
'''simple docstring'''



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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												self.assertEqual(len(_lowercase )       ,  len(_lowercase ) )
												for a, b in zip(_lowercase       ,  _lowercase ):
																		self.assertAlmostEqual(_lowercase       ,  _lowercase       ,  delta=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					None
												ops.enable_eager_execution_internal()
												_lowerCAmelCase    		=					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()] )
												_lowerCAmelCase    		=					tf.config.list_logical_devices(device_type="""CPU""" )
												_lowerCAmelCase    		=					tf.distribute.MirroredStrategy(devices=devices[:2] )

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

												def accumulate_on_replica(_lowercase ):
																		accumulator([gradient] )

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

												@tf.function
												def accumulate(_lowercase       ,  _lowercase ):
																		with strategy.scope():
																								_lowerCAmelCase    		=					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       ,  _lowercase ):
																		_lowerCAmelCase    		=					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] )






"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      '''xmod'''

 def __init__(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]=30_522       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Dict=3_072       ,				_UpperCAmelCase			:			Union[str, Any]="gelu"       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[int]=0.02       ,				_UpperCAmelCase			:			Any=1E-1_2       ,				_UpperCAmelCase			:			Tuple=1       ,				_UpperCAmelCase			:			int=0       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			List[str]="absolute"       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			List[Any]=False       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=("en_XX",)       ,				_UpperCAmelCase			:			List[str]=None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout
       _A     =  pre_norm
       _A     =  adapter_reduction_factor
       _A     =  adapter_layer_norm
       _A     =  adapter_reuse_layer_norm
       _A     =  ln_before_adapter
       _A     =  list(_UpperCAmelCase						)
       _A     =  default_language





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)

