


"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 	A_ (	snake_case_   ):




      '''simple docstring'''


      __snake_case 	=       """camembert"""




      def __init__(    self:	Union[str, Any]						,				a:	Tuple=3_0522						,				a:	Optional[int]=768						,				a:	Optional[Any]=12						,				a:	List[str]=12						,				a:	Optional[Any]=3072						,				a:	Optional[int]="gelu"						,				a:	List[str]=0.1						,				a:	Dict=0.1						,				a:	List[Any]=512						,				a:	Optional[Any]=2						,				a:	List[str]=0.0_2						,				a:	Optional[Any]=1e-12						,				a:	str=1						,				a:	int=0						,				a:	int=2						,				a:	Union[str, Any]="absolute"						,				a:	Optional[int]=True						,				a:	Optional[int]=None						,				**a:	int						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

       __lowerCamelCase		:							Optional[int]		 =						vocab_size
       __lowerCamelCase		:							Tuple		 =						hidden_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							str		 =						num_attention_heads
       __lowerCamelCase		:							Dict		 =						hidden_act
       __lowerCamelCase		:							Union[str, Any]		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						hidden_dropout_prob
       __lowerCamelCase		:							int		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							Tuple		 =						max_position_embeddings
       __lowerCamelCase		:							Union[str, Any]		 =						type_vocab_size
       __lowerCamelCase		:							Union[str, Any]		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						position_embedding_type
       __lowerCamelCase		:							str		 =						use_cache
       __lowerCamelCase		:							List[Any]		 =						classifier_dropout



class 	A_ (	snake_case_   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       if self.task == "multiple-choice":
        __lowerCamelCase		:							List[str]		 =						{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
        __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', 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 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Optional[int]						,				a:	int						,				a:	List[Any]						,				a:	Any    ):
       self.assertEqual(len(a    )						,				len(a    )    )
       for a, b in zip(a						,				a    ):
        self.assertAlmostEqual(a						,				a						,				delta=a    )




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

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

       def accumulate_on_replica(a:	str    ):
        accumulator([gradient]    )

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

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

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

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

