

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					__lowercase  (						__A		,       __A		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	CycleDiffusionPipeline
							UpperCAmelCase_						:		Dict								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    'negative_prompt',
							    'height',
							    'width',
							    'negative_prompt_embeds',
							}
							UpperCAmelCase_						:		int								=	PipelineTesterMixin.required_optional_params - {'latents'}
							UpperCAmelCase_						:		Optional[Any]								=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}				)
							UpperCAmelCase_						:		Optional[int]								=	IMAGE_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase_						:		str								=	IMAGE_TO_IMAGE_IMAGE_PARAMS




							def       snake_case			(					self						)		->       Any:
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					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    ,						)
														A      :					Optional[Any]      		=					DDIMScheduler(
														    beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						beta_schedule='''scaled_linear'''    ,						num_train_timesteps=10_00    ,						clip_sample=snake_case_    ,						set_alpha_to_one=snake_case_    ,						)
														torch.manual_seed(0						)
														A      :					int      		=					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						)
														A      :					List[Any]      		=					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=10_00    ,						)
														A      :					Union[str, Any]      		=					CLIPTextModel(snake_case_						)
														A      :					Dict      		=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Union[str, Any]:
														A      :					Optional[Any]      		=					floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_						)						).to(snake_case_						)
														A      :					str      		=					image / 2 + 0.5
														if str(snake_case_						).startswith('''mps'''						):
																					A      :					Union[str, Any]      		=					torch.manual_seed(snake_case_						)
														else:
																					A      :					int      		=					torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
														A      :					int      		=					{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs




							def       snake_case			(					self						)		->       Dict:
														A      :					Any      		=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Tuple      		=					CycleDiffusionPipeline(**snake_case_						)
														A      :					Union[str, Any]      		=					pipe.to(snake_case_						)
														pipe.set_progress_bar_config(disable=snake_case_						)

														A      :					str      		=					self.get_dummy_inputs(snake_case_						)
														A      :					List[str]      		=					pipe(**snake_case_						)
														A      :					Any      		=					output.images

														A      :					Any      		=					images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														A      :					List[Any]      		=					np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]						)

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




							@unittest.skipIf(torch_device != '''cuda'''    ,						'''This test requires a GPU'''						)
							def       snake_case			(					self						)		->       Tuple:
														A      :					Optional[Any]      		=					self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(snake_case_    ,						'''half'''						):
																												A      :					Dict      		=					module.half()
														A      :					Any      		=					CycleDiffusionPipeline(**snake_case_						)
														A      :					Dict      		=					pipe.to(snake_case_						)
														pipe.set_progress_bar_config(disable=snake_case_						)

														A      :					Tuple      		=					self.get_dummy_inputs(snake_case_						)
														A      :					Tuple      		=					pipe(**snake_case_						)
														A      :					List[str]      		=					output.images

														A      :					str      		=					images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														A      :					Optional[int]      		=					np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]						)

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




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




							@unittest.skip('''non-deterministic pipeline'''						)
							def       snake_case			(					self						)		->       Union[str, Any]:
														return super().test_inference_batch_single_identical()




							@skip_mps
							def       snake_case			(					self						)		->       Union[str, Any]:
														return super().test_dict_tuple_outputs_equivalent()




							@skip_mps
							def       snake_case			(					self						)		->       Union[str, Any]:
														return super().test_save_load_optional_components()






							@skip_mps
							def       snake_case			(					self						)		->       Dict:
														return super().test_attention_slicing_forward_pass()







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





							"""simple docstring"""

							def       snake_case			(					self						)		->       int:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def       snake_case			(					self						)		->       int:
														A      :					str      		=					load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/cycle-diffusion/black_colored_car.png'''						)
														A      :					List[Any]      		=					load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''						)
														A      :					List[str]      		=					init_image.resize((5_12, 5_12)						)

														A      :					str      		=					"""CompVis/stable-diffusion-v1-4"""
														A      :					Optional[Any]      		=					DDIMScheduler.from_pretrained(snake_case_    ,						subfolder='''scheduler'''						)
														A      :					Dict      		=					CycleDiffusionPipeline.from_pretrained(
														    snake_case_    ,						scheduler=snake_case_    ,						safety_checker=snake_case_    ,						torch_dtype=torch.floataa    ,						revision='''fp16'''						)

														pipe.to(snake_case_						)
														pipe.set_progress_bar_config(disable=snake_case_						)
														pipe.enable_attention_slicing()

														A      :					Union[str, Any]      		=					"""A black colored car"""
														A      :					int      		=					"""A blue colored car"""

														A      :					Union[str, Any]      		=					torch.manual_seed(0						)
														A      :					Tuple      		=					pipe(
														    prompt=snake_case_    ,						source_prompt=snake_case_    ,						image=snake_case_    ,						num_inference_steps=1_00    ,						eta=0.1    ,						strength=0.8_5    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=snake_case_    ,						output_type='''np'''    ,						)
														A      :					List[str]      		=					output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image						).max() < 5E-1






							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Dict      		=					load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/cycle-diffusion/black_colored_car.png'''						)
														A      :					Optional[Any]      		=					load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''						)
														A      :					Optional[Any]      		=					init_image.resize((5_12, 5_12)						)

														A      :					Optional[Any]      		=					"""CompVis/stable-diffusion-v1-4"""
														A      :					int      		=					DDIMScheduler.from_pretrained(snake_case_    ,						subfolder='''scheduler'''						)
														A      :					Union[str, Any]      		=					CycleDiffusionPipeline.from_pretrained(snake_case_    ,						scheduler=snake_case_    ,						safety_checker=snake_case_						)

														pipe.to(snake_case_						)
														pipe.set_progress_bar_config(disable=snake_case_						)
														pipe.enable_attention_slicing()

														A      :					Union[str, Any]      		=					"""A black colored car"""
														A      :					Any      		=					"""A blue colored car"""

														A      :					str      		=					torch.manual_seed(0						)
														A      :					Tuple      		=					pipe(
														    prompt=snake_case_    ,						source_prompt=snake_case_    ,						image=snake_case_    ,						num_inference_steps=1_00    ,						eta=0.1    ,						strength=0.8_5    ,						guidance_scale=3    ,						source_guidance_scale=1    ,						generator=snake_case_    ,						output_type='''np'''    ,						)
														A      :					List[Any]      		=					output.images

														assert np.abs(image - expected_image						).max() < 2E-2







"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





