from collections import Counter







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : str					):







    '''simple docstring'''
    return sum(socks_by_color // 2 for socks_by_color in Counter(SCREAMING_SNAKE_CASE__					).values()					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowercase_   				=       [int(x) for x in input("Enter socks by color :").rstrip().split()]
     print(F'''sock_merchant({colors}) = {sock_merchant(colors)}''')







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_           =	get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class        A_					(       __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   ErnieMTokenizer
      __snake_case					       =   False
      __snake_case					       =   True
      __snake_case					       =   False
      __snake_case					       =   False




      def      _snake_case	(				self:       Optional[int]				):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __lowerCamelCase		:							List[str]							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)
       tokenizer.save_pretrained(self.tmpdirname				)




      def      _snake_case	(				self:       Dict						,      a:       List[Any]				):
       __lowerCamelCase		:							Optional[Any]							=						'this is a test'
       __lowerCamelCase		:							Optional[int]							=						'this is a test'
       return input_text, output_text




      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							List[str]							=						'<pad>'
       __lowerCamelCase		:							Union[str, Any]							=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a				)						,      a				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a				)						,      a				)




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]						,      '<pad>'				)
       self.assertEqual(vocab_keys[1]						,      '<unk>'				)
       self.assertEqual(vocab_keys[-1]						,      '▁eloquent'				)
       self.assertEqual(len(a				)						,      3_0000				)




      def      _snake_case	(				self:       Tuple				):
       self.assertEqual(self.get_tokenizer().vocab_size						,      3_0000				)




      def      _snake_case	(				self:       int				):
       if not self.test_rust_tokenizer:
        return

       __lowerCamelCase		:							Any							=						self.get_tokenizer()
       __lowerCamelCase		:							Optional[Any]							=						self.get_rust_tokenizer()

       __lowerCamelCase		:							Optional[int]							=						'I was born in 92000, and this is falsé.'

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize(a				)
       __lowerCamelCase		:							Dict							=						rust_tokenizer.tokenize(a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							List[str]							=						tokenizer.encode(a						,      add_special_tokens=a				)
       __lowerCamelCase		:							List[Any]							=						rust_tokenizer.encode(a						,      add_special_tokens=a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							str							=						self.get_rust_tokenizer()
       __lowerCamelCase		:							str							=						tokenizer.encode(a				)
       __lowerCamelCase		:							List[str]							=						rust_tokenizer.encode(a				)
       self.assertListEqual(a						,      a				)




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						ErnieMTokenizer(a						,      do_lower_case=a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							List[str]							=						tokenizer.tokenize('This is a test'				)
       self.assertListEqual(a						,      ['▁this', '▁is', '▁a', '▁test']				)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)						,      [48, 25, 21, 1289]				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']				)
       __lowerCamelCase		:							Any							=						tokenizer.convert_tokens_to_ids(a				)
       self.assertListEqual(a						,      [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

       __lowerCamelCase		:							int							=						tokenizer.convert_ids_to_tokens(a				)
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']						,      )




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Tuple							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							Tuple							=						tokenizer.encode('sequence builders'				)
       __lowerCamelCase		:							List[Any]							=						tokenizer.encode('multi-sequence build'				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.build_inputs_with_special_tokens(a				)
       __lowerCamelCase		:							Tuple							=						tokenizer.build_inputs_with_special_tokens(a						,      a				)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]




      @slow
      def      _snake_case	(				self:       List[Any]				):
       # fmt: off
       __lowerCamelCase		:							int							=						{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a						,      model_name='susnato/ernie-m-base_pytorch'						,      sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ]						,      )

