





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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






@require_tokenizers
class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  LEDTokenizer
       SCREAMING_SNAKE_CASE_       :				Dict     						=  LEDTokenizerFast
       SCREAMING_SNAKE_CASE_       :				str     						=  True




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






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

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




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             return "lower newer", "lower newer"




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





             """simple docstring"""






             return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''							)




       @cached_property
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''							)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             __SCREAMING_SNAKE_CASE       :str		    =  [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer(SCREAMING_SNAKE_CASE__		,max_length=len(SCREAMING_SNAKE_CASE__							)		,padding=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   self.assertEqual((2, 9)		,batch.input_ids.shape							)
                   self.assertEqual((2, 9)		,batch.attention_mask.shape							)
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  batch.input_ids.tolist()[0]
                   self.assertListEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer(SCREAMING_SNAKE_CASE__		,padding=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)
                   self.assertIn('''input_ids'''		,SCREAMING_SNAKE_CASE__							)
                   self.assertIn('''attention_mask'''		,SCREAMING_SNAKE_CASE__							)
                   self.assertNotIn('''labels'''		,SCREAMING_SNAKE_CASE__							)
                   self.assertNotIn('''decoder_attention_mask'''		,SCREAMING_SNAKE_CASE__							)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






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




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tokenizer(
                       ['''I am a small frog''' * 10_24, '''I am a small frog''']		,padding=SCREAMING_SNAKE_CASE__		,truncation=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   self.assertEqual(batch.input_ids.shape		,(2, 51_22)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  ['''A long paragraph for summarization.''']
             __SCREAMING_SNAKE_CASE       :List[str]		    =  [
                 '''Summary of the text.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer(text_target=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  inputs['''input_ids''']
                   __SCREAMING_SNAKE_CASE       :Dict		    =  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()							)




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ['''Summary of the text.''', '''Another summary.''']
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer(SCREAMING_SNAKE_CASE__		,padding=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [[0] * len(SCREAMING_SNAKE_CASE__							) for x in encoded_output['''input_ids''']]
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.pad(SCREAMING_SNAKE_CASE__							)
                   self.assertSequenceEqual(outputs['''global_attention_mask''']		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  '''A, <mask> AllenNLP sentence.'''
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__		,add_special_tokens=SCREAMING_SNAKE_CASE__		,return_token_type_ids=SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__		,add_special_tokens=SCREAMING_SNAKE_CASE__		,return_token_type_ids=SCREAMING_SNAKE_CASE__							)
                         self.assertEqual(sum(tokens_r['''token_type_ids''']							)		,sum(tokens_p['''token_type_ids''']							)							)
                         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       :int		    =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
                         __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)
                         self.assertSequenceEqual(tokens_p['''input_ids''']		,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
                         self.assertSequenceEqual(tokens_r['''input_ids''']		,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

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






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[Any]							)      ->				Union[str, Any]:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Any=0							)      ->				Optional[Any]:
      return sorted(a_			,       key=lambda a_							: x[column]							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Optional[int]			,       a_					:		str=float('''inf'''							)							)      ->				str:

      for i in range(points_counts - 1							):
            for j in range(i + 1			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any			,       a_					:		Optional[int]=float('''inf'''							)							)      ->				Optional[Any]:

      for i in range(min(6			,       points_counts - 1							)			,       a_							):
            for j in range(max(0			,       i - 6							)			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :int		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		List[Any]			,       a_					:		int							)      ->				Optional[int]:

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(a_			,       a_							)

      # recursion
      __SCREAMING_SNAKE_CASE       :int		    =  points_counts // 2
      __SCREAMING_SNAKE_CASE       :Dict		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[:mid]			,       a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  closest_pair_of_points_sqr(
          a_			,       points_sorted_on_y[mid:]			,       points_counts - mid							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  min(a_			,       a_							)
      __SCREAMING_SNAKE_CASE       :str		    =  []
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]							) < closest_pair_dis:
                  cross_strip.append(a_							)

      __SCREAMING_SNAKE_CASE       :Dict		    =  dis_between_closest_in_strip(
          a_			,       len(a_							)			,       a_							)
      return min(a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Tuple							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  column_based_sort(a_			,       column=0							)
      __SCREAMING_SNAKE_CASE       :int		    =  column_based_sort(a_			,       column=1							)
      return (
          closest_pair_of_points_sqr(
              a_			,       a_			,       a_							)
      ) ** 0.5


if __name__ == "__main__":
  lowerCamelCase_       =		[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print("Distance:", closest_pair_of_points(points, len(points)))