"""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 		UpperCamelCase			(    a_      ,	unittest.TestCase						):
				UpperCAmelCase				:      Dict    =		KandinskyVaaInpaintPipeline
				UpperCAmelCase				:      Any    =		["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				UpperCAmelCase				:      Optional[Any]    =		[
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				UpperCAmelCase				:      Union[str, Any]    =		[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCAmelCase				:      List[str]    =		False



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



				@property
				def 					_lowercase     (self				:      Optional[int])    ->	Optional[Any]:
					return 32



				@property
				def 					_lowercase     (self				:      Optional[Any])    ->	List[Any]:
					return self.time_input_dim



				@property
				def 					_lowercase     (self				:      List[str])    ->	Optional[int]:
					return self.time_input_dim * 4



				@property
				def 					_lowercase     (self				:      List[Any])    ->	Optional[Any]:
					return 1_00



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

					__snake_case			:							Union[str, Any]      		= {
					    '''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,
					}

					__snake_case			:							Optional[Any]      		= UNetaDConditionModel(**lowercase_)
					return model



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



				def 					_lowercase     (self				:      Dict)    ->	List[str]:
					__snake_case			:							int      		= self.dummy_unet
					__snake_case			:							List[str]      		= self.dummy_movq

					__snake_case			:							Optional[Any]      		= DDIMScheduler(
					    num_train_timesteps=10_00	,  beta_schedule='linear'	,  beta_start=0.00_085	,  beta_end=0.012	,  clip_sample=lowercase_	,  set_alpha_to_one=lowercase_	,  steps_offset=1	,  prediction_type='epsilon'	,  thresholding=lowercase_	,  )

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

					return components



				def 					_lowercase     (self				:      Optional[int]	,  _A				:      Union[str, Any]	,  _A				:      Tuple=0)    ->	List[str]:
					__snake_case			:							Union[str, Any]      		= floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(lowercase_)).to(lowercase_)
					__snake_case			:							int      		= floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1)).to(
					    lowercase_)
					# create init_image
					__snake_case			:							Dict      		= floats_tensor((1, 3, 64, 64)	,  rng=random.Random(lowercase_)).to(lowercase_)
					__snake_case			:							Dict      		= image.cpu().permute(0	,  2	,  3	,  1)[0]
					__snake_case			:							Optional[int]      		= Image.fromarray(np.uinta(lowercase_)).convert('RGB').resize((2_56, 2_56))
					# create mask
					__snake_case			:							int      		= np.ones((64, 64)	,  dtype=np.floataa)
					__snake_case			:							Dict      		= 0

					if str(lowercase_).startswith('mps'):
						__snake_case			:							Union[str, Any]      		= torch.manual_seed(lowercase_)
					else:
						__snake_case			:							Optional[Any]      		= torch.Generator(device=lowercase_).manual_seed(lowercase_)
					__snake_case			:							Dict      		= {
					    '''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				:      Union[str, Any])    ->	str:
					__snake_case			:							Union[str, Any]      		= '''cpu'''

					__snake_case			:							Any      		= self.get_dummy_components()

					__snake_case			:							Dict      		= self.pipeline_class(**lowercase_)
					__snake_case			:							Any      		= pipe.to(lowercase_)

					pipe.set_progress_bar_config(disable=lowercase_)

					__snake_case			:							str      		= pipe(**self.get_dummy_inputs(lowercase_))
					__snake_case			:							Union[str, Any]      		= output.images

					__snake_case			:							Tuple      		= pipe(
					    **self.get_dummy_inputs(lowercase_)	,  return_dict=lowercase_	,  )[0]

					__snake_case			:							Optional[Any]      		= image[0, -3:, -3:, -1]
					__snake_case			:							Dict      		= image_from_tuple[0, -3:, -3:, -1]

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

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

					__snake_case			:							List[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 					_lowercase     (self				:      Union[str, Any])    ->	List[str]:
					super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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



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






				def 					_lowercase     (self				:      List[Any])    ->	Any:
					__snake_case			:							Tuple      		= load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy')

					__snake_case			:							Union[str, Any]      		= load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
					__snake_case			:							Tuple      		= np.ones((7_68, 7_68)	,  dtype=np.floataa)
					__snake_case			:							List[Any]      		= 0

					__snake_case			:							int      		= '''a hat'''

					__snake_case			:							Any      		= KandinskyVaaPriorPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-2-prior'	,  torch_dtype=torch.floataa)
					pipe_prior.to(lowercase_)

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

					__snake_case			:							List[str]      		= torch.Generator(device='cpu').manual_seed(0)
					__snake_case			:							Optional[int]      		= pipe_prior(
					    lowercase_	,  generator=lowercase_	,  num_inference_steps=5	,  negative_prompt=''	,  ).to_tuple()

					__snake_case			:							Optional[int]      		= pipeline(
					    image=lowercase_	,  mask_image=lowercase_	,  image_embeds=lowercase_	,  negative_image_embeds=lowercase_	,  generator=lowercase_	,  num_inference_steps=1_00	,  height=7_68	,  width=7_68	,  output_type='np'	,  )

					__snake_case			:							List[str]      		= output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(lowercase_	,  lowercase_)








"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase  :      Any							      =		logging.getLogger(__name__)

if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		argparse.ArgumentParser(
							    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
							)
							parser.add_argument(
							    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
							)
							parser.add_argument(
							    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
							)
							parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
							__UpperCamelCase  :      Any							      =		parser.parse_args()

							logger.info(F'''Loading data from {args.data_file}''')
							with open(args.data_file, '''rb''') as fp:
														__UpperCamelCase  :      List[Any]							      =		pickle.load(fp)

							logger.info('''Counting occurrences for MLM.''')
							__UpperCamelCase  :      Dict							      =		Counter()
							for tk_ids in data:
														counter.update(tk_ids)
							__UpperCamelCase  :      Optional[Any]							      =		[0] * args.vocab_size
							for k, v in counter.items():
														__UpperCamelCase  :      Dict							      =		v

							logger.info(F'''Dump to {args.token_counts_dump}''')
							with open(args.token_counts_dump, '''wb''') as handle:
														pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




