

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class        _SCREAMING_SNAKE_CASE	(   a__      ,  a__      ,  unittest.TestCase):
						_UpperCamelCase:Union[str, Any]     =						StableDiffusionSAGPipeline
						_UpperCamelCase:Optional[int]     =						TEXT_TO_IMAGE_PARAMS
						_UpperCamelCase:int     =						TEXT_TO_IMAGE_BATCH_PARAMS
						_UpperCamelCase:Optional[int]     =						TEXT_TO_IMAGE_IMAGE_PARAMS
						_UpperCamelCase:Any     =						TEXT_TO_IMAGE_IMAGE_PARAMS
						_UpperCamelCase:Tuple     =						False







						def     _snake_case							(       self				)->				List[str]:
													torch.manual_seed(0				)
													lowerCamelCase_				     =UNetaDConditionModel(
													    block_out_channels=(32, 64)			,		layers_per_block=2			,		sample_size=32			,		in_channels=4			,		out_channels=4			,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,		cross_attention_dim=32			,		)
													lowerCamelCase_				     =DDIMScheduler(
													    beta_start=0.0_0_0_8_5			,		beta_end=0.0_1_2			,		beta_schedule="""scaled_linear"""			,		clip_sample=lowerCAmelCase__			,		set_alpha_to_one=lowerCAmelCase__			,		)
													torch.manual_seed(0				)
													lowerCamelCase_				     =AutoencoderKL(
													    block_out_channels=[32, 64]			,		in_channels=3			,		out_channels=3			,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,		latent_channels=4			,		)
													torch.manual_seed(0				)
													lowerCamelCase_				     =CLIPTextConfig(
													    bos_token_id=0			,		eos_token_id=2			,		hidden_size=32			,		intermediate_size=37			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=1000			,		)
													lowerCamelCase_				     =CLIPTextModel(lowerCAmelCase__				)
													lowerCamelCase_				     =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													lowerCamelCase_				     ={
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=0				)->				Optional[int]:
													if str(lowerCAmelCase__				).startswith("""mps"""				):
																				lowerCamelCase_				     =torch.manual_seed(lowerCAmelCase__				)
													else:
																				lowerCamelCase_				     =torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
													lowerCamelCase_				     ={
													    "prompt": ".",
													    "generator": generator,
													    "num_inference_steps": 2,
													    "guidance_scale": 1.0,
													    "sag_scale": 1.0,
													    "output_type": "numpy",
													}
													return inputs







						def     _snake_case							(       self				)->				List[str]:
													super().test_inference_batch_single_identical(expected_max_diff=3E-3				)


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







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







						def     _snake_case							(       self				)->				Dict:
													lowerCamelCase_				     =StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""				)
													lowerCamelCase_				     =sag_pipe.to(lowerCAmelCase__				)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

													lowerCamelCase_				     ="."
													lowerCamelCase_				     =torch.manual_seed(0				)
													lowerCamelCase_				     =sag_pipe(
													    [prompt]			,		generator=lowerCAmelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=20			,		output_type="""np"""				)

													lowerCamelCase_				     =output.images

													lowerCamelCase_				     =image[0, -3:, -3:, -1]

													assert image.shape == (1, 512, 512, 3)
													lowerCamelCase_				     =np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2







						def     _snake_case							(       self				)->				Optional[int]:
													lowerCamelCase_				     =StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""				)
													lowerCamelCase_				     =sag_pipe.to(lowerCAmelCase__				)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

													lowerCamelCase_				     ="."
													lowerCamelCase_				     =torch.manual_seed(0				)
													lowerCamelCase_				     =sag_pipe(
													    [prompt]			,		generator=lowerCAmelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=20			,		output_type="""np"""				)

													lowerCamelCase_				     =output.images

													lowerCamelCase_				     =image[0, -3:, -3:, -1]

													assert image.shape == (1, 512, 512, 3)
													lowerCamelCase_				     =np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2







						def     _snake_case							(       self				)->				Dict:
													lowerCamelCase_				     =StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""				)
													lowerCamelCase_				     =sag_pipe.to(lowerCAmelCase__				)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

													lowerCamelCase_				     ="."
													lowerCamelCase_				     =torch.manual_seed(0				)
													lowerCamelCase_				     =sag_pipe(
													    [prompt]			,		width=768			,		height=512			,		generator=lowerCAmelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=20			,		output_type="""np"""			,		)

													lowerCamelCase_				     =output.images

													assert image.shape == (1, 512, 768, 3)







"""simple docstring"""




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
)
a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






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

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

	logger.info("Start encoding"					)
	logger.info(F"""{len(_lowercase					)} examples to process."""					)

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

		iter += 1
		if iter % interval == 0:
			a      :		Optional[Any]	=				time.time()
			logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""					)
			a      :		Optional[Any]	=				time.time()
	logger.info("Finished binarization"					)
	logger.info(F"""{len(_lowercase					)} examples processed."""					)

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


if __name__ == "__main__":
		main()


