

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def      snake_case_				(__A					:						Dict					)      ->     Dict:
   return (torch.arange(state.num_processes					) + 1.0 + (state.num_processes * state.process_index)).to(state.device					)





def      snake_case_				(__A					:						Any					)      ->     Optional[int]:
   __lowerCAmelCase  :      Optional[Any]    =						create_tensor(__A					)
   __lowerCAmelCase  :      int    =						gather(__A					)
   assert gathered_tensor.tolist() == list(range(1 ,       state.num_processes**2 + 1					)					)





def      snake_case_				(__A					:						int					)      ->     str:
   __lowerCAmelCase  :      Union[str, Any]    =						[state.process_index]
   __lowerCAmelCase  :      List[Any]    =						gather_object(__A					)
   assert len(__A					) == state.num_processes, f'''{gathered_obj}, {len(__A					)} != {state.num_processes}'''
   assert gathered_obj == list(range(state.num_processes					)					), f'''{gathered_obj} != {list(range(state.num_processes					)					)}'''





def      snake_case_				(__A					:						Tuple					)      ->     int:
   __lowerCAmelCase  :      Tuple    =						create_tensor(__A					)
   __lowerCAmelCase  :      Tuple    =						broadcast(__A					)
   assert broadcasted_tensor.shape == torch.Size([state.num_processes]					)
   assert broadcasted_tensor.tolist() == list(range(1 ,       state.num_processes + 1					)					)





def      snake_case_				(__A					:						List[Any]					)      ->     Optional[int]:
   # We need to pad the tensor with one more element if we are the main process
   # to ensure that we can pad
   if state.is_main_process:
      __lowerCAmelCase  :      str    =						torch.arange(state.num_processes + 1					).to(state.device					)
   else:
      __lowerCAmelCase  :      Union[str, Any]    =						torch.arange(state.num_processes					).to(state.device					)
   __lowerCAmelCase  :      str    =						pad_across_processes(__A					)
   assert padded_tensor.shape == torch.Size([state.num_processes + 1]					)
   if not state.is_main_process:
      assert padded_tensor.tolist() == list(range(0 ,       state.num_processes					)					) + [0]





def      snake_case_				(__A					:						Dict					)      ->     Dict:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   __lowerCAmelCase  :      Optional[Any]    =						create_tensor(__A					)
   __lowerCAmelCase  :      Optional[int]    =						reduce(__A ,       """sum"""					)
   __lowerCAmelCase  :      Dict    =						torch.tensor([4.0, 6]					).to(state.device					)
   assert torch.allclose(__A ,       __A					), f'''{reduced_tensor} != {truth_tensor}'''





def      snake_case_				(__A					:						Any					)      ->     str:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   __lowerCAmelCase  :      str    =						create_tensor(__A					)
   __lowerCAmelCase  :      int    =						reduce(__A ,       """mean"""					)
   __lowerCAmelCase  :      Optional[Any]    =						torch.tensor([2.0, 3]					).to(state.device					)
   assert torch.allclose(__A ,       __A					), f'''{reduced_tensor} != {truth_tensor}'''





def      snake_case_				(__A					:						Optional[int]					)      ->     str:
   # For xla_spawn (TPUs)
   main()





def      snake_case_				()      ->     List[str]:
   __lowerCAmelCase  :      Union[str, Any]    =						PartialState()
   state.print(f'''State: {state}'''					)
   state.print("""testing gather"""					)
   test_gather(__A					)
   state.print("""testing gather_object"""					)
   test_gather_object(__A					)
   state.print("""testing broadcast"""					)
   test_broadcast(__A					)
   state.print("""testing pad_across_processes"""					)
   test_pad_across_processes(__A					)
   state.print("""testing reduce_sum"""					)
   test_reduce_sum(__A					)
   state.print("""testing reduce_mean"""					)
   test_reduce_mean(__A					)


if __name__ == "__main__":
   main()





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 SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=DebertaTokenizer
  lowerCamelCase   :      Dict										=True
  lowerCamelCase   :      List[str]										=DebertaTokenizerFast







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     super().setUp()

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

     __lowerCAmelCase  :      Tuple    =						os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""vocab_file"""]   )
     __lowerCAmelCase  :      Optional[int]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Tuple   )							->     Dict:






     """simple docstring"""







     kwargs.update(self.special_tokens_map   )
     return self.tokenizer_class.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						"""lower newer"""
     __lowerCAmelCase  :      Union[str, Any]    =						"""lower newer"""
     return input_text, output_text







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







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

     __lowerCAmelCase  :      List[Any]    =						tokens + [tokenizer.unk_token]
     __lowerCAmelCase  :      Union[str, Any]    =						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase   )	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.get_tokenizer()
     __lowerCAmelCase  :      Any    =						tokenizer("""Hello"""	,							"""World"""   )
     __lowerCAmelCase  :      Tuple    =						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
     self.assertListEqual(tokd["""token_type_ids"""]	,							lowerCAmelCase   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

     __lowerCAmelCase  :      Optional[Any]    =						tokenizer.encode("""sequence builders"""	,							add_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						tokenizer.encode("""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							"""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase	,							lowerCAmelCase   )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[self.tokenizer_class]
     if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

     for tokenizer_class in tokenizer_classes:
        __lowerCAmelCase  :      int    =						tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

        __lowerCAmelCase  :      Optional[Any]    =						[
            """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.""",
        ]

        __lowerCAmelCase  :      List[str]    =						tokenizer(lowerCAmelCase	,							padding=lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						[tokenizer.decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   ) for seq in encoding["""input_ids"""]]

        # fmt: off
        __lowerCAmelCase  :      Any    =						{
            """input_ids""": [
                [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

        __lowerCAmelCase  :      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.""",
        ]

        self.assertDictEqual(encoding.data	,							lowerCAmelCase   )

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


