




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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		DebertaTokenizer
      A__		     =		True
      A__		     =		DebertaTokenizerFast

      def 	__magic_name__(   self		):
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowerCAmelCase__				:  Optional[Any]		       =     [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''[UNK]''',
        ]
        lowerCAmelCase__				:  Dict		       =     dict(zip(__UpperCAmelCase      ,				range(len(__UpperCAmelCase		)		)		)		)
        lowerCAmelCase__				:  str		       =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        lowerCAmelCase__				:  str		       =     {'''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(__UpperCAmelCase		) + '''\n'''		)
        with open(self.merges_file      ,				'''w'''      ,				encoding='''utf-8'''		) as fp:
          fp.write('''\n'''.join(__UpperCAmelCase		)		)

      def 	__magic_name__(   self      ,				**__UpperCAmelCase		):
        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     '''lower newer'''
        lowerCAmelCase__				:  Optional[Any]		       =     '''lower newer'''
        return input_text, output_text

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_tokenizer()
        lowerCAmelCase__				:  Union[str, Any]		       =     '''lower newer'''
        lowerCAmelCase__				:  Any		       =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        lowerCAmelCase__				:  Optional[Any]		       =     tokenizer.tokenize(__UpperCAmelCase		)
        self.assertListEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     tokens + [tokenizer.unk_token]
        lowerCAmelCase__				:  int		       =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase		)      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     self.get_tokenizer()
        lowerCAmelCase__				:  Optional[int]		       =     tokenizer('''Hello'''      ,				'''World'''		)
        lowerCAmelCase__				:  List[Any]		       =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['''token_type_ids''']      ,				__UpperCAmelCase		)

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

        lowerCAmelCase__				:  Union[str, Any]		       =     tokenizer.encode('''sequence builders'''      ,				add_special_tokens=__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     tokenizer.encode('''multi-sequence build'''      ,				add_special_tokens=__UpperCAmelCase		)

        lowerCAmelCase__				:  Tuple		       =     tokenizer.encode(
            '''sequence builders'''      ,				add_special_tokens=__UpperCAmelCase      ,				add_prefix_space=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     tokenizer.encode(
            '''sequence builders'''      ,				'''multi-sequence build'''      ,				add_special_tokens=__UpperCAmelCase      ,				add_prefix_space=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase      ,				__UpperCAmelCase		)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







      @slow
      def 	__magic_name__(   self		):
        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__				:  List[str]		       =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

          lowerCAmelCase__				:  Union[str, 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__				:  int		       =     tokenizer(__UpperCAmelCase      ,				padding=__UpperCAmelCase		)
          lowerCAmelCase__				:  Tuple		       =     [tokenizer.decode(__UpperCAmelCase      ,				skip_special_tokens=__UpperCAmelCase		) for seq in encoding['''input_ids''']]

          # fmt: off
          lowerCAmelCase__				:  List[str]		       =     {
              '''input_ids''': [
                  [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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__				:  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.''',
          ]

          self.assertDictEqual(encoding.data      ,				__UpperCAmelCase		)

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







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
          # modified it, it will not be accepted here again, since `auto` values would have been overridden
          lowerCAmelCase__				:  Union[str, Any]		       =     deepcopy(__UpperCAmelCase		)
        elif os.path.exists(__UpperCAmelCase		):
          with io.open(__UpperCAmelCase      ,				'''r'''      ,				encoding='''utf-8'''		) as f:
            lowerCAmelCase__				:  int		       =     json.load(__UpperCAmelCase		)
        else:
          try:
            lowerCAmelCase__				:  Union[str, Any]		       =     baseaa.urlsafe_baadecode(__UpperCAmelCase		).decode('''utf-8'''		)
            lowerCAmelCase__				:  Optional[int]		       =     json.loads(__UpperCAmelCase		)
          except (UnicodeDecodeError, AttributeError, ValueError):
            raise ValueError(
                f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""		)

        lowerCAmelCase__				:  Optional[Any]		       =     config

        self.set_stage_and_offload()

      def 	__magic_name__(   self		):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value('''zero_optimization.stage'''      ,				-1		)

        # offload
        lowerCAmelCase__				:  Dict		       =     False
        if self.is_zeroa() or self.is_zeroa():
          lowerCAmelCase__				:  Dict		       =     set(['''cpu''', '''nvme''']		)
          lowerCAmelCase__				:  Optional[Any]		       =     set(
              [
                  self.get_value('''zero_optimization.offload_optimizer.device'''		),
                  self.get_value('''zero_optimization.offload_param.device'''		),
              ]		)
          if len(offload_devices & offload_devices_valid		) > 0:
            lowerCAmelCase__				:  List[Any]		       =     True

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  Tuple		       =     ds_key_long.split('''.'''		)
        lowerCAmelCase__				:  int		       =     nodes.pop()
        for node in nodes:
          lowerCAmelCase__				:  int		       =     config.get(__UpperCAmelCase		)
          if config is None:
            return None, ds_key

        return config, ds_key

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.find_config_node(__UpperCAmelCase		)
        if config is None:
          return default
        return config.get(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  Dict		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  str		       =     ds_key_long.split('''.'''		)
        for node in nodes:
          lowerCAmelCase__				:  Dict		       =     config
          lowerCAmelCase__				:  List[str]		       =     config.get(__UpperCAmelCase		)
          if config is None:
            if must_exist:
              raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""		)
            else:
              return

        # if found remove it
        if parent_config is not None:
          parent_config.pop(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else bool(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else not bool(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        return self._stage == 2

      def 	__magic_name__(   self		):
        return self._stage == 3







      def 	__magic_name__(   self		):
        return self._offload






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     engine







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__UpperCAmelCase      ,				**__UpperCAmelCase		)

        # Deepspeed's `engine.step` performs the following operations:
        # - gradient accumulation check
        # - gradient clipping
        # - optimizer step
        # - zero grad
        # - checking overflow
        # - lr_scheduler step (only if engine.lr_scheduler is not None)
        self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				device_placement=__UpperCAmelCase      ,				scaler=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     hasattr(self.optimizer      ,				'''overflow'''		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def 	__magic_name__(   self		):
        if self.__has_overflow__:
          return self.optimizer.overflow
        return False






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0.001      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     params
        lowerCAmelCase__				:  str		       =     lr
        lowerCAmelCase__				:  List[str]		       =     weight_decay
        lowerCAmelCase__				:  Dict		       =     kwargs






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     optimizer
        lowerCAmelCase__				:  Optional[Any]		       =     total_num_steps
        lowerCAmelCase__				:  str		       =     warmup_num_steps
        lowerCAmelCase__				:  Dict		       =     kwargs

