


"""simple docstring"""


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 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Any													=LEDTokenizer
     snake_case__	:					Any													=LEDTokenizerFast
     snake_case__	:					Optional[int]													=True

     def 		a__						(	self:						Union[str, Any]	)->    int:
           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					:					List[Any]        =	dict(zip(__a     ,		range(len(__a	)	)	)	)
           lowerCamelCase					:					str        =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           lowerCamelCase					:					List[Any]        =	{"""unk_token""": """<unk>"""}

           lowerCamelCase					:					Optional[int]        =	os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]	)
           lowerCamelCase					:					Tuple        =	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(__a	) + """\n"""	)
           with open(self.merges_file     ,		"""w"""     ,		encoding="""utf-8"""	) as fp:
                 fp.write("""\n""".join(__a	)	)

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[Any]	)->    List[Any]:
           kwargs.update(self.special_tokens_map	)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						int     ,		**__a:						Optional[int]	)->    Dict:
           kwargs.update(self.special_tokens_map	)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						Dict     ,		__a:						int	)->    Any:
           return "lower newer", "lower newer"

     @cached_property
     def 		a__						(	self:						Dict	)->    List[Any]:
           return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""	)

     @cached_property
     def 		a__						(	self:						Optional[Any]	)->    int:
           return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""	)

     @require_torch
     def 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					int        =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           lowerCamelCase					:					Dict        =	[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					Dict        =	tokenizer(__a     ,		max_length=len(__a	)     ,		padding=__a     ,		return_tensors="""pt"""	)
                 self.assertIsInstance(__a     ,		__a	)

                 self.assertEqual((2, 9)     ,		batch.input_ids.shape	)
                 self.assertEqual((2, 9)     ,		batch.attention_mask.shape	)
                 lowerCamelCase					:					Dict        =	batch.input_ids.tolist()[0]
                 self.assertListEqual(__a     ,		__a	)

     @require_torch
     def 		a__						(	self:						Any	)->    Any:
           lowerCamelCase					:					List[Any]        =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					int        =	tokenizer(__a     ,		padding=__a     ,		return_tensors="""pt"""	)
                 self.assertIn("""input_ids"""     ,		__a	)
                 self.assertIn("""attention_mask"""     ,		__a	)
                 self.assertNotIn("""labels"""     ,		__a	)
                 self.assertNotIn("""decoder_attention_mask"""     ,		__a	)

     @require_torch
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	[
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					Any        =	tokenizer(text_target=__a     ,		max_length=32     ,		padding="""max_length"""     ,		return_tensors="""pt"""	)
                 self.assertEqual(32     ,		targets["""input_ids"""].shape[1]	)

     @require_torch
     def 		a__						(	self:						int	)->    Tuple:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					List[str]        =	tokenizer(
                     ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,		padding=__a     ,		truncation=__a     ,		return_tensors="""pt"""	)
                 self.assertIsInstance(__a     ,		__a	)
                 self.assertEqual(batch.input_ids.shape     ,		(2, 5_122)	)

     @require_torch
     def 		a__						(	self:						Tuple	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	["""A long paragraph for summarization."""]
           lowerCamelCase					:					Any        =	[
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					str        =	tokenizer(__a     ,		return_tensors="""pt"""	)
                 lowerCamelCase					:					str        =	tokenizer(text_target=__a     ,		return_tensors="""pt"""	)
                 lowerCamelCase					:					Dict        =	inputs["""input_ids"""]
                 lowerCamelCase					:					List[str]        =	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 		a__						(	self:						Optional[Any]	)->    str:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 lowerCamelCase					:					List[str]        =	["""Summary of the text.""", """Another summary."""]
                 lowerCamelCase					:					Tuple        =	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                 lowerCamelCase					:					Optional[int]        =	tokenizer(__a     ,		padding=__a	)
                 lowerCamelCase					:					Any        =	[[0] * len(__a	) for x in encoded_output["""input_ids"""]]
                 lowerCamelCase					:					Union[str, Any]        =	tokenizer.pad(__a	)
                 self.assertSequenceEqual(outputs["""global_attention_mask"""]     ,		__a	)

     def 		a__						(	self:						int	)->    Optional[int]:
           pass







     def 		a__						(	self:						Any	)->    Union[str, Any]:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'	):
                       lowerCamelCase					:					List[str]        =	self.rust_tokenizer_class.from_pretrained(__a     ,		**__a	)
                       lowerCamelCase					:					List[Any]        =	self.tokenizer_class.from_pretrained(__a     ,		**__a	)
                       lowerCamelCase					:					int        =	"""A, <mask> AllenNLP sentence."""
                       lowerCamelCase					:					Optional[Any]        =	tokenizer_r.encode_plus(__a     ,		add_special_tokens=__a     ,		return_token_type_ids=__a	)
                       lowerCamelCase					:					Tuple        =	tokenizer_p.encode_plus(__a     ,		add_special_tokens=__a     ,		return_token_type_ids=__a	)
                       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"""]	)     ,		)

                       lowerCamelCase					:					Any        =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
                       lowerCamelCase					:					List[Any]        =	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)
                       self.assertSequenceEqual(tokens_p["""input_ids"""]     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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








"""simple docstring"""


__lowerCamelCase      :List[Any]	    =		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__lowerCamelCase      :Union[str, Any]	    =		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					Tuple        =	True
      lowerCamelCase					:					Any        =	[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      order.append(UpperCamelCase__    )

      return order




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					List[Any]        =	True
      lowerCamelCase					:					int        =	[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      return component






def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]    )   ->      list[list[int]]:
      lowerCamelCase					:					int        =	len(UpperCamelCase__    ) * [False]
      lowerCamelCase					:					dict[int, list[int]]        =	{vert: [] for vert in range(len(UpperCamelCase__    )    )}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCamelCase__    )

      lowerCamelCase					:					int        =	[]
      for i, was_visited in enumerate(UpperCamelCase__    ):
            if not was_visited:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      lowerCamelCase					:					Tuple        =	[]
      lowerCamelCase					:					str        =	len(UpperCamelCase__    ) * [False]

      for i in range(len(UpperCamelCase__    )    ):
            lowerCamelCase					:					Any        =	order[len(UpperCamelCase__    ) - i - 1]
            if not visited[vert]:
                  lowerCamelCase					:					List[str]        =	find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
                  components_list.append(UpperCamelCase__    )

      return components_list




