






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 	lowerCamelCase__				(							__UpperCAmelCase,   unittest.TestCase ):
      lowerCamelCase_							:   str											=	BartTokenizer
      lowerCamelCase_							:   List[Any]											=	BartTokenizerFast
      lowerCamelCase_							:   Any											=	True
      lowerCamelCase_							:   List[Any]											=	filter_roberta_detectors






      def      UpperCAmelCase_     (self					:    Dict		)       ->   str:


       """simple docstring"""






       super().setUp()
       lowerCamelCase_							:    str							=     [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       lowerCamelCase_							:    str							=     dict(zip(_snake_case		,		range(len(_snake_case		)		)		)		)
       lowerCamelCase_							:    Union[str, Any]							=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       lowerCamelCase_							:    Dict							=     {'unk_token': '<unk>'}

       lowerCamelCase_							:    Union[str, Any]							=     os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['vocab_file']		)
       lowerCamelCase_							:    str							=     os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['merges_file']		)
       with open(self.vocab_file		,		'w'		,		encoding='utf-8'		) as fp:
        fp.write(json.dumps(_snake_case		) + '\n'		)
       with open(self.merges_file		,		'w'		,		encoding='utf-8'		) as fp:
        fp.write('\n'.join(_snake_case		)		)






      def      UpperCAmelCase_     (self					:    str		,		**_snake_case					:    List[str]		)       ->   Union[str, Any]:


       """simple docstring"""






       kwargs.update(self.special_tokens_map		)
       return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**_snake_case		)






      def      UpperCAmelCase_     (self					:    Optional[Any]		,		**_snake_case					:    Optional[Any]		)       ->   Any:


       """simple docstring"""






       kwargs.update(self.special_tokens_map		)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**_snake_case		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		,		_snake_case					:    int		)       ->   Dict:


       """simple docstring"""






       return "lower newer", "lower newer"






      @cached_property
      def      UpperCAmelCase_     (self					:    Any		)       ->   Union[str, Any]:


       """simple docstring"""






       return BartTokenizer.from_pretrained('facebook/bart-large'		)






      @cached_property
      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   Any:


       """simple docstring"""






       return BartTokenizerFast.from_pretrained('facebook/bart-large'		)






      @require_torch
      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       lowerCamelCase_							:    Dict							=     [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCamelCase_							:    Union[str, Any]							=     tokenizer(_snake_case		,		max_length=len(_snake_case		)		,		padding=_snake_case		,		return_tensors='pt'		)
        self.assertIsInstance(_snake_case		,		_snake_case		)

        self.assertEqual((2, 9)		,		batch.input_ids.shape		)
        self.assertEqual((2, 9)		,		batch.attention_mask.shape		)
        lowerCamelCase_							:    Tuple							=     batch.input_ids.tolist()[0]
        self.assertListEqual(_snake_case		,		_snake_case		)
        # Test that special tokens are reset






      @require_torch
      def      UpperCAmelCase_     (self					:    int		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCamelCase_							:    Any							=     tokenizer(_snake_case		,		padding=_snake_case		,		return_tensors='pt'		)
        # check if input_ids are returned and no labels
        self.assertIn('input_ids'		,		_snake_case		)
        self.assertIn('attention_mask'		,		_snake_case		)
        self.assertNotIn('labels'		,		_snake_case		)
        self.assertNotIn('decoder_attention_mask'		,		_snake_case		)






      @require_torch
      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     [
           'Summary of the text.',
           'Another summary.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCamelCase_							:    Optional[Any]							=     tokenizer(text_target=_snake_case		,		max_length=32		,		padding='max_length'		,		return_tensors='pt'		)
        self.assertEqual(32		,		targets['input_ids'].shape[1]		)






      @require_torch
      def      UpperCAmelCase_     (self					:    List[Any]		)       ->   Optional[int]:


       """simple docstring"""






       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCamelCase_							:    Dict							=     tokenizer(
            ['I am a small frog' * 1024, 'I am a small frog']		,		padding=_snake_case		,		truncation=_snake_case		,		return_tensors='pt'		)
        self.assertIsInstance(_snake_case		,		_snake_case		)
        self.assertEqual(batch.input_ids.shape		,		(2, 1024)		)






      @require_torch
      def      UpperCAmelCase_     (self					:    int		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     ['A long paragraph for summarization.']
       lowerCamelCase_							:    Dict							=     [
           'Summary of the text.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCamelCase_							:    List[Any]							=     tokenizer(_snake_case		,		return_tensors='pt'		)
        lowerCamelCase_							:    str							=     tokenizer(text_target=_snake_case		,		return_tensors='pt'		)
        lowerCamelCase_							:    List[str]							=     inputs['input_ids']
        lowerCamelCase_							:    Union[str, Any]							=     targets['input_ids']
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Dict:


       """simple docstring"""






       pass






      def      UpperCAmelCase_     (self					:    str		)       ->   Tuple:


       """simple docstring"""






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
         lowerCamelCase_							:    int							=     self.rust_tokenizer_class.from_pretrained(_snake_case		,		**_snake_case		)
         lowerCamelCase_							:    Any							=     self.tokenizer_class.from_pretrained(_snake_case		,		**_snake_case		)
         lowerCamelCase_							:    Optional[int]							=     'A, <mask> AllenNLP sentence.'
         lowerCamelCase_							:    Optional[Any]							=     tokenizer_r.encode_plus(_snake_case		,		add_special_tokens=_snake_case		,		return_token_type_ids=_snake_case		)
         lowerCamelCase_							:    Dict							=     tokenizer_p.encode_plus(_snake_case		,		add_special_tokens=_snake_case		,		return_token_type_ids=_snake_case		)

         # token_type_ids should put 0 everywhere
         self.assertEqual(sum(tokens_r['token_type_ids']		)		,		sum(tokens_p['token_type_ids']		)		)

         # attention_mask should put 1 everywhere, so sum over length should be 1
         self.assertEqual(
             sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)		,		sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)		,		)

         lowerCamelCase_							:    Union[str, Any]							=     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
         lowerCamelCase_							:    Tuple							=     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)

         # Rust correctly handles the space before the mask while python doesnt
         self.assertSequenceEqual(tokens_p['input_ids']		,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
         self.assertSequenceEqual(tokens_r['input_ids']		,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

         self.assertSequenceEqual(
             _snake_case		,		['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
         self.assertSequenceEqual(
             _snake_case		,		['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)







import numpy as np
import requests


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''       )
       _UpperCamelCase														=    response.text.splitlines()
       _UpperCamelCase														=    []
       for item in lines:
              _UpperCamelCase														=    item.split(''','''       )
              data.append(lowerCAmelCase       )
       data.pop(0       )  # This is for removing the labels from the list
       _UpperCamelCase														=    np.matrix(lowerCAmelCase       )
       return dataset


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta - (alpha / n) * sum_grad
       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

       _UpperCamelCase														=    data_x.shape[1]
       _UpperCamelCase														=    data_x.shape[0] - 1

       _UpperCamelCase														=    np.zeros((1, no_features)       )

       for i in range(0     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    run_steep_gradient_descent(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              _UpperCamelCase														=    sum_of_square_error(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              print(f'''At Iteration {i + 1} - Error is {error:.5f}'''       )

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

       _UpperCamelCase														=    data.shape[0]
       _UpperCamelCase														=    np.c_[np.ones(lowerCAmelCase       ), data[:, :-1]].astype(lowerCAmelCase       )
       _UpperCamelCase														=    data[:, -1].astype(lowerCAmelCase       )

       _UpperCamelCase														=    run_linear_regression(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta.shape[1]
       print('''Resultant Feature vector : '''       )
       for i in range(0     ,							lowerCAmelCase       ):
              print(f'''{theta[0, i]:.5f}'''       )


if __name__ == "__main__":
       main()






