

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 						_A     (				__UpperCAmelCase						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      int					=   KandinskyVaaImgaImgPipeline
  __lowerCamelCase			:      Tuple					=   ['''image_embeds''', '''negative_image_embeds''', '''image''']
  __lowerCamelCase			:      Optional[int]					=   [
      '''image_embeds''',
      '''negative_image_embeds''',
      '''image''',
  ]
  __lowerCamelCase			:      Any					=   [
      '''generator''',
      '''height''',
      '''width''',
      '''strength''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  __lowerCamelCase			:      str					=   False





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return 32





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return 32





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.time_input_dim





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self.time_input_dim * 4





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return 100





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )

      snake_case							:		List[str]        =		{
          """in_channels""": 4,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 8,
          """addition_embed_type""": """image""",
          """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
          """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
          """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
          """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
          """layers_per_block""": 1,
          """encoder_hid_dim""": self.text_embedder_hidden_size,
          """encoder_hid_dim_type""": """image_proj""",
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": None,
      }

      snake_case							:		Tuple        =		UNetaDConditionModel(**lowerCamelCase__    )
      return model





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		Optional[int]        =		VQModel(**self.dummy_movq_kwargs    )
      return model





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.dummy_unet
      snake_case							:		Tuple        =		self.dummy_movq

      snake_case							:		List[str]        =		{
          """num_train_timesteps""": 1000,
          """beta_schedule""": """linear""",
          """beta_start""": 0.0_00_85,
          """beta_end""": 0.0_12,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      snake_case							:		List[Any]        =		DDIMScheduler(**lowerCamelCase__    )

      snake_case							:		str        =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=0    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
      snake_case							:		Union[str, Any]        =		floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1    )    ).to(
          lowerCamelCase__    )
      # create init_image
      snake_case							:		Dict        =		floats_tensor((1, 3, 64, 64)   ,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
      snake_case							:		int        =		image.cpu().permute(0   ,2   ,3   ,1    )[0]
      snake_case							:		List[str]        =		Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert("""RGB"""    ).resize((256, 256)    )

      if str(lowerCamelCase__    ).startswith("""mps"""    ):
          snake_case							:		Optional[int]        =		torch.manual_seed(lowerCamelCase__    )
      else:
          snake_case							:		str        =		torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
      snake_case							:		Optional[int]        =		{
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """generator""": generator,
          """height""": 64,
          """width""": 64,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		"""cpu"""

      snake_case							:		Dict        =		self.get_dummy_components()

      snake_case							:		Any        =		self.pipeline_class(**lowerCamelCase__    )
      snake_case							:		int        =		pipe.to(lowerCamelCase__    )

      pipe.set_progress_bar_config(disable=lowerCamelCase__    )

      snake_case							:		str        =		pipe(**self.get_dummy_inputs(lowerCamelCase__    )    )
      snake_case							:		Dict        =		output.images

      snake_case							:		str        =		pipe(
          **self.get_dummy_inputs(lowerCamelCase__    )   ,return_dict=lowerCamelCase__   ,)[0]

      snake_case							:		Tuple        =		image[0, -3:, -3:, -1]
      snake_case							:		Dict        =		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      snake_case							:		Tuple        =		np.array(
          [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]    )
      assert (
          np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
      ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
      ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_img2img_frog.npy"""    )

      snake_case							:		Dict        =		load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
      snake_case							:		List[str]        =		"""A red cartoon frog, 4k"""

      snake_case							:		Union[str, Any]        =		KandinskyVaaPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""   ,torch_dtype=torch.floataa    )
      pipe_prior.to(lowerCamelCase__    )

      snake_case							:		List[Any]        =		KandinskyVaaImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-decoder"""   ,torch_dtype=torch.floataa    )
      snake_case							:		Any        =		pipeline.to(lowerCamelCase__    )

      pipeline.set_progress_bar_config(disable=lowerCamelCase__    )

      snake_case							:		str        =		torch.Generator(device="""cpu"""    ).manual_seed(0    )
      snake_case							:		List[str]        =		pipe_prior(
          lowerCamelCase__   ,generator=lowerCamelCase__   ,num_inference_steps=5   ,negative_prompt=""""""   ,).to_tuple()

      snake_case							:		Optional[Any]        =		pipeline(
          image=lowerCamelCase__   ,image_embeds=lowerCamelCase__   ,negative_image_embeds=lowerCamelCase__   ,generator=lowerCamelCase__   ,num_inference_steps=100   ,height=768   ,width=768   ,strength=0.2   ,output_type="""np"""   ,)

      snake_case							:		Dict        =		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(lowerCamelCase__   ,lowerCamelCase__    )







"""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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						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(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file ,     lowerCamelCase__						)
    elif not os.path.isfile(self.vocab_file						):
     with open(lowerCamelCase__ ,     """wb"""						) as fi:
      lowercase__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)