

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 						a_			(				lowerCamelCase_					,     unittest.TestCase	):



      """simple docstring"""







      __UpperCAmelCase     						= BartTokenizer
      __UpperCAmelCase     						= BartTokenizerFast
      __UpperCAmelCase     						= True
      __UpperCAmelCase     						= filter_roberta_detectors
      def 			_lowerCAmelCase	( self	:				Dict							):
           super().setUp()
           SCREAMING_SNAKE_CASE 		=[
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           SCREAMING_SNAKE_CASE 		=dict(zip(snake_case     ,range(len(snake_case							)							)							)							)
           SCREAMING_SNAKE_CASE 		=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           SCREAMING_SNAKE_CASE 		={'unk_token': '<unk>'}

           SCREAMING_SNAKE_CASE 		=os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['vocab_file']							)
           SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				Tuple     ,**snake_case	:				Any							):
           kwargs.update(self.special_tokens_map							)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**snake_case							)
      def 			_lowerCAmelCase	( self	:				Dict     ,**snake_case	:				List[Any]							):
           kwargs.update(self.special_tokens_map							)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,**snake_case							)
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				Optional[Any]							):
           return "lower newer", "lower newer"
      @cached_property
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           return BartTokenizer.from_pretrained('facebook/bart-large'							)
      @cached_property
      def 			_lowerCAmelCase	( self	:				List[Any]							):
           return BartTokenizerFast.from_pretrained('facebook/bart-large'							)
      @require_torch
      def 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=['A long paragraph for summarization.', 'Another paragraph for summarization.']
           SCREAMING_SNAKE_CASE 		=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                SCREAMING_SNAKE_CASE 		=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							)
                SCREAMING_SNAKE_CASE 		=batch.input_ids.tolist()[0]
                self.assertListEqual(snake_case     ,snake_case							)
                # Test that special tokens are reset
      @require_torch
      def 			_lowerCAmelCase	( self	:				List[Any]							):
           SCREAMING_SNAKE_CASE 		=['A long paragraph for summarization.', 'Another paragraph for summarization.']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=[
               'Summary of the text.',
               'Another summary.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				Any							):
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				Dict							):
           SCREAMING_SNAKE_CASE 		=['A long paragraph for summarization.']
           SCREAMING_SNAKE_CASE 		=[
               'Summary of the text.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                SCREAMING_SNAKE_CASE 		=tokenizer(snake_case     ,return_tensors='pt'							)
                SCREAMING_SNAKE_CASE 		=tokenizer(text_target=snake_case     ,return_tensors='pt'							)
                SCREAMING_SNAKE_CASE 		=inputs['input_ids']
                SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				Tuple							):
           pass


      def 			_lowerCAmelCase	( self	:				Tuple							):
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'							):
                     SCREAMING_SNAKE_CASE 		=self.rust_tokenizer_class.from_pretrained(snake_case     ,**snake_case							)
                     SCREAMING_SNAKE_CASE 		=self.tokenizer_class.from_pretrained(snake_case     ,**snake_case							)
                     SCREAMING_SNAKE_CASE 		='A, <mask> AllenNLP sentence.'
                     SCREAMING_SNAKE_CASE 		=tokenizer_r.encode_plus(snake_case     ,add_special_tokens=snake_case     ,return_token_type_ids=snake_case							)
                     SCREAMING_SNAKE_CASE 		=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']							)     ,)

                     SCREAMING_SNAKE_CASE 		=tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
                     SCREAMING_SNAKE_CASE 		=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, 50264, 3823, 487, 21992, 3645, 4, 2]							)
                     self.assertSequenceEqual(tokens_r['input_ids']     ,[0, 250, 6, 50264, 3823, 487, 21992, 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







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=np.exp(lowerCAmelCase_     )

     # Add up the all the exponentials
     SCREAMING_SNAKE_CASE 		=np.sum(lowerCAmelCase_     )

     # Divide every exponent by the sum of all exponents
     SCREAMING_SNAKE_CASE 		=exponent_vector / sum_of_exponents

     return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

