






"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        UpperCamelCase			(	snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       Tuple			=					KandinskyInpaintPipeline
 UpperCamelCase					:       str			=					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 UpperCamelCase					:       Tuple			=					[
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 UpperCamelCase					:       Tuple			=					[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 UpperCamelCase					:       Any			=					False


 @property
 def _lowercase	(     self		:						Union[str, Any]							)					->       Any:
   return 32


 @property
 def _lowercase	(     self		:						str							)					->       int:
   return 32


 @property
 def _lowercase	(     self		:						int							)					->       Tuple:
   return self.time_input_dim


 @property
 def _lowercase	(     self		:						Dict							)					->       Union[str, Any]:
   return self.time_input_dim * 4


 @property
 def _lowercase	(     self		:						Optional[int]							)					->       Any:
   return 100


 @property
 def _lowercase	(     self		:						Optional[int]							)					->       Dict:
   _a  :       int             =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
   return tokenizer


 @property
 def _lowercase	(     self		:						Any							)					->       Any:
   torch.manual_seed(0							)
   _a  :       List[Any]             =	MCLIPConfig(
       numDims=self.cross_attention_dim							,						transformerDimensions=self.text_embedder_hidden_size							,						hidden_size=self.text_embedder_hidden_size							,						intermediate_size=37							,						num_attention_heads=4							,						num_hidden_layers=5							,						vocab_size=1005							,						)

   _a  :       Tuple             =	MultilingualCLIP(UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	text_encoder.eval()

   return text_encoder


 @property
 def _lowercase	(     self		:						List[str]							)					->       Tuple:
   torch.manual_seed(0							)

   _a  :       Optional[int]             =	{
       """in_channels""": 9,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 8,
       """addition_embed_type""": """text_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""": """text_image_proj""",
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": None,
   }

   _a  :       str             =	UNetaDConditionModel(**UpperCAmelCase__							)
   return model


 @property
 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   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 _lowercase	(     self		:						Union[str, Any]							)					->       Any:
   torch.manual_seed(0							)
   _a  :       List[str]             =	VQModel(**self.dummy_movq_kwargs							)
   return model


 def _lowercase	(     self		:						Union[str, Any]							)					->       Tuple:
   _a  :       List[Any]             =	self.dummy_text_encoder
   _a  :       Dict             =	self.dummy_tokenizer
   _a  :       Union[str, Any]             =	self.dummy_unet
   _a  :       List[Any]             =	self.dummy_movq

   _a  :       str             =	DDIMScheduler(
       num_train_timesteps=1000							,						beta_schedule="""linear"""							,						beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						clip_sample=UpperCAmelCase__							,						set_alpha_to_one=UpperCAmelCase__							,						steps_offset=1							,						prediction_type="""epsilon"""							,						thresholding=UpperCAmelCase__							,						)

   _a  :       Union[str, Any]             =	{
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }

   return components


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Any=0							)					->       Tuple:
   _a  :       Optional[int]             =	floats_tensor((1, self.cross_attention_dim)							,						rng=random.Random(UpperCAmelCase__							)							).to(UpperCAmelCase__							)
   _a  :       Optional[Any]             =	floats_tensor((1, self.cross_attention_dim)							,						rng=random.Random(seed + 1							)							).to(UpperCAmelCase__							)
   # create init_image
   _a  :       int             =	floats_tensor((1, 3, 64, 64)							,						rng=random.Random(UpperCAmelCase__							)							).to(UpperCAmelCase__							)
   _a  :       Dict             =	image.cpu().permute(0							,						2							,						3							,						1							)[0]
   _a  :       str             =	Image.fromarray(np.uinta(UpperCAmelCase__							)							).convert("""RGB"""							).resize((256, 256)							)
   # create mask
   _a  :       Optional[Any]             =	np.ones((64, 64)							,						dtype=np.floataa							)
   _a  :       Union[str, Any]             =	0

   if str(UpperCAmelCase__							).startswith("""mps"""							):
     _a  :       Optional[Any]             =	torch.manual_seed(UpperCAmelCase__							)
   else:
     _a  :       int             =	torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
   _a  :       Any             =	{
       """prompt""": """horse""",
       """image""": init_image,
       """mask_image""": mask,
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """num_inference_steps""": 2,
       """guidance_scale""": 4.0,
       """output_type""": """np""",
   }
   return inputs


 def _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   _a  :       Tuple             =	"""cpu"""

   _a  :       int             =	self.get_dummy_components()

   _a  :       Optional[int]             =	self.pipeline_class(**UpperCAmelCase__							)
   _a  :       Optional[int]             =	pipe.to(UpperCAmelCase__							)

   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       str             =	pipe(**self.get_dummy_inputs(UpperCAmelCase__							)							)
   _a  :       Dict             =	output.images

   _a  :       Optional[int]             =	pipe(
       **self.get_dummy_inputs(UpperCAmelCase__							)							,						return_dict=UpperCAmelCase__							,						)[0]

   _a  :       List[Any]             =	image[0, -3:, -3:, -1]
   _a  :       Tuple             =	image_from_tuple[0, -3:, -3:, -1]

   print(f"""image.shape {image.shape}"""							)

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

   _a  :       Optional[int]             =	np.array(
       [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]							)

   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 _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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


 def _lowercase	(     self		:						Tuple							)					->       int:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def _lowercase	(     self		:						Optional[Any]							)					->       str:
   _a  :       Optional[int]             =	load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""							)

   _a  :       int             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
   _a  :       int             =	np.ones((768, 768)							,						dtype=np.floataa							)
   _a  :       List[Any]             =	0

   _a  :       Optional[Any]             =	"""a hat"""

   _a  :       int             =	KandinskyPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-prior"""							,						torch_dtype=torch.floataa							)
   pipe_prior.to(UpperCAmelCase__							)

   _a  :       int             =	KandinskyInpaintPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-inpaint"""							,						torch_dtype=torch.floataa							)
   _a  :       Tuple             =	pipeline.to(UpperCAmelCase__							)
   pipeline.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	torch.Generator(device="""cpu"""							).manual_seed(0							)
   _a      ,  _a  :       int             =	pipe_prior(
       UpperCAmelCase__							,						generator=UpperCAmelCase__							,						num_inference_steps=5							,						negative_prompt=""""""							,						).to_tuple()

   _a  :       Dict             =	pipeline(
       UpperCAmelCase__							,						image=UpperCAmelCase__							,						mask_image=UpperCAmelCase__							,						image_embeds=UpperCAmelCase__							,						negative_image_embeds=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						num_inference_steps=100							,						height=768							,						width=768							,						output_type="""np"""							,						)

   _a  :       str             =	output.images[0]

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

   assert_mean_pixel_difference(UpperCAmelCase__							,						UpperCAmelCase__							)









"""simple docstring"""




import json
import os
from typing import Optional, Tuple

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'vocab.json'}

_snake_case          =   {
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_snake_case          =   {'mgp-str': 27}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       List[str]			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       Union[str, Any]			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Optional[int]			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[Any]="[GO]"							,						UpperCAmelCase__		:						Tuple="[GO]"							,						UpperCAmelCase__		:						Optional[int]="[s]"							,						UpperCAmelCase__		:						int="[GO]"							,						**UpperCAmelCase__		:						Dict							)					->       int:
   super().__init__(
       unk_token=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as vocab_handle:
     _a  :       int             =	json.load(UpperCAmelCase__							)
   _a  :       Optional[int]             =	{v: k for k, v in self.vocab.items()}


 @property
 def _lowercase	(     self		:						Dict							)					->       Union[str, Any]:
   return len(self.vocab							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       str:
   return dict(self.vocab							,						**self.added_tokens_encoder							)


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						str							)					->       Union[str, Any]:
   _a  :       Tuple             =	[]
   for s in text:
     char_tokens.extend(UpperCAmelCase__							)
   return char_tokens


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       Dict:
   return self.vocab.get(UpperCAmelCase__							,						self.vocab.get(self.unk_token							)							)


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Tuple							)					->       List[Any]:
   return self.decoder.get(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCAmelCase__							)							)
     return
   _a  :       Tuple             =	os.path.join(
       UpperCAmelCase__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

   return (vocab_file,)

