from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase_           =				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def    lowerCAmelCase_  (		lowercase:				Any		)     ->					Optional[Any]:







 '''simple docstring'''
 if isinstance(lowercase	,       torch.Tensor		):
  return image
 elif isinstance(lowercase	,       PIL.Image.Image		):
  _UpperCamelCase:  Any    						=			[image]

 _UpperCamelCase:  Tuple    						=			[trans(img.convert('''RGB'''		)		) for img in image]
 _UpperCamelCase:  List[Any]    						=			torch.stack(lowercase		)
 return image





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Optional[Any]		,	_lowercase       :	Optional[int]		,	_lowercase       :	Dict				):



    """simple docstring"""



    super().__init__()

    # make sure scheduler can always be converted to DDIM
    _UpperCamelCase:  List[Any]    						=			DDIMScheduler.from_config(scheduler.config				)

    self.register_modules(unet=_lowercase		,	scheduler=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	_lowercase       :	str				):



    """simple docstring"""



    if strength < 0 or strength > 1:
     raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""				)



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	Any		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			min(int(num_inference_steps * strength				)		,	_lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			max(num_inference_steps - init_timestep		,	0				)
    _UpperCamelCase:  List[str]    						=			self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start



   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	_lowercase       :	Dict		,	_lowercase       :	str		,	_lowercase       :	Optional[Any]		,	_lowercase       :	int		,	_lowercase       :	Dict		,	_lowercase       :	List[Any]=None				):



    """simple docstring"""



    if not isinstance(_lowercase		,	(torch.Tensor, PIL.Image.Image, list)				):
     raise ValueError(
         f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase				)}"""				)

    _UpperCamelCase:  Dict    						=			image.to(device=_lowercase		,	dtype=_lowercase				)

    if isinstance(_lowercase		,	_lowercase				) and len(_lowercase				) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

    _UpperCamelCase:  Optional[int]    						=			init_latents.shape
    _UpperCamelCase:  Dict    						=			randn_tensor(_lowercase		,	generator=_lowercase		,	device=_lowercase		,	dtype=_lowercase				)

    # get latents
    print('''add noise to latents at timestep'''		,	_lowercase				)
    _UpperCamelCase:  Any    						=			self.scheduler.add_noise(_lowercase		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			init_latents

    return latents



   @torch.no_grad()
   def __call__(						self       :	Tuple		,	_lowercase       :	Union[torch.FloatTensor, PIL.Image.Image] = None		,	_lowercase       :	float = 0.8		,	_lowercase       :	int = 1		,	_lowercase       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	_lowercase       :	float = 0.0		,	_lowercase       :	int = 50		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[str] = "pil"		,	_lowercase       :	bool = True		,	):



    """simple docstring"""



    self.check_inputs(_lowercase				)

    # 2. Preprocess image
    _UpperCamelCase:  Optional[int]    						=			preprocess(_lowercase				)

    # 3. set timesteps
    self.scheduler.set_timesteps(_lowercase		,	device=self.device				)
    _UpperCamelCase						,				_UpperCamelCase:  int    						=			self.get_timesteps(_lowercase		,	_lowercase		,	self.device				)
    _UpperCamelCase:  int    						=			timesteps[:1].repeat(_lowercase				)

    # 4. Prepare latent variables
    _UpperCamelCase:  str    						=			self.prepare_latents(_lowercase		,	_lowercase		,	_lowercase		,	self.unet.dtype		,	self.device		,	_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			latents

    # 5. Denoising loop
    for t in self.progress_bar(_lowercase				):
     # 1. predict noise model_output
     _UpperCamelCase:  str    						=			self.unet(_lowercase		,	_lowercase				).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     _UpperCamelCase:  Tuple    						=			self.scheduler.step(
         _lowercase		,	_lowercase		,	_lowercase		,	eta=_lowercase		,	use_clipped_model_output=_lowercase		,	generator=_lowercase		,	).prev_sample

    _UpperCamelCase:  str    						=			(image / 2 + 0.5).clamp(0		,	1				)
    _UpperCamelCase:  Optional[Any]    						=			image.cpu().permute(0		,	2		,	3		,	1				).numpy()
    if output_type == "pil":
     _UpperCamelCase:  Optional[int]    						=			self.numpy_to_pil(_lowercase				)

    if not return_dict:
     return (image, latent_timestep.item())

    return ImagePipelineOutput(images=_lowercase				)
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_           =				logging.getLogger(__name__)




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
 parser.add_argument(
     '''--dataset_name'''	,       type=lowercase	,       default='''wikitext'''	,       help='''Name of the training. Explore datasets at: hf.co/datasets.'''	,       )
 parser.add_argument(
     '''--dataset_config'''	,       type=lowercase	,       default='''wikitext-103-raw-v1'''	,       help='''Configuration name of the dataset.'''		)
 parser.add_argument(
     '''--tokenizer_name_or_path'''	,       type=lowercase	,       default='''sayakpaul/unigram-tokenizer-wikitext'''	,       help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''	,       )
 parser.add_argument(
     '''--shard_size'''	,       type=lowercase	,       default=1_000	,       help='''Number of entries to go in a single shard.'''	,       )
 parser.add_argument('''--split'''	,       type=lowercase	,       default='''train'''	,       choices=['''train''', '''test''', '''validation''']		)
 parser.add_argument(
     '''--limit'''	,       default=lowercase	,       type=lowercase	,       help='''Limit the number of shards (used for debugging).'''	,       )
 parser.add_argument(
     '''--max_length'''	,       type=lowercase	,       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=lowercase	,       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.'''	,       )

 _UpperCamelCase:  str    						=			parser.parse_args()
 return args




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 def fn(lowercase:				List[Any]		):
  return tokenizer(examples['''text''']		)

 return fn




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:  Tuple    						=			{
      '''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]		)		),
  }
  _UpperCamelCase:  Optional[int]    						=			tf.train.Features(feature=lowercase		)
  _UpperCamelCase:  Optional[int]    						=			tf.train.Example(features=lowercase		)
  _UpperCamelCase:  List[Any]    						=			example.SerializeToString()
  records.append(lowercase		)
 return records




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			datasets.load_dataset(args.dataset_name	,       args.dataset_config	,       split=args.split		)

 if args.limit is not None:
  _UpperCamelCase:  str    						=			min(len(lowercase		)	,       args.limit		)
  _UpperCamelCase:  Optional[Any]    						=			dataset.select(range(lowercase		)		)
  print(F"""Limiting the dataset to {args.limit} entries."""		)

 _UpperCamelCase:  List[Any]    						=			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		)
  _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       args.split		)
  if not os.path.exists(lowercase		):
   os.makedirs(lowercase		)
 else:
  _UpperCamelCase:  str    						=			os.path.join(args.output_dir	,       args.split		)

 # Tokenize the whole dataset at once.
 _UpperCamelCase:  Union[str, Any]    						=			tokenize_function(lowercase		)
 _UpperCamelCase:  List[Any]    						=			dataset.map(lowercase	,       batched=lowercase	,       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(lowercase:				int		):
  # Concatenate all texts.
  _UpperCamelCase:  List[str]    						=			{k: sum(examples[k]	,       []		) for k in examples.keys()}
  _UpperCamelCase:  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 🫀
  _UpperCamelCase:  Optional[int]    						=			(total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  _UpperCamelCase:  Tuple    						=			{
      k: [t[i : i + args.max_length] for i in range(0	,       lowercase	,       args.max_length		)]
      for k, t in concatenated_examples.items()
  }
  return result

 _UpperCamelCase:  Dict    						=			dataset_tokenized.map(lowercase	,       batched=lowercase	,       batch_size=1_000	,       num_proc=4		)

 _UpperCamelCase:  List[str]    						=			0
 _UpperCamelCase:  Tuple    						=			0
 for shard in range(0	,       len(lowercase		)	,       args.shard_size		):
  _UpperCamelCase:  List[str]    						=			grouped_dataset[shard : shard + args.shard_size]
  _UpperCamelCase:  Optional[Any]    						=			len(dataset_snapshot['''input_ids''']		)
  _UpperCamelCase:  str    						=			os.path.join(lowercase	,       F"""dataset-{shard_count}-{records_containing}.tfrecord"""		)
  _UpperCamelCase:  Any    						=			get_serialized_examples(lowercase		)

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

  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=lowercase		)


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