'''simple docstring'''


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCAmelCase	:       Any						       =	logging.getLogger(__name__)





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			argparse.ArgumentParser(
	    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''       )
	parser.add_argument(
	    '''--dataset_name'''    ,					type=UpperCamelCase__    ,					default='''wikitext'''    ,					help='''Name of the training. Explore datasets at: hf.co/datasets.'''    ,					)
	parser.add_argument(
	    '''--dataset_config'''    ,					type=UpperCamelCase__    ,					default='''wikitext-103-raw-v1'''    ,					help='''Configuration name of the dataset.'''       )
	parser.add_argument(
	    '''--tokenizer_name_or_path'''    ,					type=UpperCamelCase__    ,					default='''sayakpaul/unigram-tokenizer-wikitext'''    ,					help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''    ,					)
	parser.add_argument(
	    '''--shard_size'''    ,					type=UpperCamelCase__    ,					default=1_0_0_0    ,					help='''Number of entries to go in a single shard.'''    ,					)
	parser.add_argument('''--split'''    ,					type=UpperCamelCase__    ,					default='''train'''    ,					choices=['''train''', '''test''', '''validation''']       )
	parser.add_argument(
	    '''--limit'''    ,					default=UpperCamelCase__    ,					type=UpperCamelCase__    ,					help='''Limit the number of shards (used for debugging).'''    ,					)
	parser.add_argument(
	    '''--max_length'''    ,					type=UpperCamelCase__    ,					default=5_1_2    ,					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=UpperCamelCase__    ,					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							=			parser.parse_args()
	return args





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""



	def fn(UpperCamelCase__				:   Optional[int]       ):
		return tokenizer(examples['''text''']       )

	return fn





def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for i in range(len(tokenized_data['''input_ids''']       )       ):
		__UpperCAmelCase							=			{
		    "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							=			tf.train.Features(feature=UpperCamelCase__       )
		__UpperCAmelCase							=			tf.train.Example(features=UpperCamelCase__       )
		__UpperCAmelCase							=			example.SerializeToString()
		records.append(UpperCamelCase__       )
	return records





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	__UpperCAmelCase							=			datasets.load_dataset(args.dataset_name    ,					args.dataset_config    ,					split=args.split       )

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

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

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

	__UpperCAmelCase							=			dataset_tokenized.map(UpperCamelCase__    ,					batched=UpperCamelCase__    ,					batch_size=1_0_0_0    ,					num_proc=4       )

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

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

		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=UpperCamelCase__       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Optional[Any]						       =	parse_args()
					main(args)


'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	if velocity > c:
		raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''       )
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError('''Speed must be greater than or equal to 1!'''       )

	return velocity / c





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowerCAmelCase	:       Dict						       =	transform(29_979_245)
					print("Example of four vector: ")
					print(F"""ct' = {four_vector[0]}""")
					print(F"""x' = {four_vector[1]}""")
					print(F"""y' = {four_vector[2]}""")
					print(F"""z' = {four_vector[3]}""")

					# Substitute symbols with numerical values
					__lowerCAmelCase	:       Union[str, Any]						       =	{ct: c, x: 1, y: 1, z: 1}
					__lowerCAmelCase	:       Optional[int]						       =	[four_vector[i].subs(sub_dict) for i in range(4)]

					print(F"""\n{numerical_vector}""")
