


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

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			str:
             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    # 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
                    a_					:		Optional[Any]							=  deepcopy(SCREAMING_SNAKE_CASE__     )
             elif os.path.exists(SCREAMING_SNAKE_CASE__     ):
                    with io.open(SCREAMING_SNAKE_CASE__     ,							'r'     ,							encoding='utf-8'     ) as f:
                           a_					:		Any							=  json.load(SCREAMING_SNAKE_CASE__     )
             else:
                    try:
                           a_					:		int							=  baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE__     ).decode('utf-8'     )
                           a_					:		str							=  json.loads(SCREAMING_SNAKE_CASE__     )
                    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}"""     )

             a_					:		Any							=  config

             self.set_stage_and_offload()
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             # 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.
             a_					:		Tuple							=  self.get_value('zero_optimization.stage'     ,							-1     )

             # offload
             a_					:		Union[str, Any]							=  False
             if self.is_zeroa() or self.is_zeroa():
                    a_					:		List[Any]							=  set(['cpu', 'nvme']     )
                    a_					:		List[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:
                           a_					:		Optional[Any]							=  True
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			List[str]:
             a_					:		Union[str, Any]							=  self.config

             # find the config node of interest if it exists
             a_					:		Dict							=  ds_key_long.split('.'     )
             a_					:		str							=  nodes.pop()
             for node in nodes:
                    a_					:		Optional[int]							=  config.get(SCREAMING_SNAKE_CASE__     )
                    if config is None:
                           return None, ds_key

             return config, ds_key
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     )		->			Dict:
             a_							,		a_					:		Dict							=  self.find_config_node(SCREAMING_SNAKE_CASE__     )
             if config is None:
                    return default
             return config.get(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int=False     )		->			Optional[int]:
             a_					:		Optional[Any]							=  self.config

             # find the config node of interest if it exists
             a_					:		Tuple							=  ds_key_long.split('.'     )
             for node in nodes:
                    a_					:		Any							=  config
                    a_					:		Optional[Any]							=  config.get(SCREAMING_SNAKE_CASE__     )
                    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(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			str:
             a_					:		Union[str, Any]							=  self.get_value(SCREAMING_SNAKE_CASE__     )
             return False if value is None else bool(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[Any]:
             a_					:		List[Any]							=  self.get_value(SCREAMING_SNAKE_CASE__     )
             return False if value is None else not bool(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             return self._stage == 2
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             return self._stage == 3

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             return self._offload
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Union[str, Any]:
             a_					:		Optional[int]							=  engine

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Dict:
             # runs backpropagation and handles mixed precision
             self.engine.backward(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             # 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  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Dict:
             super().__init__(SCREAMING_SNAKE_CASE__     ,							device_placement=SCREAMING_SNAKE_CASE__     ,							scaler=SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  hasattr(self.optimizer     ,							'overflow'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     )		->			List[Any]:
             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			List[str]:
             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Any:
             if self.__has_overflow__:
                    return self.optimizer.overflow
             return False
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			List[str]:
             super().__init__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Union[str, Any]:
             pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.001     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			Optional[Any]:
             a_					:		Tuple							=  params
             a_					:		Optional[int]							=  lr
             a_					:		List[str]							=  weight_decay
             a_					:		Optional[Any]							=  kwargs
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Dict=0     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			Dict:
             a_					:		Optional[int]							=  optimizer
             a_					:		int							=  total_num_steps
             a_					:		Optional[Any]							=  warmup_num_steps
             a_					:		int							=  kwargs




import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case								=  get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[str]				       =XLMProphetNetTokenizer
	A__    :   List[Any]				       =False
	A__    :   Tuple				       =True
	def      A_				(			self							:   str							):
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)
						tokenizer.save_pretrained(self.tmpdirname							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= '[PAD]'
						SCREAMING_SNAKE_CASE__													= 0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= list(self.get_tokenizer().get_vocab().keys()							)

						self.assertEqual(vocab_keys[0]       ,   '[PAD]'							)
						self.assertEqual(vocab_keys[1]       ,   '[CLS]'							)
						self.assertEqual(vocab_keys[-1]       ,   'j'							)
						self.assertEqual(len(UpperCAmelCase_							)       ,   1012							)
	def      A_				(			self							:   List[Any]							):
						self.assertEqual(self.get_tokenizer().vocab_size       ,   1012							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('This is a test'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['▁This', '▁is', '▁a', '▁t', 'est']							)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '9',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        'é',
						        '.',
						    ]       ,   )
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.convert_ids_to_tokens(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '[UNK]',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        '[UNK]',
						        '.',
						    ]       ,   )
	@cached_property
	def      A_				(			self							:   Optional[Any]							):
						return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
	@slow
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= 'Hello World!'
						SCREAMING_SNAKE_CASE__													= [35389, 6672, 49, 2]
						self.assertListEqual(UpperCAmelCase_       ,   self.big_tokenizer.encode(UpperCAmelCase_							)							)





	@slow
	def      A_				(			self							:   Tuple							):
						# fmt: off
						SCREAMING_SNAKE_CASE__													= {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCAmelCase_       ,   model_name='microsoft/xprophetnet-large-wiki100-cased'       ,   revision='1acad1643ddd54a44df6a1b797ada8373685d90e'       ,   )





