



from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """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 	lowerCamelCase_    ( _snake_case						):






						'''simple docstring'''
						a__   =  "roformer"







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			List[str]=5_00_00	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			Union[str, Any]=7_68	,	__lowerCamelCase      :			Dict=12	,	__lowerCamelCase      :			int=12	,	__lowerCamelCase      :			Tuple=30_72	,	__lowerCamelCase      :			Tuple="gelu"	,	__lowerCamelCase      :			str=0.1	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Dict=15_36	,	__lowerCamelCase      :			Dict=2	,	__lowerCamelCase      :			Any=0.02	,	__lowerCamelCase      :			Tuple=1e-12	,	__lowerCamelCase      :			Any=0	,	__lowerCamelCase      :			List[Any]=False	,	__lowerCamelCase      :			Tuple=True	,	**__lowerCamelCase      :			int	,	)      ->				Dict:
									super().__init__(pad_token_id=lowerCAmelCase__	,	**lowerCAmelCase__ )

									A							:					List[Any]              =						vocab_size
									A							:					Dict              =						hidden_size if embedding_size is None else embedding_size
									A							:					str              =						hidden_size
									A							:					Optional[int]              =						num_hidden_layers
									A							:					str              =						num_attention_heads
									A							:					List[str]              =						hidden_act
									A							:					List[Any]              =						intermediate_size
									A							:					List[str]              =						hidden_dropout_prob
									A							:					Any              =						attention_probs_dropout_prob
									A							:					Optional[int]              =						max_position_embeddings
									A							:					Optional[int]              =						type_vocab_size
									A							:					Any              =						initializer_range
									A							:					int              =						layer_norm_eps
									A							:					Optional[Any]              =						rotary_value
									A							:					str              =						use_cache







class 	lowerCamelCase_    ( _snake_case						):






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
												A							:					Optional[Any]              =						{0: "batch", 1: "choice", 2: "sequence"}
									else:
												A							:					Any              =						{0: "batch", 1: "sequence"}
									A							:					Any              =						{0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ] )




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 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[int]:
									self.assertEqual(len(__lowerCamelCase )	,	len(__lowerCamelCase ) )
									for a, b in zip(__lowerCamelCase	,	__lowerCamelCase ):
												self.assertAlmostEqual(__lowerCamelCase	,	__lowerCamelCase	,	delta=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[Any]              =						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(__lowerCamelCase ):
												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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Union[str, Any]              =						None
									ops.enable_eager_execution_internal()
									A							:					Tuple              =						tf.config.list_physical_devices("CPU" )
									if len(__lowerCamelCase ) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]	,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
									A							:					Dict              =						tf.config.list_logical_devices(device_type="CPU" )
									A							:					List[str]              =						tf.distribute.MirroredStrategy(devices=devices[:2] )

									with strategy.scope():
												A							:					Optional[int]              =						GradientAccumulator()
												A							:					Tuple              =						tf.Variable([4.0, 3.0] )
												A       , A							:					List[Any]              =						create_optimizer(5e-5	,	10	,	5 )
												A							:					List[str]              =						tf.Variable([0.0, 0.0]	,	trainable=__lowerCamelCase )

									def accumulate_on_replica(__lowerCamelCase      :			Tuple ):
												accumulator([gradient] )

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

									@tf.function
									def accumulate(__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[int] ):
												with strategy.scope():
															A							:					int              =						strategy.experimental_local_results(__lowerCamelCase )
															local_variables[0].assign(__lowerCamelCase )
															local_variables[1].assign(__lowerCamelCase )
															strategy.run(__lowerCamelCase	,	args=(gradient_placeholder,) )

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

									def _check_local_values(__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any] ):
												A							:					Optional[int]              =						strategy.experimental_local_results(accumulator._gradients[0] )
												self.assertListAlmostEqual(values[0].value()	,	__lowerCamelCase	,	tol=1e-2 )
												self.assertListAlmostEqual(values[1].value()	,	__lowerCamelCase	,	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] )