


"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors




@require_tokenizers
class 			__snake_case   (lowerCAmelCase__			,		unittest.TestCase  ):
							__a					 =       MvpTokenizer
							__a					 =       MvpTokenizerFast
							__a					 =       True
							__a					 =       filter_roberta_detectors




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

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




							def   __a   (     self:    Union[str, Any]     ,							**A_:    Optional[int]   ):
										kwargs.update(self.special_tokens_map   )
										return self.tokenizer_class.from_pretrained(self.tmpdirname     ,							**snake_case_   )




							def   __a   (     self:    Tuple     ,							**A_:    str   ):
										kwargs.update(self.special_tokens_map   )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,							**snake_case_   )




							def   __a   (     self:    int     ,							A_:    Tuple   ):
										return "lower newer", "lower newer"




							@cached_property
							def   __a   (     self:    Optional[Any]   ):
										return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""   )




							@cached_property
							def   __a   (     self:    Optional[int]   ):
										return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""   )




							@require_torch
							def   __a   (     self:    int   ):
										__lowerCamelCase          =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										__lowerCamelCase          =      [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]:
													__lowerCamelCase          =      tokenizer(snake_case_     ,							max_length=len(snake_case_   )     ,							padding=snake_case_     ,							return_tensors="""pt"""   )
													self.assertIsInstance(snake_case_     ,							snake_case_   )

													self.assertEqual((2, 9)     ,							batch.input_ids.shape   )
													self.assertEqual((2, 9)     ,							batch.attention_mask.shape   )
													__lowerCamelCase          =      batch.input_ids.tolist()[0]
													self.assertListEqual(snake_case_     ,							snake_case_   )
													# Test that special tokens are reset




							@require_torch
							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCamelCase          =      tokenizer(snake_case_     ,							padding=snake_case_     ,							return_tensors="""pt"""   )
													# check if input_ids are returned and no labels
													self.assertIn("""input_ids"""     ,							snake_case_   )
													self.assertIn("""attention_mask"""     ,							snake_case_   )
													self.assertNotIn("""labels"""     ,							snake_case_   )
													self.assertNotIn("""decoder_attention_mask"""     ,							snake_case_   )




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




							@require_torch
							def   __a   (     self:    Optional[Any]   ):

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCamelCase          =      tokenizer(
													    ["""I am a small frog""" * 10_24, """I am a small frog"""]     ,							padding=snake_case_     ,							truncation=snake_case_     ,							return_tensors="""pt"""   )
													self.assertIsInstance(snake_case_     ,							snake_case_   )
													self.assertEqual(batch.input_ids.shape     ,							(2, 10_24)   )




							@require_torch
							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      ["""A long paragraph for summarization."""]
										__lowerCamelCase          =      [
										    """Summary of the text.""",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCamelCase          =      tokenizer(snake_case_     ,							text_target=snake_case_     ,							return_tensors="""pt"""   )
													__lowerCamelCase          =      inputs["""input_ids"""]
													__lowerCamelCase          =      inputs["""labels"""]
													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()   )




							def   __a   (     self:    Optional[int]   ):
										pass




							def   __a   (     self:    Optional[int]   ):

										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																__lowerCamelCase          =      self.rust_tokenizer_class.from_pretrained(snake_case_     ,							**snake_case_   )
																__lowerCamelCase          =      self.tokenizer_class.from_pretrained(snake_case_     ,							**snake_case_   )
																__lowerCamelCase          =      """A, <mask> AllenNLP sentence."""
																__lowerCamelCase          =      tokenizer_r.encode_plus(snake_case_     ,							add_special_tokens=snake_case_     ,							return_token_type_ids=snake_case_   )
																__lowerCamelCase          =      tokenizer_p.encode_plus(snake_case_     ,							add_special_tokens=snake_case_     ,							return_token_type_ids=snake_case_   )

																# token_type_ids should put 0 everywhere
																self.assertEqual(sum(tokens_r["""token_type_ids"""]   )     ,							sum(tokens_p["""token_type_ids"""]   )   )

																# attention_mask should put 1 everywhere, so sum over length should be 1
																self.assertEqual(
																    sum(tokens_r["""attention_mask"""]   ) / len(tokens_r["""attention_mask"""]   )     ,							sum(tokens_p["""attention_mask"""]   ) / len(tokens_p["""attention_mask"""]   )     ,							)

																__lowerCamelCase          =      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
																__lowerCamelCase          =      tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]   )

																# Rust correctly handles the space before the mask while python doesnt
																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(
																    snake_case_     ,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )
																self.assertSequenceEqual(
																    snake_case_     ,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )








import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


