





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     __snake_case       (							__lowerCamelCase   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     LEDTokenizer
       lowerCAmelCase__      		=     LEDTokenizerFast
       lowerCAmelCase__      		=     True




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         super().setUp()
         __snake_case:						Tuple   =						[
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         __snake_case:						str   =						dict(zip(A							,				range(len(A		)		)		)		)
         __snake_case:						List[str]   =						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         __snake_case:						List[str]   =						{"""unk_token""": """<unk>"""}

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




       def 							UpperCAmelCase__ (  self :    Dict							,				**A :    Optional[int]		):
         kwargs.update(self.special_tokens_map		)
         return self.tokenizer_class.from_pretrained(self.tmpdirname							,				**A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				**A :    List[str]		):
         kwargs.update(self.special_tokens_map		)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,				**A		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    List[Any]		):
         return "lower newer", "lower newer"




       @cached_property
       def 							UpperCAmelCase__ (  self :    List[str]		):
         return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""		)




       @cached_property
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""		)




       @require_torch
       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						str   =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         __snake_case:						List[str]   =						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						Optional[int]   =						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		)
           __snake_case:						Dict   =						batch.input_ids.tolist()[0]
           self.assertListEqual(A							,				A		)




       @require_torch
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[int]   =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						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 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						List[Any]   =						[
             """Summary of the text.""",
             """Another summary.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						Dict   =						tokenizer(text_target=A							,				max_length=32							,				padding="""max_length"""							,				return_tensors="""pt"""		)
           self.assertEqual(32							,				targets["""input_ids"""].shape[1]		)




       @require_torch
       def 							UpperCAmelCase__ (  self :    List[Any]		):


         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						Optional[int]   =						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 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						int   =						["""A long paragraph for summarization."""]
         __snake_case:						int   =						[
             """Summary of the text.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						Optional[Any]   =						tokenizer(A							,				return_tensors="""pt"""		)
           __snake_case:						Union[str, Any]   =						tokenizer(text_target=A							,				return_tensors="""pt"""		)
           __snake_case:						int   =						inputs["""input_ids"""]
           __snake_case:						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 							UpperCAmelCase__ (  self :    Optional[int]		):


         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __snake_case:						Union[str, Any]   =						["""Summary of the text.""", """Another summary."""]
           __snake_case:						Union[str, Any]   =						[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

           __snake_case:						int   =						tokenizer(A							,				padding=A		)
           __snake_case:						str   =						[[0] * len(A		) for x in encoded_output["""input_ids"""]]
           __snake_case:						List[Any]   =						tokenizer.pad(A		)
           self.assertSequenceEqual(outputs["""global_attention_mask"""]							,				A		)




       def 							UpperCAmelCase__ (  self :    int		):
         pass






       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):


         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             __snake_case:						int   =						self.rust_tokenizer_class.from_pretrained(A							,				**A		)
             __snake_case:						Any   =						self.tokenizer_class.from_pretrained(A							,				**A		)
             __snake_case:						Dict   =						"""A, <mask> AllenNLP sentence."""
             __snake_case:						int   =						tokenizer_r.encode_plus(A							,				add_special_tokens=A							,				return_token_type_ids=A		)
             __snake_case:						List[str]   =						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"""]		)							,				)

             __snake_case:						Tuple   =						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
             __snake_case:						Tuple   =						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>"""]		)








from collections.abc import Sequence






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			return sum(c * (x**i) for i, c in enumerate(__lowercase ) )






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			_snake_case		=			0.0
			for coeff in reversed(__lowercase ):
						_snake_case		=			result * x + coeff
			return result


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[Any]       =			(0.0, 0.0, 5.0, 9.3, 7.0)
			_lowerCamelCase       :				Optional[int]       =			1_0.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))