
"""simple docstring"""


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowercase				 =					logging.getLogger(__name__)






def    _snake_case					(		):
	A     = argparse.ArgumentParser(
	    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'  )
	parser.add_argument(
	    '--dataset_name'						,   type=snake_case__						,   default='wikitext'						,   help='Name of the training. Explore datasets at: hf.co/datasets.'						,   )
	parser.add_argument(
	    '--dataset_config'						,   type=snake_case__						,   default='wikitext-103-raw-v1'						,   help='Configuration name of the dataset.'  )
	parser.add_argument(
	    '--tokenizer_name_or_path'						,   type=snake_case__						,   default='sayakpaul/unigram-tokenizer-wikitext'						,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'						,   )
	parser.add_argument(
	    '--shard_size'						,   type=snake_case__						,   default=1000						,   help='Number of entries to go in a single shard.'						,   )
	parser.add_argument('--split'						,   type=snake_case__						,   default='train'						,   choices=['train', 'test', 'validation']  )
	parser.add_argument(
	    '--limit'						,   default=snake_case__						,   type=snake_case__						,   help='Limit the number of shards (used for debugging).'						,   )
	parser.add_argument(
	    '--max_length'						,   type=snake_case__						,   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=snake_case__						,   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     = parser.parse_args()
	return args






def    _snake_case					(		snake_case__ :  Optional[Any]  ):




	def fn(snake_case__ :  Union[str, Any]  ):
		return tokenizer(examples['text']  )

	return fn






def    _snake_case					(		snake_case__ :  Optional[Any]  ):
	A     = []
	for i in range(len(tokenized_data['input_ids']  )  ):
		A     = {
		    '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     = tf.train.Features(feature=snake_case__  )
		A     = tf.train.Example(features=snake_case__  )
		A     = example.SerializeToString()
		records.append(snake_case__  )
	return records






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = datasets.load_dataset(args.dataset_name						,   args.dataset_config						,   split=args.split  )

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

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

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

	A     = dataset_tokenized.map(snake_case__						,   batched=snake_case__						,   batch_size=1000						,   num_proc=4  )

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

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

		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=snake_case__  )


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

"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while row < size and col < size:
        # pivoting
        UpperCAmelCase          =       max((abs(augmented[rowa][col]			), rowa) for rowa in range(lowerCAmelCase			,					lowerCAmelCase			)			)[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            UpperCAmelCase      ,				UpperCAmelCase          =       augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,					lowerCAmelCase			):
            UpperCAmelCase          =       augmented[rowa][col] / augmented[row][col]
            UpperCAmelCase          =       0
            for cola in range(col + 1			,					size + 1			):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1			,					lowerCAmelCase			):
        for row in range(lowerCAmelCase			):
            UpperCAmelCase          =       augmented[row][col] / augmented[col][col]
            for cola in range(lowerCAmelCase			,					size + 1			):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row]			,					10			)] for row in range(lowerCAmelCase			)
    ]




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for x_val, y_val in enumerate(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       (x_val + 1) ** (size - col - 1)
        UpperCAmelCase          =       y_val

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

    def interpolated_func(lowerCAmelCase					:       int			) -> int:
        return sum(
            round(coeffs[x_val][0]			) * (var ** (size - x_val - 1))
            for x_val in range(lowerCAmelCase			)			)

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

    UpperCAmelCase          =       [
        interpolate(data_points[:max_coeff]			) for max_coeff in range(1			,					order + 1			)
    ]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


if __name__ == "__main__":
   print(F'{solution() = }')



