




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=sys.maxsize ):
							SCREAMING_SNAKE_CASE_   : str					  =      'bilinear'
							SCREAMING_SNAKE_CASE_   : List[str]					  =      max_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      short_edge_length






				def __call__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      []
							for img in imgs:
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      img.shape[:2]
										# later: provide list and randomly choose index for resize
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      np.random.randint(self.short_edge_length[0]      ,self.short_edge_length[1] + 1 )
										if size == 0:
													return img
										SCREAMING_SNAKE_CASE_   : str					  =      size * 1.0 / min(snake_case__      ,snake_case__ )
										if h < w:
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      size, scale * w
										else:
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      scale * h, size
										if max(snake_case__      ,snake_case__ ) > self.max_size:
													SCREAMING_SNAKE_CASE_   : Tuple					  =      self.max_size * 1.0 / max(snake_case__      ,snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      newh * scale
													SCREAMING_SNAKE_CASE_   : Tuple					  =      neww * scale
										SCREAMING_SNAKE_CASE_   : List[str]					  =      int(neww + 0.5 )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      int(newh + 0.5 )

										if img.dtype == np.uinta:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      Image.fromarray(snake_case__ )
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      pil_image.resize((neww, newh)      ,PILImageResampling.BILINEAR )
													SCREAMING_SNAKE_CASE_   : Dict					  =      np.asarray(snake_case__ )
										else:
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      img.permute(2      ,0      ,1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      nn.functional.interpolate(
													    snake_case__      ,(newh, neww)      ,mode=self.interp_method      ,align_corners=snake_case__ ).squeeze(0 )
										img_augs.append(snake_case__ )

							return img_augs







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,cfg.INPUT.MAX_SIZE_TEST )
							SCREAMING_SNAKE_CASE_   : str					  =      cfg.INPUT.FORMAT
							SCREAMING_SNAKE_CASE_   : Dict					  =      cfg.SIZE_DIVISIBILITY
							SCREAMING_SNAKE_CASE_   : Dict					  =      cfg.PAD_VALUE
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      cfg.INPUT.MAX_SIZE_TEST
							SCREAMING_SNAKE_CASE_   : str					  =      cfg.MODEL.DEVICE
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )      ,1      ,1 )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )      ,1      ,1 )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      lambda snake_case__ : (x - self.pixel_mean) / self.pixel_std






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      tuple(max(snake_case__ ) for s in zip(*[img.shape for img in images] ) )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [im.shape[-2:] for im in images]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [
							    nn.functional.pad(
							        snake_case__      ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,value=self.pad_value      ,)
							    for size, im in zip(snake_case__      ,snake_case__ )
							]

							return torch.stack(snake_case__ ), torch.tensor(snake_case__ )






				def __call__(			self      ,snake_case__      ,snake_case__=False ):
							with torch.no_grad():
										if not isinstance(snake_case__      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      [images]
										if single_image:
													assert len(snake_case__ ) == 1
										for i in range(len(snake_case__ ) ):
													if isinstance(images[i]      ,torch.Tensor ):
																images.insert(snake_case__      ,images.pop(snake_case__ ).to(self.device ).float() )
													elif not isinstance(images[i]      ,torch.Tensor ):
																images.insert(
																    snake_case__      ,torch.as_tensor(img_tensorize(images.pop(snake_case__ )      ,input_format=self.input_format ) )
																    .to(self.device )
																    .float()      ,)
            # resize smallest edge
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.tensor([im.shape[:2] for im in images] )
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.aug(snake_case__ )
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										SCREAMING_SNAKE_CASE_   : Any					  =      [self.normalizer(snake_case__ ) for x in images]
										# now pad them to do the following operations
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.pad(snake_case__ )
										# Normalize

										if self.size_divisibility > 0:
													raise NotImplementedError()
										# pad
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.true_divide(snake_case__      ,snake_case__ )
										if single_image:
													return images[0], sizes[0], scales_yx[0]
										else:
													return images, sizes, scales_yx





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     str  )       ->  Any:







			"""simple docstring"""





			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Tuple[int, int]  )       ->  List[Any]:







			"""simple docstring"""





			assert torch.isfinite(lowerCamelCase_  ).all(), "Box tensor contains infinite or NaN!"
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      box_size
			tensor[:, 0].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 1].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 2].clamp_(min=0  ,							max=lowerCamelCase_  )
			tensor[:, 3].clamp_(min=0  ,							max=lowerCamelCase_  )







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase   :    Any     =		logging.getLogger(__name__)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  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."""  ,							)

			lowerCamelCase    		=  parser.parse_args()
			return args






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			def fn(lowerCamelCase__		:						Any      ):
						return tokenizer(examples["""text"""]      )

			return fn






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




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

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

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

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

			lowerCamelCase    		=  dataset_tokenized.map(lowerCamelCase__  ,							batched=lowerCamelCase__  ,							batch_size=1000  ,							num_proc=4      )

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

						with tf.io.TFRecordWriter(lowerCamelCase__      ) as out_file:
									for i in range(len(lowerCamelCase__      )      ):
												lowerCamelCase    		=  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   :    Optional[int]     =		parse_args()
		main(args)

