


from __future__ import annotations

from typing import Any




def SCREAMING_SNAKE_CASE_  (				*__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	if all(_check_not_integer(__lowerCamelCase  ) for m in matrix_s  ):
		for i in matrix_s[1:]:
			_verify_matrix_sizes(matrix_s[0]  ,			__lowerCamelCase  )
		return [[sum(__lowerCamelCase  ) for t in zip(*__lowerCamelCase  )] for m in zip(*__lowerCamelCase  )]
	raise TypeError("Expected a matrix, got int/list instead"  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	if (
	    _check_not_integer(__lowerCamelCase  )
	    and _check_not_integer(__lowerCamelCase  )
	    and _verify_matrix_sizes(__lowerCamelCase  ,			__lowerCamelCase  )
	):
		return [[i - j for i, j in zip(*__lowerCamelCase  )] for m in zip(__lowerCamelCase  ,			__lowerCamelCase  )]
	raise TypeError("Expected a matrix, got int/list instead"  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		int | float  ):







	'''simple docstring'''



	return [[x * n for x in row] for row in matrix]




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	if _check_not_integer(__lowerCamelCase  ) and _check_not_integer(__lowerCamelCase  ):
		lowercase_ ,	lowercase_			 = _verify_matrix_sizes(__lowerCamelCase  ,			__lowerCamelCase  )

	if cols[0] != rows[1]:
		lowercase_			 = (
		    "Cannot multiply matrix of dimensions "
		    F'({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'
		)
		raise ValueError(__lowerCamelCase  )
	return [
	    [sum(m * n for m, n in zip(__lowerCamelCase  ,			__lowerCamelCase  )  ) for j in zip(*__lowerCamelCase  )] for i in matrix_a
	]




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ):







	'''simple docstring'''



	lowercase_			 = int(__lowerCamelCase  )
	return [[int(row == column  ) for column in range(__lowerCamelCase  )] for row in range(__lowerCamelCase  )]




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		bool = True  ):







	'''simple docstring'''



	if _check_not_integer(__lowerCamelCase  ):
		if return_map:
			return map(__lowerCamelCase  ,			zip(*__lowerCamelCase  )  )
		else:
			return list(map(__lowerCamelCase  ,			zip(*__lowerCamelCase  )  )  )
	raise TypeError("Expected a matrix, got int/list instead"  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		int  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



	lowercase_			 = matrix[:row] + matrix[row + 1 :]
	return [row[:column] + row[column + 1 :] for row in minor]




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) == 1:
		return matrix[0][0]

	return sum(
	    x * determinant(minor(__lowerCamelCase  ,			0  ,			__lowerCamelCase  )  ) * (-1) ** i
	    for i, x in enumerate(matrix[0]  )  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	lowercase_			 = determinant(__lowerCamelCase  )
	if det == 0:
		return None

	lowercase_			 = [
	    [determinant(minor(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  )  ) for j in range(len(__lowerCamelCase  )  )]
	    for i in range(len(__lowerCamelCase  )  )
	]

	lowercase_			 = [
	    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]  )]
	    for row in range(len(__lowerCamelCase  )  )
	]
	lowercase_			 = list(transpose(__lowerCamelCase  )  )
	return scalar_multiply(__lowerCamelCase  ,			1 / det  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	return not isinstance(__lowerCamelCase  ,			__lowerCamelCase  ) and not isinstance(matrix[0]  ,			__lowerCamelCase  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	return len(__lowerCamelCase  ), len(matrix[0]  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[list[int]]  ,			__lowerCamelCase:		list[list[int]]  ):







	'''simple docstring'''



	lowercase_			 = _shape(__lowerCamelCase  ) + _shape(__lowerCamelCase  )
	if shape[0] != shape[3] or shape[1] != shape[2]:
		lowercase_			 = (
		    "operands could not be broadcast together with shape "
		    F'({shape[0], shape[1]}), ({shape[2], shape[3]})'
		)
		raise ValueError(__lowerCamelCase  )
	return (shape[0], shape[2]), (shape[1], shape[3])




def SCREAMING_SNAKE_CASE_  (				):







	'''simple docstring'''



	lowercase_			 = [[12, 10], [3, 9]]
	lowercase_			 = [[3, 4], [7, 4]]
	lowercase_			 = [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
	lowercase_			 = [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
	print(F'Add Operation, {add(__lowerCamelCase  ,			__lowerCamelCase  ) = } \n'  )
	print(
	    F'Multiply Operation, {multiply(__lowerCamelCase  ,			__lowerCamelCase  ) = } \n'  ,			)
	print(F'Identity: {identity(5  )}\n'  )
	print(F'Minor of {matrix_c} = {minor(__lowerCamelCase  ,			1  ,			2  )} \n'  )
	print(F'Determinant of {matrix_b} = {determinant(__lowerCamelCase  )} \n'  )
	print(F'Inverse of {matrix_d} = {inverse(__lowerCamelCase  )}\n'  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()





import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5E-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							SCREAMING_SNAKE_CASE__       			=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							SCREAMING_SNAKE_CASE__       			=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							SCREAMING_SNAKE_CASE__       			=				[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(f"""train_runtime = {end_train_time}""")
							for key, value in train_results.history.items():
														logger.info(f"""  {key} = {value}""")

