     					=				output.images

             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							return_dict=_UpperCAmelCase						,							)[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-3
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 5e-3



       @unittest.skipIf(torch_device != "cuda"						,							"This test requires a GPU"       )
       def 	lowercase__							(				self     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_cond_unet
             UpperCAmelCase_     					=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
             UpperCAmelCase_     					=				77

             UpperCAmelCase_     					=				self.dummy_image.to(_UpperCAmelCase       )

             # put models in fp16
             UpperCAmelCase_     					=				unet.half()
             UpperCAmelCase_     					=				vae.half()
             UpperCAmelCase_     					=				bert.half()

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							feature_extractor=self.dummy_extractor						,							)
             UpperCAmelCase_     					=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,							do_normalize=_UpperCAmelCase       )
             UpperCAmelCase_     					=				alt_pipe.to(_UpperCAmelCase       )
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							).images

             assert image.shape == (1, 32, 32, 3)







       @unittest.skipIf(torch_device != "cuda"						,							"This test requires a GPU"       )
       def 	lowercase__							(				self     :    Tuple       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             # resize to resolution that is divisible by 8 but not 16 or 32
             UpperCAmelCase_     					=				init_image.resize((760, 504)       )

             UpperCAmelCase_     					=				"BAAI/AltDiffusion"
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             UpperCAmelCase_     					=				image[255:258, 383:386, -1]

             assert image.shape == (504, 760, 3)
             UpperCAmelCase_     					=				np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]       )

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



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







       '''simple docstring'''


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


             '''simple docstring'''




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







       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((768, 512)       )
             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"       )

             UpperCAmelCase_     					=				"BAAI/AltDiffusion"
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (512, 768, 3)
             # img2img is flaky across GPUs even in fp32, so using MAE here
             assert np.abs(expected_image - image       ).max() < 1e-2







'''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, PreTrainedTokenizer
from ...utils import logging


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[int]							=    '▁'

_a					:			str							=    {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a					:			Optional[Any]							=    {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a					:			Union[str, Any]							=    {'vinai/bartpho-syllable': 1_024}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			str     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Any     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			int     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_ ,			a_="<s>" ,			a_="</s>" ,			a_="</s>" ,			a_="<s>" ,			a_="<unk>" ,			a_="<pad>" ,			a_="<mask>" ,			a_ = None ,			**a_ ,			)				->					None:


    """simple docstring"""
    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase    				=						AddedToken(a_ ,			lstrip=a_ ,			rstrip=a_  ) if isinstance(a_ ,			a_  ) else mask_token

    UpperCAmelCase    				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=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_ ,			)

    UpperCAmelCase    				=						vocab_file
    UpperCAmelCase    				=						monolingual_vocab_file
    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(str(a_  )  )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase    				=						{}
    UpperCAmelCase    				=						0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(a_  ) not in self.fairseq_tokens_to_ids:
        UpperCAmelCase    				=						cnt
        cnt += 1
    with open(a_ ,			'r' ,			encoding='utf-8'  ) as f:
      for line in f.readlines():
        UpperCAmelCase    				=						line.strip().split()[0]
        UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )
    if str(a_  ) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )

    UpperCAmelCase    				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__(  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
    return state
  def __setstate__(  self ,			a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						d

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

    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = 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_  )

    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  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[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]
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return len(self.fairseq_ids_to_tokens  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{self.convert_ids_to_tokens(a_  ): i for i in range(self.vocab_size  )}
    vocab.update(self.added_tokens_encoder  )
    return vocab
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self.sp_model.encode(a_ ,			out_type=a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.fairseq_ids_to_tokens[index]
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						''.join(a_  ).replace(a_ ,			' '  ).strip()
    return out_string





  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
        UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
        fi.write(a_  )

    if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
        a_  ) and os.path.isfile(self.monolingual_vocab_file  ):
      copyfile(self.monolingual_vocab_file ,			a_  )
    elif not os.path.isfile(self.monolingual_vocab_file  ):
      with open(a_ ,			'w' ,			encoding='utf-8'  ) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(a_  )} \n'''  )

    return out_vocab_file, out_monolingual_vocab_file


