



'''simple docstring'''

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCAmelCase		     =  logging.getLogger(__name__)



def 	__lowerCamelCase	( )     -> int:
		_a	: Union[str, Any]								=				argparse.ArgumentParser(
		    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'    )
		parser.add_argument(
		    '--dataset_name'		,     type=lowerCAmelCase_		,     default='wikitext'		,     help='Name of the training. Explore datasets at: hf.co/datasets.'		,     )
		parser.add_argument(
		    '--dataset_config'		,     type=lowerCAmelCase_		,     default='wikitext-103-raw-v1'		,     help='Configuration name of the dataset.'    )
		parser.add_argument(
		    '--tokenizer_name_or_path'		,     type=lowerCAmelCase_		,     default='sayakpaul/unigram-tokenizer-wikitext'		,     help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'		,     )
		parser.add_argument(
		    '--shard_size'		,     type=lowerCAmelCase_		,     default=1000		,     help='Number of entries to go in a single shard.'		,     )
		parser.add_argument('--split'		,     type=lowerCAmelCase_		,     default='train'		,     choices=['train', 'test', 'validation']    )
		parser.add_argument(
		    '--limit'		,     default=lowerCAmelCase_		,     type=lowerCAmelCase_		,     help='Limit the number of shards (used for debugging).'		,     )
		parser.add_argument(
		    '--max_length'		,     type=lowerCAmelCase_		,     default=512		,     help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
		    ' sequence length that is a multiple of 8.'		,     )
		parser.add_argument(
		    '--output_dir'		,     default='tf-tpu'		,     type=lowerCAmelCase_		,     help='Output directory where the TFRecord shards will be saved. If the'
		    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
		    ' shards will be directly saved to a Google Cloud Storage bucket.'		,     )

		_a	: Optional[Any]								=				parser.parse_args()
		return args



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> Tuple:
		def fn(lowerCAmelCase_    ):
				return tokenizer(examples['text']    )

		return fn



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> Dict:
		_a	: str								=				[]
		for i in range(len(tokenized_data['input_ids']    )    ):
				_a	: List[Any]								=				{
				    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]    )    ),
				    'attention_mask': tf.train.Feature(
				        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]    )    ),
				}
				_a	: List[str]								=				tf.train.Features(feature=lowerCAmelCase_    )
				_a	: Any								=				tf.train.Example(features=lowerCAmelCase_    )
				_a	: Dict								=				example.SerializeToString()
				records.append(lowerCAmelCase_    )
		return records



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> List[Any]:
		_a	: List[str]								=				datasets.load_dataset(args.dataset_name		,     args.dataset_config		,     split=args.split    )

		if args.limit is not None:
				_a	: Dict								=				min(len(lowerCAmelCase_    )		,     args.limit    )
				_a	: Any								=				dataset.select(range(lowerCAmelCase_    )    )
				print(f"""Limiting the dataset to {args.limit} entries."""    )

		_a	: List[str]								=				AutoTokenizer.from_pretrained(args.tokenizer_name_or_path    )

		# Handle output directory creation.
		# For serializing into a Google Cloud Storage Bucket, one needs to first
		# create a bucket.
		if "gs" not in args.output_dir:
				if not os.path.exists(args.output_dir    ):
						os.makedirs(args.output_dir    )
				_a	: Dict								=				os.path.join(args.output_dir		,     args.split    )
				if not os.path.exists(lowerCAmelCase_    ):
						os.makedirs(lowerCAmelCase_    )
		else:
				_a	: str								=				os.path.join(args.output_dir		,     args.split    )

		# Tokenize the whole dataset at once.
		_a	: Optional[Any]								=				tokenize_function(lowerCAmelCase_    )
		_a	: List[Any]								=				dataset.map(lowerCAmelCase_		,     batched=lowerCAmelCase_		,     num_proc=4		,     remove_columns=['text']    )

		# We need to concatenate all our texts together, and then split the result
		# into chunks of a fixed size, which we will call block_size. To do this, we
		# will use the map method again, with the option batched=True. When we use batched=True,
		# the function we pass to map() will be passed multiple inputs at once, allowing us
		# to group them into more or fewer examples than we had in the input.
		# This allows us to create our new fixed-length samples. The advantage of this
		# method is that we don't lose a whole lot of content from the dataset compared to the
		# case where we simply tokenize with a pre-defined max_length.

		def group_texts(lowerCAmelCase_    ):
				# Concatenate all texts.
				_a	: str								=				{k: sum(examples[k]		,     []    ) for k in examples.keys()}
				_a	: Optional[Any]								=				len(concatenated_examples[list(examples.keys()    )[0]]    )
				# We drop the small remainder, though you could add padding instead if the model supports it
				# In this, as in all things, we advise you to follow your heart 🫀
				_a	: str								=				(total_length // args.max_length) * args.max_length
				# Split by chunks of max_len.
				_a	: str								=				{
				    k: [t[i : i + args.max_length] for i in range(0		,     lowerCAmelCase_		,     args.max_length    )]
				    for k, t in concatenated_examples.items()
				}
				return result

		_a	: List[str]								=				dataset_tokenized.map(lowerCAmelCase_		,     batched=lowerCAmelCase_		,     batch_size=1000		,     num_proc=4    )

		_a	: Any								=				0
		_a	: List[str]								=				0
		for shard in range(0		,     len(lowerCAmelCase_    )		,     args.shard_size    ):
				_a	: Dict								=				grouped_dataset[shard : shard + args.shard_size]
				_a	: List[Any]								=				len(dataset_snapshot['input_ids']    )
				_a	: int								=				os.path.join(lowerCAmelCase_		,     f"""dataset-{shard_count}-{records_containing}.tfrecord"""    )
				_a	: int								=				get_serialized_examples(lowerCAmelCase_    )

				with tf.io.TFRecordWriter(lowerCAmelCase_    ) as out_file:
						for i in range(len(lowerCAmelCase_    )    ):
								_a	: Optional[Any]								=				serialized_examples[i]
								out_file.write(lowerCAmelCase_    )
						print('Wrote file {} containing {} records'.format(lowerCAmelCase_		,     lowerCAmelCase_    )    )

				shard_count += 1
				total_records += records_containing

		with open(f"""split-{args.split}-records-count.txt"""		,     'w'    ) as f:
				print(f"""Total {args.split} records: {total_records}"""		,     file=lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  parse_args()
							main(args)









'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, 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=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







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







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




