



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					UnCLIPImageVariationPipeline
							_UpperCAmelCase      				=					IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
							_UpperCAmelCase      				=					IMAGE_VARIATION_BATCH_PARAMS

							_UpperCAmelCase      				=					[
							    '''generator''',
							    '''return_dict''',
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]
							_UpperCAmelCase      				=					False






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return 32






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






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






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
									return self.time_input_dim * 4






							@property
							def        lowerCamelCase_   (  self						)			->							Tuple:
									return 100






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
									return tokenizer






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		CLIPTextConfig(
									    bos_token_id=0	,       eos_token_id=2	,       hidden_size=self.text_embedder_hidden_size	,       projection_dim=self.text_embedder_hidden_size	,       intermediate_size=37	,       layer_norm_eps=1E-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       )
									return CLIPTextModelWithProjection(snake_case						)






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size	,       projection_dim=self.text_embedder_hidden_size	,       num_hidden_layers=5	,       num_attention_heads=4	,       image_size=32	,       intermediate_size=37	,       patch_size=1	,       )
									return CLIPVisionModelWithProjection(snake_case						)






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

									_UpperCAmelCase							 =		{
									    'clip_embeddings_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'cross_attention_dim': self.cross_attention_dim,
									}

									_UpperCAmelCase							 =		UnCLIPTextProjModel(**snake_case						)
									return model






							@property
							def        lowerCamelCase_   (  self						)			->							Tuple:
									torch.manual_seed(0						)

									_UpperCAmelCase							 =		{
									    'sample_size': 32,
									    # RGB in channels
									    'in_channels': 3,
									    # Out channels is double in channels because predicts mean and variance
									    'out_channels': 6,
									    '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,
									    'cross_attention_dim': self.cross_attention_dim,
									    'attention_head_dim': 4,
									    'resnet_time_scale_shift': 'scale_shift',
									    'class_embed_type': 'identity',
									}

									_UpperCAmelCase							 =		UNetaDConditionModel(**snake_case						)
									return model






							@property
							def        lowerCamelCase_   (  self						)			->							Tuple:
									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}






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

									_UpperCAmelCase							 =		UNetaDModel(**self.dummy_super_res_kwargs						)
									return model






							@property
							def        lowerCamelCase_   (  self						)			->							List[str]:
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1						)

									_UpperCAmelCase							 =		UNetaDModel(**self.dummy_super_res_kwargs						)
									return model






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		self.dummy_decoder
									_UpperCAmelCase							 =		self.dummy_text_proj
									_UpperCAmelCase							 =		self.dummy_text_encoder
									_UpperCAmelCase							 =		self.dummy_tokenizer
									_UpperCAmelCase							 =		self.dummy_super_res_first
									_UpperCAmelCase							 =		self.dummy_super_res_last

									_UpperCAmelCase							 =		UnCLIPScheduler(
									    variance_type='learned_range'	,       prediction_type='epsilon'	,       num_train_timesteps=1000	,       )

									_UpperCAmelCase							 =		UnCLIPScheduler(
									    variance_type='fixed_small_log'	,       prediction_type='epsilon'	,       num_train_timesteps=1000	,       )

									_UpperCAmelCase							 =		CLIPImageProcessor(crop_size=32	,       size=32						)

									_UpperCAmelCase							 =		self.dummy_image_encoder

									return {
									    "decoder": decoder,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "text_proj": text_proj,
									    "feature_extractor": feature_extractor,
									    "image_encoder": image_encoder,
									    "super_res_first": super_res_first,
									    "super_res_last": super_res_last,
									    "decoder_scheduler": decoder_scheduler,
									    "super_res_scheduler": super_res_scheduler,
									}






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0	,       snake_case=True						)			->							int:
									_UpperCAmelCase							 =		floats_tensor((1, 3, 32, 32)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)

									if pil_image:
											_UpperCAmelCase							 =		input_image * 0.5 + 0.5
											_UpperCAmelCase							 =		input_image.clamp(0	,       1						)
											_UpperCAmelCase							 =		input_image.cpu().permute(0	,       2	,       3	,       1						).float().numpy()
											_UpperCAmelCase							 =		DiffusionPipeline.numpy_to_pil(snake_case						)[0]

									return {
									    "image": input_image,
									    "generator": generator,
									    "decoder_num_inference_steps": 2,
									    "super_res_num_inference_steps": 2,
									    "output_type": "np",
									}






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)

									_UpperCAmelCase							 =		pipe(**snake_case						)
									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)

									_UpperCAmelCase							 =		pipe(
									    **snake_case	,       return_dict=snake_case	,       )[0]

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

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

									_UpperCAmelCase							 =		np.array(
									    [
									        0.9997,
									        0.0002,
									        0.9997,
									        0.9997,
									        0.9969,
									        0.0023,
									        0.9997,
									        0.9969,
									        0.9970,
									    ]						)

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






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)

									_UpperCAmelCase							 =		pipe(**snake_case						)
									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)

									_UpperCAmelCase							 =		pipe(
									    **snake_case	,       return_dict=snake_case	,       )[0]

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

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

									_UpperCAmelCase							 =		np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]						)

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






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)
									_UpperCAmelCase							 =		[
									    pipeline_inputs['image'],
									    pipeline_inputs['image'],
									]

									_UpperCAmelCase							 =		pipe(**snake_case						)
									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)
									_UpperCAmelCase							 =		[
									    tuple_pipeline_inputs['image'],
									    tuple_pipeline_inputs['image'],
									]

									_UpperCAmelCase							 =		pipe(
									    **snake_case	,       return_dict=snake_case	,       )[0]

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

									assert image.shape == (2, 64, 64, 3)

									_UpperCAmelCase							 =		np.array(
									    [
									        0.9997,
									        0.9989,
									        0.0008,
									        0.0021,
									        0.9960,
									        0.0018,
									        0.0014,
									        0.0002,
									        0.9933,
									    ]						)

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






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		torch.device('cpu'						)




									class 			lowercase__			:
																'''simple docstring'''







																_UpperCAmelCase      				=					1







									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(0						)
									_UpperCAmelCase							 =		pipe.decoder.dtype
									_UpperCAmelCase							 =		1

									_UpperCAmelCase							 =		(
									    batch_size,
									    pipe.decoder.config.in_channels,
									    pipe.decoder.config.sample_size,
									    pipe.decoder.config.sample_size,
									)
									_UpperCAmelCase							 =		pipe.prepare_latents(
									    snake_case	,       dtype=snake_case	,       device=snake_case	,       generator=snake_case	,       latents=snake_case	,       scheduler=DummyScheduler()						)

									_UpperCAmelCase							 =		(
									    batch_size,
									    pipe.super_res_first.config.in_channels // 2,
									    pipe.super_res_first.config.sample_size,
									    pipe.super_res_first.config.sample_size,
									)
									_UpperCAmelCase							 =		pipe.prepare_latents(
									    snake_case	,       dtype=snake_case	,       device=snake_case	,       generator=snake_case	,       latents=snake_case	,       scheduler=DummyScheduler()						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)

									_UpperCAmelCase							 =		pipe(
									    **snake_case	,       decoder_latents=snake_case	,       super_res_latents=snake_case						).images

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case	,       pil_image=snake_case						)
									# Don't pass image, instead pass embedding
									_UpperCAmelCase							 =		pipeline_inputs.pop('image'						)
									_UpperCAmelCase							 =		pipe.image_encoder(snake_case						).image_embeds

									_UpperCAmelCase							 =		pipe(
									    **snake_case	,       decoder_latents=snake_case	,       super_res_latents=snake_case	,       image_embeddings=snake_case	,       ).images

									# make sure passing text embeddings manually is identical
									assert np.abs(img_out_a - img_out_a						).max() < 1E-4






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		torch_device == 'cpu'

									# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
									_UpperCAmelCase							 =		1E-2

									self._test_attention_slicing_forward_pass(
									    test_max_difference=snake_case	,       expected_max_diff=snake_case						)






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch_device == 'cpu'
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		[
									    'decoder_num_inference_steps',
									    'super_res_num_inference_steps',
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=snake_case	,       relax_max_difference=snake_case	,       additional_params_copy_to_batched_inputs=snake_case	,       )






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		[
									    'decoder_num_inference_steps',
									    'super_res_num_inference_steps',
									]

									if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											_UpperCAmelCase							 =		[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=snake_case	,       additional_params_copy_to_batched_inputs=snake_case	,       )
									else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=snake_case						)






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							int:
									return super().test_dict_tuple_outputs_equivalent()






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return super().test_save_load_local()






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'						)
									_UpperCAmelCase							 =		load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'						)

									_UpperCAmelCase							 =		UnCLIPImageVariationPipeline.from_pretrained(
									    'kakaobrain/karlo-v1-alpha-image-variations'	,       torch_dtype=torch.floataa						)
									_UpperCAmelCase							 =		pipeline.to(snake_case						)
									pipeline.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
									_UpperCAmelCase							 =		pipeline(
									    snake_case	,       generator=snake_case	,       output_type='np'	,       )

									_UpperCAmelCase							 =		output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(snake_case	,       snake_case	,       15						)







"""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
)
lowercase  							=					logging.getLogger(__name__)

if __name__ == "__main__":
					lowercase  							=					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_05_22, type=int)
					lowercase  							=					parser.parse_args()

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

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


