

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        _lowerCAmelCase     (      self     :							List[Any]			,snake_case     :							List[Any]			,snake_case     :							int			,snake_case     :							int				):
           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        _lowerCAmelCase     (      self     :							Optional[int]				):
           SCREAMING_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        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=None
           ops.enable_eager_execution_internal()
           SCREAMING_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()]				)
           SCREAMING_SNAKE_CASE      						=tf.config.list_logical_devices(device_type='CPU'				)
           SCREAMING_SNAKE_CASE      						=tf.distribute.MirroredStrategy(devices=devices[:2]				)

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

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

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

           @tf.function
           def accumulate(snake_case     :							Optional[Any]			,snake_case     :							Tuple				):
                with strategy.scope():
                     SCREAMING_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     :							List[Any]			,snake_case     :							Optional[int]				):
                SCREAMING_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]				)






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



