






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	@property
	def       __magic_name__    (    self			)							->				Optional[int]:
							torch.manual_seed(0			)
							__magic_name__      :       Tuple       						= UNetaDModel(
							    sample_size=(32, 64)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")      ,       )
							return model
	@property
	def       __magic_name__    (    self			)							->				List[str]:
							torch.manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= UNetaDConditionModel(
							    sample_size=(64, 32)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       cross_attention_dim=10      ,       )
							return model
	@property
	def       __magic_name__    (    self			)							->				str:
							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL(
							    sample_size=(1_28, 64)      ,       in_channels=1      ,       out_channels=1      ,       latent_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")      ,       up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")      ,       )
							__magic_name__      :       Any       						= UNetaDModel(
							    sample_size=(64, 32)      ,       in_channels=1      ,       out_channels=1      ,       layers_per_block=2      ,       block_out_channels=(1_28, 1_28)      ,       down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")      ,       )
							return vqvae, unet







	@slow
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       Dict       						= Mel(
							    x_res=self.dummy_unet.config.sample_size[1]      ,       y_res=self.dummy_unet.config.sample_size[0]      ,       )

							__magic_name__      :       str       						= DDPMScheduler()
							__magic_name__      :       str       						= AudioDiffusionPipeline(vqvae=a__      ,       unet=self.dummy_unet      ,       mel=a__      ,       scheduler=a__			)
							__magic_name__      :       str       						= pipe.to(a__			)
							pipe.set_progress_bar_config(disable=a__			)

							__magic_name__      :       int       						= torch.Generator(device=a__			).manual_seed(42			)
							__magic_name__      :       Dict       						= pipe(generator=a__      ,       steps=4			)
							__magic_name__      :       List[str]       						= output.audios[0]
							__magic_name__      :       Dict       						= output.images[0]

							__magic_name__      :       int       						= torch.Generator(device=a__			).manual_seed(42			)
							__magic_name__      :       Any       						= pipe(generator=a__      ,       steps=4      ,       return_dict=a__			)
							__magic_name__      :       Any       						= output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							__magic_name__      :       Any       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       Union[str, Any]       						= np.frombuffer(image_from_tuple.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       Any       						= np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() == 0

							__magic_name__      :       Union[str, Any]       						= Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]      ,       y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]      ,       )

							__magic_name__      :       int       						= DDIMScheduler()
							__magic_name__      :       List[Any]       						= self.dummy_vqvae_and_unet
							__magic_name__      :       str       						= AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]      ,       unet=dummy_vqvae_and_unet[1]      ,       mel=a__      ,       scheduler=a__			)
							__magic_name__      :       str       						= pipe.to(a__			)
							pipe.set_progress_bar_config(disable=a__			)

							np.random.seed(0			)
							__magic_name__      :       List[Any]       						= np.random.uniform(-1      ,       1      ,       ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
							__magic_name__      :       List[str]       						= torch.Generator(device=a__			).manual_seed(42			)
							__magic_name__      :       int       						= pipe(raw_audio=a__      ,       generator=a__      ,       start_step=5      ,       steps=10			)
							__magic_name__      :       Dict       						= output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							__magic_name__      :       Dict       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       List[str]       						= np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0

							__magic_name__      :       int       						= self.dummy_unet_condition
							__magic_name__      :       int       						= AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]      ,       unet=a__      ,       mel=a__      ,       scheduler=a__			)
							__magic_name__      :       List[str]       						= pipe.to(a__			)
							pipe.set_progress_bar_config(disable=a__			)

							np.random.seed(0			)
							__magic_name__      :       int       						= torch.rand((1, 1, 10)			)
							__magic_name__      :       Optional[Any]       						= pipe(generator=a__      ,       encoding=a__			)
							__magic_name__      :       Optional[int]       						= output.images[0]
							__magic_name__      :       Dict       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       int       						= np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Optional[int]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Optional[int]       						= torch_device

							__magic_name__      :       List[str]       						= DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""			)
							__magic_name__      :       Tuple       						= pipe.to(a__			)
							pipe.set_progress_bar_config(disable=a__			)

							__magic_name__      :       List[Any]       						= torch.Generator(device=a__			).manual_seed(42			)
							__magic_name__      :       Union[str, Any]       						= pipe(generator=a__			)
							__magic_name__      :       Optional[Any]       						= output.audios[0]
							__magic_name__      :       Dict       						= output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							__magic_name__      :       Dict       						= np.frombuffer(image.tobytes()      ,       dtype="""uint8"""			)[:10]
							__magic_name__      :       List[str]       						= np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() == 0








import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= argparse.ArgumentParser(
						    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""			)
						parser.add_argument("""--file_path""",   type=_A,   default="""data/dump.txt""",   help="""The path to the data."""			)
						parser.add_argument("""--tokenizer_type""",   type=_A,   default="""bert""",   choices=["""bert""", """roberta""", """gpt2"""]			)
						parser.add_argument("""--tokenizer_name""",   type=_A,   default="""bert-base-uncased""",   help="""The tokenizer to use."""			)
						parser.add_argument("""--dump_file""",   type=_A,   default="""data/dump""",   help="""The dump file prefix."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

						logger.info(f'Loading Tokenizer ({args.tokenizer_name})'			)
						if args.tokenizer_type == "bert":
												__magic_name__      :       Tuple       						= BertTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
						elif args.tokenizer_type == "roberta":
												__magic_name__      :       Optional[int]       						= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
												__magic_name__      :       Any       						= tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
						elif args.tokenizer_type == "gpt2":
												__magic_name__      :       Any       						= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       int       						= tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

						logger.info(f'Loading text from {args.file_path}'			)
						with open(args.file_path,   """r""",   encoding="""utf8"""			) as fp:
												__magic_name__      :       Tuple       						= fp.readlines()

						logger.info("""Start encoding"""			)
						logger.info(f'{len(_A			)} examples to process.'			)

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

						__magic_name__      :       Tuple       						= f'{args.dump_file}.{args.tokenizer_name}.pickle'
						__magic_name__      :       Tuple       						= tokenizer.vocab_size
						if vocab_size < (1 << 16):
												__magic_name__      :       Optional[int]       						= [np.uintaa(_A			) for d in rslt]
						else:
												__magic_name__      :       str       						= [np.intaa(_A			) for d in rslt]
						random.shuffle(rslt_			)
						logger.info(f'Dump to {dp_file}'			)
						with open(_A,   """wb"""			) as handle:
												pickle.dump(rslt_,   _A,   protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
		main()
