



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						List[str],					__lowercase					:						str,					__lowercase					:						Tuple,					__lowercase					:						Optional[int]						):
							self.assertEqual(len(__lowercase						),					len(__lowercase						)						)
							for a, b in zip(__lowercase,					__lowercase						):
										self.assertAlmostEqual(__lowercase,					__lowercase,					delta=__lowercase						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				GradientAccumulator()
							accumulator([tf.constant([1.0, 2.0]						)]						)
							accumulator([tf.constant([-2.0, 1.0]						)]						)
							accumulator([tf.constant([-1.0, 2.0]						)]						)
							with self.assertRaises(__lowercase						):
										accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
							self.assertEqual(accumulator.step,					3						)
							self.assertEqual(len(accumulator.gradients						),					1						)
							self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(),					[-2.0, 5.0],					tol=1e-2						)
							accumulator.reset()
							self.assertEqual(accumulator.step,					0						)
							self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(),					[0.0, 0.0],					tol=1e-2						)




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				None
							ops.enable_eager_execution_internal()
							lowercase__					  =				tf.config.list_physical_devices("CPU"						)
							if len(__lowercase						) == 1:
										tf.config.set_logical_device_configuration(
										    physical_devices[0],					[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
							lowercase__					  =				tf.config.list_logical_devices(device_type="CPU"						)
							lowercase__					  =				tf.distribute.MirroredStrategy(devices=devices[:2]						)

							with strategy.scope():
										lowercase__					  =				GradientAccumulator()
										lowercase__					  =				tf.Variable([4.0, 3.0]						)
										lowercase__  ,       lowercase__					  =				create_optimizer(5e-5,					10,					5						)
										lowercase__					  =				tf.Variable([0.0, 0.0],					trainable=__lowercase						)

							def accumulate_on_replica(__lowercase					:						Dict						):
										accumulator([gradient]						)

							def apply_on_replica():
										optimizer.apply_gradients(list(zip(accumulator.gradients,					[variable]						)						)						)

							@tf.function
							def accumulate(__lowercase					:						List[Any],					__lowercase					:						Tuple						):
										with strategy.scope():
													lowercase__					  =				strategy.experimental_local_results(__lowercase						)
													local_variables[0].assign(__lowercase						)
													local_variables[1].assign(__lowercase						)
													strategy.run(__lowercase,					args=(gradient_placeholder,)						)

							@tf.function
							def apply_grad():
										with strategy.scope():
													strategy.run(__lowercase						)

							def _check_local_values(__lowercase					:						int,					__lowercase					:						int						):
										lowercase__					  =				strategy.experimental_local_results(accumulator._gradients[0]						)
										self.assertListAlmostEqual(values[0].value(),					__lowercase,					tol=1e-2						)
										self.assertListAlmostEqual(values[1].value(),					__lowercase,					tol=1e-2						)

							accumulate([1.0, 2.0],					[-1.0, 1.0]						)
							accumulate([3.0, -1.0],					[-1.0, -1.0]						)
							accumulate([-2.0, 2.0],					[3.0, -2.0]						)
							self.assertEqual(accumulator.step,					3						)
							_check_local_values([2.0, 3.0],					[1.0, -2.0]						)
							apply_grad()
							self.assertListAlmostEqual(variable.value(),					[4.0, 3.0],					tol=1e-2						)
							accumulator.reset()
							self.assertEqual(accumulator.step,					0						)
							_check_local_values([0.0, 0.0],					[0.0, 0.0]						)









import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Optional[Any],					__lowercase					:						str = "▁",					__lowercase					:						bool = True,					__lowercase					:						Union[str, AddedToken] = "<unk>",					__lowercase					:						Union[str, AddedToken] = "</s>",					__lowercase					:						Union[str, AddedToken] = "<pad>",					):
							lowercase__					  =				{
							    "pad": {"id": 0, "token": pad_token},
							    "eos": {"id": 1, "token": eos_token},
							    "unk": {"id": 2, "token": unk_token},
							}

							lowercase__					  =				[None] * len(self.special_tokens						)
							for token_dict in self.special_tokens.values():
										lowercase__					  =				token_dict["token"]

							lowercase__					  =				Tokenizer(Unigram()						)

							lowercase__					  =				normalizers.Sequence(
							    [
							        normalizers.Nmt(),
							        normalizers.NFKC(),
							        normalizers.Replace(Regex(" {2,}"						),					" "						),
							        normalizers.Lowercase(),
							    ]						)
							lowercase__					  =				pre_tokenizers.Sequence(
							    [
							        pre_tokenizers.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						),
							        pre_tokenizers.Digits(individual_digits=__lowercase						),
							        pre_tokenizers.Punctuation(),
							    ]						)
							lowercase__					  =				decoders.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						)

							lowercase__					  =				TemplateProcessing(
							    single=F'''$A {self.special_tokens["eos"]["token"]}''',					special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],					)

							lowercase__					  =				{
							    "model": "SentencePieceUnigram",
							    "replacement": replacement,
							    "add_prefix_space": add_prefix_space,
							}

							super().__init__(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, List[str]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				[files]
							self._tokenizer.train(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[Iterator[str], Iterator[Iterator[str]]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							self._tokenizer.train_from_iterator(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						str						):
							lowercase__					  =				json.loads(self._tokenizer.to_str()						)

							lowercase__					  =				self.special_tokens["unk"]["id"]

							lowercase__					  =				Tokenizer.from_str(json.dumps(__lowercase						)						)




