

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 			lowercase    (	unittest.TestCase      ):




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




				def    a							( self	):
											snake_case_          =   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(snake_case	):
																		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	):
											snake_case_          =   None
											ops.enable_eager_execution_internal()
											snake_case_          =   tf.config.list_physical_devices('CPU'	)
											if len(snake_case	) == 1:
																		tf.config.set_logical_device_configuration(
																		    physical_devices[0]				,       [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]	)
											snake_case_          =   tf.config.list_logical_devices(device_type='CPU'	)
											snake_case_          =   tf.distribute.MirroredStrategy(devices=devices[:2]	)

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

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

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

											@tf.function
											def accumulate(snake_case				,       snake_case	):
																		with strategy.scope():
																									snake_case_          =   strategy.experimental_local_results(snake_case	)
																									local_variables[0].assign(snake_case	)
																									local_variables[1].assign(snake_case	)
																									strategy.run(snake_case				,       args=(gradient_placeholder,)	)

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

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


_UpperCAmelCase		:       int						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''roformer'''




				def __init__( self				,       snake_case=5_0000				,       snake_case=None				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1536				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=False				,       snake_case=True				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       **snake_case	)

											snake_case_          =   vocab_size
											snake_case_          =   hidden_size if embedding_size is None else embedding_size
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_act
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_vocab_size
											snake_case_          =   initializer_range
											snake_case_          =   layer_norm_eps
											snake_case_          =   rotary_value
											snake_case_          =   use_cache







class 			lowercase    (	lowercase_      ):




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




