




'''simple docstring'''



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_							:	List[str]	     =		logging.getLogger(__name__)







def       _UpperCamelCase			()-> Any:




     '''simple docstring'''





     __snake_case            =	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=10_00 ,						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=5_12 ,						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.''' ,						)

     __snake_case            =	parser.parse_args()
     return args







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> Any:




     '''simple docstring'''





     def fn(_lowerCamelCase				:  List[str]						):
          return tokenizer(examples['''text''']						)

     return fn







def       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> Any:




     '''simple docstring'''





     __snake_case            =	[]
     for i in range(len(tokenized_data['''input_ids''']						)						):
          __snake_case            =	{
              '''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]						)						),
          }
          __snake_case            =	tf.train.Features(feature=_lowerCamelCase						)
          __snake_case            =	tf.train.Example(features=_lowerCamelCase						)
          __snake_case            =	example.SerializeToString()
          records.append(_lowerCamelCase						)
     return records







def       _UpperCamelCase			(_lowerCamelCase				:  str						)-> Any:




     '''simple docstring'''





     __snake_case            =	datasets.load_dataset(args.dataset_name ,						args.dataset_config ,						split=args.split						)

     if args.limit is not None:
          __snake_case            =	min(len(_lowerCamelCase						) ,						args.limit						)
          __snake_case            =	dataset.select(range(_lowerCamelCase						)						)
          print(f'''Limiting the dataset to {args.limit} entries.'''						)

     __snake_case            =	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						)
          __snake_case            =	os.path.join(args.output_dir ,						args.split						)
          if not os.path.exists(_lowerCamelCase						):
               os.makedirs(_lowerCamelCase						)
     else:
          __snake_case            =	os.path.join(args.output_dir ,						args.split						)

     # Tokenize the whole dataset at once.
     __snake_case            =	tokenize_function(_lowerCamelCase						)
     __snake_case            =	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				:  Optional[int]						):
          # Concatenate all texts.
          __snake_case            =	{k: sum(examples[k] ,						[]						) for k in examples.keys()}
          __snake_case            =	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 🫀
          __snake_case            =	(total_length // args.max_length) * args.max_length
          # Split by chunks of max_len.
          __snake_case            =	{
              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

     __snake_case            =	dataset_tokenized.map(_lowerCamelCase ,						batched=_lowerCamelCase ,						batch_size=10_00 ,						num_proc=4						)

     __snake_case            =	0
     __snake_case            =	0
     for shard in range(0 ,						len(_lowerCamelCase						) ,						args.shard_size						):
          __snake_case            =	grouped_dataset[shard : shard + args.shard_size]
          __snake_case            =	len(dataset_snapshot['''input_ids''']						)
          __snake_case            =	os.path.join(_lowerCamelCase ,						f'''dataset-{shard_count}-{records_containing}.tfrecord'''						)
          __snake_case            =	get_serialized_examples(_lowerCamelCase						)

          with tf.io.TFRecordWriter(_lowerCamelCase						) as out_file:
               for i in range(len(_lowerCamelCase						)						):
                    __snake_case            =	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__":
     UpperCAmelCase_							:	Dict	     =		parse_args()
     main(args)












'''simple docstring'''



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_							:	Tuple	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     warnings.warn(
         '''The preprocess method is deprecated and will be removed in a future version. Please'''
         ''' use VaeImageProcessor.preprocess instead''' ,						_lowerCamelCase ,						)
     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	image[0].size
          __snake_case       ,     __snake_case            =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return mask
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE	)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

            __snake_case            =	self.scheduler.timesteps[0] + 1
            __snake_case            =	generator[0] if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 if t < t_last:
                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      # compute previous image: x_t -> x_t-1
                      __snake_case            =	self.scheduler.step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).prev_sample

                 else:
                      # compute the reverse: x_t-1 -> x_t
                      __snake_case            =	self.scheduler.undo_step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	t

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






