
'''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] )




from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''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 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

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



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





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