

'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 			lowerCAmelCase__     (					lowerCAmelCase__				,			unittest.TestCase						):




     """simple docstring"""





     __UpperCamelCase									= KandinskyVaaInpaintPipeline
     __UpperCamelCase									= ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
     __UpperCamelCase									= [
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
         """mask_image""",
     ]
     __UpperCamelCase									= [
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCamelCase									= False







     @property
     def 			__lowerCAmelCase	(		self						:	Optional[int]	)       ->			str:
          '''simple docstring'''




          return 3_2







     @property
     def 			__lowerCAmelCase	(		self						:	List[Any]	)       ->			Optional[Any]:
          '''simple docstring'''




          return 3_2







     @property
     def 			__lowerCAmelCase	(		self						:	Union[str, Any]	)       ->			Optional[int]:
          '''simple docstring'''




          return self.time_input_dim







     @property
     def 			__lowerCAmelCase	(		self						:	str	)       ->			Any:
          '''simple docstring'''




          return self.time_input_dim * 4







     @property
     def 			__lowerCAmelCase	(		self						:	str	)       ->			List[Any]:
          '''simple docstring'''




          return 1_0_0







     @property
     def 			__lowerCAmelCase	(		self						:	Dict	)       ->			str:
          '''simple docstring'''




          torch.manual_seed(0	)

          a__   :      Dict     =						{
              '''in_channels''': 9,
              # 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,
          }

          a__   :      Optional[Any]     =						UNetaDConditionModel(**__a	)
          return model







     @property
     def 			__lowerCAmelCase	(		self						:	int	)       ->			str:
          '''simple docstring'''




          return {
              "block_out_channels": [3_2, 6_4],
              "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": 1_2,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }







     @property
     def 			__lowerCAmelCase	(		self						:	List[Any]	)       ->			Union[str, Any]:
          '''simple docstring'''




          torch.manual_seed(0	)
          a__   :      Any     =						VQModel(**self.dummy_movq_kwargs	)
          return model







     def 			__lowerCAmelCase	(		self						:	Optional[int]	)       ->			Any:
          '''simple docstring'''




          a__   :      List[str]     =						self.dummy_unet
          a__   :      Tuple     =						self.dummy_movq

          a__   :      Union[str, Any]     =						DDIMScheduler(
              num_train_timesteps=1_0_0_0      ,						beta_schedule='''linear'''      ,						beta_start=0.00_085      ,						beta_end=0.012      ,						clip_sample=__a      ,						set_alpha_to_one=__a      ,						steps_offset=1      ,						prediction_type='''epsilon'''      ,						thresholding=__a      ,						)

          a__   :      Optional[Any]     =						{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components







     def 			__lowerCAmelCase	(		self						:	Optional[Any]      ,						A__						:	Optional[int]      ,						A__						:	Union[str, Any]=0	)       ->			Optional[Any]:
          '''simple docstring'''




          a__   :      Tuple     =						floats_tensor((1, self.text_embedder_hidden_size)      ,						rng=random.Random(__a	)	).to(__a	)
          a__   :      Optional[Any]     =						floats_tensor((1, self.text_embedder_hidden_size)      ,						rng=random.Random(seed + 1	)	).to(
              __a	)
          # create init_image
          a__   :      Tuple     =						floats_tensor((1, 3, 6_4, 6_4)      ,						rng=random.Random(__a	)	).to(__a	)
          a__   :      Dict     =						image.cpu().permute(0      ,						2      ,						3      ,						1	)[0]
          a__   :      Union[str, Any]     =						Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	).resize((2_5_6, 2_5_6)	)
          # create mask
          a__   :      Dict     =						np.ones((6_4, 6_4)      ,						dtype=np.floataa	)
          a__   :      List[Any]     =						0

          if str(__a	).startswith('''mps'''	):
               a__   :      Any     =						torch.manual_seed(__a	)
          else:
               a__   :      Optional[int]     =						torch.Generator(device=__a	).manual_seed(__a	)
          a__   :      Union[str, Any]     =						{
              '''image''': init_image,
              '''mask_image''': mask,
              '''image_embeds''': image_embeds,
              '''negative_image_embeds''': negative_image_embeds,
              '''generator''': generator,
              '''height''': 6_4,
              '''width''': 6_4,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 4.0,
              '''output_type''': '''np''',
          }
          return inputs







     def 			__lowerCAmelCase	(		self						:	str	)       ->			Dict:
          '''simple docstring'''




          a__   :      List[Any]     =						'''cpu'''

          a__   :      int     =						self.get_dummy_components()

          a__   :      Dict     =						self.pipeline_class(**__a	)
          a__   :      Optional[int]     =						pipe.to(__a	)

          pipe.set_progress_bar_config(disable=__a	)

          a__   :      Dict     =						pipe(**self.get_dummy_inputs(__a	)	)
          a__   :      Dict     =						output.images

          a__   :      Any     =						pipe(
              **self.get_dummy_inputs(__a	)      ,						return_dict=__a      ,						)[0]

          a__   :      Optional[int]     =						image[0, -3:, -3:, -1]
          a__   :      List[Any]     =						image_from_tuple[0, -3:, -3:, -1]

          print(F'image.shape {image.shape}'	)

          assert image.shape == (1, 6_4, 6_4, 3)

          a__   :      Union[str, Any]     =						np.array(
              [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]	)

          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()}'







     def 			__lowerCAmelCase	(		self						:	str	)       ->			int:
          '''simple docstring'''




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






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




     """simple docstring"""





     def 			__lowerCAmelCase	(		self						:	Union[str, Any]	)       ->			Any:
          '''simple docstring'''




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







     def 			__lowerCAmelCase	(		self						:	Tuple	)       ->			int:
          '''simple docstring'''




          a__   :      Dict     =						load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''	)

          a__   :      Any     =						load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''	)
          a__   :      str     =						np.ones((7_6_8, 7_6_8)      ,						dtype=np.floataa	)
          a__   :      List[str]     =						0

          a__   :      int     =						'''a hat'''

          a__   :      Optional[Any]     =						KandinskyVaaPriorPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-prior'''      ,						torch_dtype=torch.floataa	)
          pipe_prior.to(__a	)

          a__   :      str     =						KandinskyVaaInpaintPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''      ,						torch_dtype=torch.floataa	)
          a__   :      List[Any]     =						pipeline.to(__a	)
          pipeline.set_progress_bar_config(disable=__a	)

          a__   :      Any     =						torch.Generator(device='''cpu'''	).manual_seed(0	)
          a__     ,    a__   :      Union[str, Any]     =						pipe_prior(
              __a      ,						generator=__a      ,						num_inference_steps=5      ,						negative_prompt=''''''      ,						).to_tuple()

          a__   :      Optional[int]     =						pipeline(
              image=__a      ,						mask_image=__a      ,						image_embeds=__a      ,						negative_image_embeds=__a      ,						generator=__a      ,						num_inference_steps=1_0_0      ,						height=7_6_8      ,						width=7_6_8      ,						output_type='''np'''      ,						)

          a__   :      int     =						output.images[0]

          assert image.shape == (7_6_8, 7_6_8, 3)

          assert_mean_pixel_difference(__a      ,						__a	)




"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlnet import XLNetTokenizer
else:
	A	:  Optional[Any]            =     None


A	:  Any            =     logging.get_logger(__name__)

A	:  Union[str, Any]            =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A	:  List[Any]            =     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A	:  Union[str, Any]            =     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A	:  Optional[int]            =     "▁"

# Segments (not really needed)
A	:  str            =     0
A	:  Dict            =     1
A	:  List[str]            =     2
A	:  Tuple            =     3
A	:  Tuple            =     4
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       Optional[int]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Dict							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       List[Any]							 ="""left"""
							__UpperCAmelCase		:       List[str]							 =XLNetTokenizer

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=False       ,  __a=True       ,  __a=False       ,  __a="<s>"       ,  __a="</s>"       ,  __a="<unk>"       ,  __a="<sep>"       ,  __a="<pad>"       ,  __a="<cls>"       ,  __a="<mask>"       ,  __a=["<eop>", "<eod>"]       ,  **__a       ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else mask_token

										super().__init__(
										    vocab_file=__a       ,  tokenizer_file=__a       ,  do_lower_case=__a       ,  remove_space=__a       ,  keep_accents=__a       ,  bos_token=__a       ,  eos_token=__a       ,  unk_token=__a       ,  sep_token=__a       ,  pad_token=__a       ,  cls_token=__a       ,  mask_token=__a       ,  additional_special_tokens=__a       ,  **__a       ,  )

										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     do_lower_case
										__lowerCAmelCase							       =     remove_space
										__lowerCAmelCase							       =     keep_accents
										__lowerCAmelCase							       =     vocab_file
										__lowerCAmelCase							       =     False if not self.vocab_file else True

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [2]

										if token_ids_a is None:
													return len(token_ids_a + sep      ) * [0] + cls_segment_id
										return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id



							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."      )

										if not os.path.isdir(__a      ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
													return
										__lowerCAmelCase							       =     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      ):
													copyfile(self.vocab_file       ,  __a      )

										return (out_vocab_file,)




