


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Dict							    =     DebertaTokenizer
       lowerCamelCase__:     Optional[Any]							    =     True
       lowerCamelCase__:     Union[str, Any]							    =     DebertaTokenizerFast

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            a_    :     List[Any]										=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            a_    :     Tuple										=    dict(zip(lowerCamelCase_      ,							range(len(lowerCamelCase_    )    )    )    )
            a_    :     Optional[Any]										=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            a_    :     Any										=    {"""unk_token""": """[UNK]"""}

            a_    :     int										=    os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]    )
            a_    :     List[str]										=    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 						UpperCAmelCase(   self      :     Optional[Any]      ,							**lowerCamelCase_      :     Optional[Any]    ):
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     Tuple										=    """lower newer"""
            a_    :     Tuple										=    """lower newer"""
            return input_text, output_text

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     List[str]										=    self.get_tokenizer()
            a_    :     int										=    """lower newer"""
            a_    :     Union[str, Any]										=    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            a_    :     Any										=    tokenizer.tokenize(lowerCamelCase_    )
            self.assertListEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            a_    :     List[Any]										=    tokens + [tokenizer.unk_token]
            a_    :     Union[str, Any]										=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    )      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    self.get_tokenizer()
            a_    :     str										=    tokenizer("""Hello"""      ,							"""World"""    )
            a_    :     Optional[Any]										=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]      ,							lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Optional[int]										=    self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            a_    :     Optional[Any]										=    tokenizer.encode("""sequence builders"""      ,							add_special_tokens=lowerCamelCase_    )
            a_    :     Dict										=    tokenizer.encode("""multi-sequence build"""      ,							add_special_tokens=lowerCamelCase_    )

            a_    :     Optional[Any]										=    tokenizer.encode(
                """sequence builders"""      ,							add_special_tokens=lowerCamelCase_      ,							add_prefix_space=lowerCamelCase_    )
            a_    :     Any										=    tokenizer.encode(
                """sequence builders"""      ,							"""multi-sequence build"""      ,							add_special_tokens=lowerCamelCase_      ,							add_prefix_space=lowerCamelCase_    )

            a_    :     int										=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_    )
            a_    :     str										=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      ,							lowerCamelCase_    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode

       @slow
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     int										=    [self.tokenizer_class]
            if self.test_rust_tokenizer:
                 tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                 a_    :     Optional[int]										=    tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                 a_    :     int										=    [
                     """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                     """ALBERT incorporates two parameter reduction techniques""",
                     """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                     """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                     """ vocabulary embedding.""",
                 ]

                 a_    :     str										=    tokenizer(lowerCamelCase_      ,							padding=lowerCamelCase_    )
                 a_    :     int										=    [tokenizer.decode(lowerCamelCase_      ,							skip_special_tokens=lowerCamelCase_    ) for seq in encoding["""input_ids"""]]

                 # fmt: off
                 a_    :     Dict										=    {
                     """input_ids""": [
                         [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
                     ],
                     """token_type_ids""": [
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                     ],
                     """attention_mask""": [
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [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

                 a_    :     str										=    [
                     """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                     """ALBERT incorporates two parameter reduction techniques""",
                     """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                     """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                     """ vocabulary embedding.""",
                 ]

                 self.assertDictEqual(encoding.data      ,							lowerCamelCase_    )

                 for expected, decoded in zip(lowerCamelCase_      ,							lowerCamelCase_    ):
                      self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )







from heapq import heappop, heappush

import numpy as np



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_			,			a_    :     Any										=    grid.shape
     a_    :     Dict										=    [-1, 1, 0, 0]
     a_    :     List[Any]										=    [0, 0, -1, 1]
     if allow_diagonal:
          dx += [-1, -1, 1, 1]
          dy += [-1, 1, -1, 1]

     a_			,			a_    :     int										=    [(0, source)], set()
     a_    :     Optional[Any]										=    np.full((rows, cols)  ,  np.inf )
     a_    :     Tuple										=    0
     a_    :     Optional[Any]										=    np.empty((rows, cols)  ,  dtype=__UpperCamelCase )
     a_    :     Optional[Any]										=    None

     while queue:
          ((a_)			,			(a_))    :     Any										=    heappop(__UpperCamelCase )
          if (x, y) in visited:
               continue
          visited.add((x, y) )

          if (x, y) == destination:
               a_    :     Tuple										=    []
               while (x, y) != source:
                    path.append((x, y) )
                    a_			,			a_    :     Any										=    predecessors[x, y]
               path.append(__UpperCamelCase )  # add the source manually
               path.reverse()
               return matrix[destination], path

          for i in range(len(__UpperCamelCase ) ):
               a_			,			a_    :     Dict										=    x + dx[i], y + dy[i]
               if 0 <= nx < rows and 0 <= ny < cols:
                    a_    :     List[Any]										=    grid[nx][ny]
                    if next_node == 1 and matrix[nx, ny] > dist + 1:
                         heappush(__UpperCamelCase  ,  (dist + 1, (nx, ny)) )
                         a_    :     List[str]										=    dist + 1
                         a_    :     Union[str, Any]										=    (x, y)

     return np.inf, []


if __name__ == "__main__":
       import doctest

       doctest.testmod()



