

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 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







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

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


			@property
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							return 32


			@property
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return 32


			@property
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							return self.time_input_dim


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.time_input_dim * 4


			@property
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return 100


			@property
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
							return tokenizer


			@property
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			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(__a       )


			@property
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			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(__a       )


			@property
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							torch.manual_seed(0       )

							A__        =			{
							    "clip_embeddings_dim": self.text_embedder_hidden_size,
							    "time_embed_dim": self.time_embed_dim,
							    "cross_attention_dim": self.cross_attention_dim,
							}

							A__        =			UnCLIPTextProjModel(**__a       )
							return model


			@property
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							torch.manual_seed(0       )

							A__        =			{
							    "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",
							}

							A__        =			UNetaDConditionModel(**__a       )
							return model


			@property
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							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      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							torch.manual_seed(0       )

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


			@property
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							torch.manual_seed(1       )

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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.dummy_decoder
							A__        =			self.dummy_text_proj
							A__        =			self.dummy_text_encoder
							A__        =			self.dummy_tokenizer
							A__        =			self.dummy_super_res_first
							A__        =			self.dummy_super_res_last

							A__        =			UnCLIPScheduler(
							    variance_type="learned_range"      ,      prediction_type="epsilon"      ,      num_train_timesteps=1000      ,      )

							A__        =			UnCLIPScheduler(
							    variance_type="fixed_small_log"      ,      prediction_type="epsilon"      ,      num_train_timesteps=1000      ,      )

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

							A__        =			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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0      ,      lowercase=True       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			floats_tensor((1, 3, 32, 32)      ,      rng=random.Random(__a       )       ).to(__a       )
							if str(__a       ).startswith("mps"       ):
											A__        =			torch.manual_seed(__a       )
							else:
											A__        =			torch.Generator(device=__a       ).manual_seed(__a       )

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

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


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			"cpu"

							A__        =			self.get_dummy_components()

							A__        =			self.pipeline_class(**__a       )
							A__        =			pipe.to(__a       )

							pipe.set_progress_bar_config(disable=__a       )

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )

							A__        =			pipe(**__a       )
							A__        =			output.images

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )

							A__        =			pipe(
							    **__a      ,      return_dict=__a      ,      )[0]

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

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

							A__        =			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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			"cpu"

							A__        =			self.get_dummy_components()

							A__        =			self.pipeline_class(**__a       )
							A__        =			pipe.to(__a       )

							pipe.set_progress_bar_config(disable=__a       )

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )

							A__        =			pipe(**__a       )
							A__        =			output.images

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )

							A__        =			pipe(
							    **__a      ,      return_dict=__a      ,      )[0]

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

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

							A__        =			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      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			"cpu"

							A__        =			self.get_dummy_components()

							A__        =			self.pipeline_class(**__a       )
							A__        =			pipe.to(__a       )

							pipe.set_progress_bar_config(disable=__a       )

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )
							A__        =			[
							    pipeline_inputs["image"],
							    pipeline_inputs["image"],
							]

							A__        =			pipe(**__a       )
							A__        =			output.images

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )
							A__        =			[
							    tuple_pipeline_inputs["image"],
							    tuple_pipeline_inputs["image"],
							]

							A__        =			pipe(
							    **__a      ,      return_dict=__a      ,      )[0]

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

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

							A__        =			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      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			torch.device("cpu"       )

							class 					a__						:
										"""simple docstring"""







										__lowerCamelCase        =       1

							A__        =			self.get_dummy_components()

							A__        =			self.pipeline_class(**__a       )
							A__        =			pipe.to(__a       )

							pipe.set_progress_bar_config(disable=__a       )

							A__        =			torch.Generator(device=__a       ).manual_seed(0       )
							A__        =			pipe.decoder.dtype
							A__        =			1

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

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

							A__        =			self.get_dummy_inputs(__a      ,      pil_image=__a       )

							A__        =			pipe(
							    **__a      ,      decoder_latents=__a      ,      super_res_latents=__a       ).images

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

							A__        =			pipe(
							    **__a      ,      decoder_latents=__a      ,      super_res_latents=__a      ,      image_embeddings=__a      ,      ).images

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


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			torch_device == "cpu"

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

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


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			torch_device == "cpu"
							A__        =			True
							A__        =			[
							    "decoder_num_inference_steps",
							    "super_res_num_inference_steps",
							]

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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			[
							    "decoder_num_inference_steps",
							    "super_res_num_inference_steps",
							]

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


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							return super().test_dict_tuple_outputs_equivalent()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return super().test_save_load_local()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							return super().test_save_load_optional_components()

@slow
@require_torch_gpu
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"       )
							A__        =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"       )

							A__        =			UnCLIPImageVariationPipeline.from_pretrained(
							    "kakaobrain/karlo-v1-alpha-image-variations"      ,      torch_dtype=torch.floataa       )
							A__        =			pipeline.to(__a       )
							pipeline.set_progress_bar_config(disable=__a       )

							A__        =			torch.Generator(device="cpu"       ).manual_seed(0       )
							A__        =			pipeline(
							    __a      ,      generator=__a      ,      output_type="np"      ,      )

							A__        =			output.images[0]

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

							assert_mean_pixel_difference(__a      ,      __a      ,      15       )




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

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

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

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			Path(SCREAMING_SNAKE_CASE_      )
				save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				for split in ["train"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__    ,				A__        =			pack_examples(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								print(F'packed {split} split from {len(SCREAMING_SNAKE_CASE_      )} examples -> {len(SCREAMING_SNAKE_CASE_      )}.'      )
								Path(save_path / F'{split}.source'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
								Path(save_path / F'{split}.target'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
				for split in ["val", "test"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.source'      )
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.target'      )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

