"""simple docstring"""


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      GPTaTokenizer
       SCREAMING_SNAKE_CASE_			=      GPTaTokenizerFast
       SCREAMING_SNAKE_CASE_			=      True
       SCREAMING_SNAKE_CASE_			=      {"add_prefix_space": True}
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Dict:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            snake_case_					       =					[
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
                '<|endoftext|>',
            ]
            snake_case_					       =					dict(zip(lowerCAmelCase__,							range(len(lowerCAmelCase__))))
            snake_case_					       =					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            snake_case_					       =					{'unk_token': '<unk>'}

            snake_case_					       =					os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file'])
            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(lowerCAmelCase__) + '\n')
            with open(self.merges_file,							'w',							encoding='utf-8') as fp:
                 fp.write('\n'.join(lowerCAmelCase__))







       def 		a_						( self,							**lowerCAmelCase__)      ->      int:
            kwargs.update(self.special_tokens_map)
            return GPTaTokenizer.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Optional[int]:
            kwargs.update(self.special_tokens_map)
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					'lower newer'
            snake_case_					       =					'lower newer'
            return input_text, output_text







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					GPTaTokenizer(self.vocab_file,							self.merges_file,							**self.special_tokens_map)
            snake_case_					       =					'lower newer'
            snake_case_					       =					['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
            snake_case_					       =					tokenizer.tokenize(lowerCAmelCase__,							add_prefix_space=lowerCAmelCase__)
            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            snake_case_					       =					tokens + [tokenizer.unk_token]
            snake_case_					       =					[14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__),							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            if not self.test_rust_tokenizer:
                 return

            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__)

            snake_case_					       =					'lower newer'

            # Testing tokenization
            snake_case_					       =					tokenizer.tokenize(lowerCAmelCase__,							add_prefix_space=lowerCAmelCase__)
            snake_case_					       =					rust_tokenizer.tokenize(lowerCAmelCase__)
            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            # Testing conversion to ids without special tokens
            snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							add_prefix_space=lowerCAmelCase__)
            snake_case_					       =					rust_tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            # Testing conversion to ids with special tokens
            snake_case_					       =					self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__)
            snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_prefix_space=lowerCAmelCase__)
            snake_case_					       =					rust_tokenizer.encode(lowerCAmelCase__)
            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            # Testing the unknown token
            snake_case_					       =					tokens + [rust_tokenizer.unk_token]
            snake_case_					       =					[14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__),							lowerCAmelCase__)







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      List[Any]:
            # It's very difficult to mix/test pretokenization with byte-level
            # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
            pass







       def 		a_						( self,							lowerCAmelCase__=15)      ->      List[str]:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
                      snake_case_					       =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__,							**lowerCAmelCase__)

                      # Simple input
                      snake_case_					       =					'This is a simple input'
                      snake_case_					       =					['This is a simple input 1', 'This is a simple input 2']
                      snake_case_					       =					('This is a simple input', 'This is a pair')
                      snake_case_					       =					[
                          ('This is a simple input 1', 'This is a simple input 2'),
                          ('This is a simple pair 1', 'This is a simple pair 2'),
                      ]

                      # Simple input tests
                      self.assertRaises(lowerCAmelCase__,							tokenizer_r.encode,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length')

                      # Simple input
                      self.assertRaises(lowerCAmelCase__,							tokenizer_r.encode_plus,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length')

                      # Simple input
                      self.assertRaises(
                          lowerCAmelCase__,							tokenizer_r.batch_encode_plus,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length',							)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__,							tokenizer_r.encode,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length')

                      # Pair input
                      self.assertRaises(lowerCAmelCase__,							tokenizer_r.encode_plus,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length')

                      # Pair input
                      self.assertRaises(
                          lowerCAmelCase__,							tokenizer_r.batch_encode_plus,							lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding='max_length',							)







       def 		a_						( self)      ->      int:
            snake_case_					       =					GPTaTokenizer.from_pretrained(self.tmpdirname,							pad_token='<pad>')

            # Simple input
            snake_case_					       =					'This is a simple input'
            snake_case_					       =					['This is a simple input looooooooong', 'This is a simple input']
            snake_case_					       =					('This is a simple input', 'This is a pair')
            snake_case_					       =					[
                ('This is a simple input loooooong', 'This is a simple input'),
                ('This is a simple pair loooooong', 'This is a simple pair'),
            ]

            snake_case_					       =					tokenizer.pad_token_id

            snake_case_					       =					tokenizer(lowerCAmelCase__,							padding='max_length',							max_length=30,							return_tensors='np')
            snake_case_					       =					tokenizer(lowerCAmelCase__,							padding=lowerCAmelCase__,							truncate=lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					tokenizer(*lowerCAmelCase__,							padding='max_length',							max_length=60,							return_tensors='np')
            snake_case_					       =					tokenizer(lowerCAmelCase__,							padding=lowerCAmelCase__,							truncate=lowerCAmelCase__,							return_tensors='np')

            # s
            # test single string max_length padding
            self.assertEqual(out_s['input_ids'].shape[-1],							30)
            self.assertTrue(pad_token_id in out_s['input_ids'])
            self.assertTrue(0 in out_s['attention_mask'])

            # s2
            # test automatic padding
            self.assertEqual(out_sa['input_ids'].shape[-1],							33)
            # long slice doesn't have padding
            self.assertFalse(pad_token_id in out_sa['input_ids'][0])
            self.assertFalse(0 in out_sa['attention_mask'][0])
            # short slice does have padding
            self.assertTrue(pad_token_id in out_sa['input_ids'][1])
            self.assertTrue(0 in out_sa['attention_mask'][1])

            # p
            # test single pair max_length padding
            self.assertEqual(out_p['input_ids'].shape[-1],							60)
            self.assertTrue(pad_token_id in out_p['input_ids'])
            self.assertTrue(0 in out_p['attention_mask'])

            # p2
            # test automatic padding pair
            self.assertEqual(out_pa['input_ids'].shape[-1],							52)
            # long slice pair doesn't have padding
            self.assertFalse(pad_token_id in out_pa['input_ids'][0])
            self.assertFalse(0 in out_pa['attention_mask'][0])
            # short slice pair does have padding
            self.assertTrue(pad_token_id in out_pa['input_ids'][1])
            self.assertTrue(0 in out_pa['attention_mask'][1])







       def 		a_						( self)      ->      Any:
            snake_case_					       =					'$$$'
            snake_case_					       =					GPTaTokenizer.from_pretrained(self.tmpdirname,							bos_token=lowerCAmelCase__,							add_bos_token=lowerCAmelCase__)

            snake_case_					       =					'This is a simple input'
            snake_case_					       =					['This is a simple input 1', 'This is a simple input 2']

            snake_case_					       =					tokenizer.bos_token_id

            snake_case_					       =					tokenizer(lowerCAmelCase__)
            snake_case_					       =					tokenizer(lowerCAmelCase__)

            self.assertEqual(out_s.input_ids[0],							lowerCAmelCase__)
            self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

            snake_case_					       =					tokenizer.decode(out_s.input_ids)
            snake_case_					       =					tokenizer.batch_decode(out_sa.input_ids)

            self.assertEqual(decode_s.split()[0],							lowerCAmelCase__)
            self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))







       def 		a_						( self)      ->      int:
            pass







       def 		a_						( self)      ->      Tuple:
            # TODO: change to self.get_tokenizers() when the fast version is implemented
            snake_case_					       =					[self.get_tokenizer(do_lower_case=lowerCAmelCase__,							add_bos_token=lowerCAmelCase__)]
            for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}'):
                      snake_case_					       =					'Encode this.'
                      snake_case_					       =					'This one too please.'
                      snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      encoded_sequence += tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      snake_case_					       =					tokenizer.encode_plus(
                          lowerCAmelCase__,							lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							return_special_tokens_mask=lowerCAmelCase__,							)
                      snake_case_					       =					encoded_sequence_dict['input_ids']
                      snake_case_					       =					encoded_sequence_dict['special_tokens_mask']
                      self.assertEqual(len(lowerCAmelCase__),							len(lowerCAmelCase__))

                      snake_case_					       =					[
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase__)
                      ]
                      snake_case_					       =					[x for x in filtered_sequence if x is not None]
                      self.assertEqual(lowerCAmelCase__,							lowerCAmelCase__)




@require_tokenizers
class  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      Optional[int]:
            # More context:
            # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
            # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
            # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

            snake_case_					       =					AutoTokenizer.from_pretrained('facebook/opt-350m',							from_slow=lowerCAmelCase__)
            snake_case_					       =					'A photo of a cat'

            snake_case_					       =					tokenizer.encode(
                lowerCAmelCase__,							)
            self.assertEqual(lowerCAmelCase__,							[2, 250, 1345, 9, 10, 4758])
            tokenizer.save_pretrained('test_opt')

            snake_case_					       =					AutoTokenizer.from_pretrained('./test_opt')
            snake_case_					       =					tokenizer.encode(
                lowerCAmelCase__,							)
            self.assertEqual(lowerCAmelCase__,							[2, 250, 1345, 9, 10, 4758])







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					AutoTokenizer.from_pretrained('facebook/opt-350m',							use_slow=lowerCAmelCase__)
            snake_case_					       =					'A photo of a cat'

            snake_case_					       =					tokenizer.encode(
                lowerCAmelCase__,							)
            # Same as above
            self.assertEqual(lowerCAmelCase__,							[2, 250, 1345, 9, 10, 4758])







       @unittest.skip('This test is failing because of a bug in the fast tokenizer')
       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					AutoTokenizer.from_pretrained('facebook/opt-350m',							from_slow=lowerCAmelCase__)

            snake_case_					       =					'bos'
            snake_case_					       =					tokenizer.get_vocab()['bos']

            snake_case_					       =					'A photo of a cat'
            snake_case_					       =					tokenizer.encode(
                lowerCAmelCase__,							)
            # We changed the bos token
            self.assertEqual(lowerCAmelCase__,							[3_1957, 250, 1345, 9, 10, 4758])
            tokenizer.save_pretrained('./tok')
            snake_case_					       =					AutoTokenizer.from_pretrained('./tok')
            self.assertTrue(tokenizer.is_fast)
            snake_case_					       =					tokenizer.encode(
                lowerCAmelCase__,							)
            self.assertEqual(lowerCAmelCase__,							[3_1957, 250, 1345, 9, 10, 4758])








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A    :					Dict =      Lock()







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






		"""simple docstring"""

		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0    ,     1_0    ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(__a    )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						A__        =   rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						A__        =   min(__a    ,     __a    )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(__a    )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						A__        =   lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						A__        =   max(__a    ,     __a    )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(__a    )







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""simple docstring"""

		A__        =   []
		A__        =   []
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe()    )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		A__        =   Pipe()
		A__        =   Pipe()
		process_array_.append(
		    Process(
		        target=__a    ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,     )    )
		A__        =   temp_rs
		A__        =   temp_rr

		for i in range(1    ,     len(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])    ,     )    )
				A__        =   temp_rs
				A__        =   temp_rr

		process_array_.append(
		    Process(
		        target=__a    ,     args=(
		            len(__a    ) - 1,
		            arr[len(__a    ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(__a    ) - 1],
		        )    ,     )    )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0    ,     len(__a    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



