
'''simple docstring'''







import numpy as np
import requests

def 				_snake_case (		)       ->       str:
				__a       :     Tuple								=	requests.get(
				    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
				    """master/Week1/ADRvsRating.csv"""						)
				__a       :     List[Any]								=	response.text.splitlines()
				__a       :     List[Any]								=	[]
				for item in lines:
								__a       :     Any								=	item.split(""","""						)
								data.append(lowercase						)
				data.pop(0						)  # This is for removing the labels from the list
				__a       :     Optional[int]								=	np.matrix(lowercase						)
				return dataset

def 				_snake_case (		lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase						)       ->       Any:
				__a       :     Union[str, Any]								=	len_data

				__a       :     Any								=	np.dot(lowercase			,    data_x.transpose()						)
				prod -= data_y.transpose()
				__a       :     List[str]								=	np.dot(lowercase			,    lowercase						)
				__a       :     Optional[Any]								=	theta - (alpha / n) * sum_grad
				return theta

def 				_snake_case (		lowercase			,    lowercase			,    lowercase			,    lowercase						)       ->       Optional[Any]:
				__a       :     Dict								=	np.dot(lowercase			,    data_x.transpose()						)
				prod -= data_y.transpose()
				__a       :     List[str]								=	np.sum(np.square(lowercase						)						)
				__a       :     List[str]								=	sum_elem / (2 * len_data)
				return error

def 				_snake_case (		lowercase			,    lowercase						)       ->       int:
				__a       :     List[Any]								=	1_0_0_0_0_0
				__a       :     Dict								=	0.0_0_0_1_5_5_0

				__a       :     Any								=	data_x.shape[1]
				__a       :     Dict								=	data_x.shape[0] - 1

				__a       :     Any								=	np.zeros((1, no_features)						)

				for i in range(0			,    lowercase						):
								__a       :     Union[str, Any]								=	run_steep_gradient_descent(lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase						)
								__a       :     Dict								=	sum_of_square_error(lowercase			,    lowercase			,    lowercase			,    lowercase						)
								print(F"""At Iteration {i + 1} - Error is {error:.5f}"""						)

				return theta

def 				_snake_case (		lowercase			,    lowercase						)       ->       Dict:
				__a       :     Union[str, Any]								=	sum(abs(y - predicted_y[i]						) for i, y in enumerate(lowercase						)						)
				return total / len(lowercase						)

def 				_snake_case (		)       ->       int:
				__a       :     Union[str, Any]								=	collect_dataset()

				__a       :     Tuple								=	data.shape[0]
				__a       :     Tuple								=	np.c_[np.ones(lowercase						), data[:, :-1]].astype(lowercase						)
				__a       :     int								=	data[:, -1].astype(lowercase						)

				__a       :     int								=	run_linear_regression(lowercase			,    lowercase						)
				__a       :     Optional[int]								=	theta.shape[1]
				print("""Resultant Feature vector : """						)
				for i in range(0			,    lowercase						):
								print(F"""{theta[0, i]:.5f}"""						)


if __name__ == "__main__":
					main()

'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """こん""",
					    """こんに""",
					    """にちは""",
					    """ばんは""",
					    """世界,㔺界""",
					    """、""",
					    """。""",
					    """<BR>""",
					    """<SP>""",
					    """<TAB>""",
					    """<URL>""",
					    """<EMAIL>""",
					    """<TEL>""",
					    """<DATE>""",
					    """<PRICE>""",
					    """<BLOCK>""",
					    """<KIGOU>""",
					    """<U2000U2BFF>""",
					    """<|emoji1|>""",
					    """<unk>""",
					    """<|startoftext|>""",
					    """<|endoftext|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""emoji_file"""]			)
					with open(self.vocab_file  ,    """w"""  ,    encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
					with open(self.emoji_file  ,    """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__UpperCamelCase			)			)
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass