






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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Optional[Any],      snake_case	:Tuple,      snake_case	:List[str]):



          """simple docstring"""

          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 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =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 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =None
          ops.enable_eager_execution_internal()
          _lowercase              =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()])
          _lowercase              =tf.config.list_logical_devices(device_type='CPU')
          _lowercase              =tf.distribute.MirroredStrategy(devices=devices[:2])

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

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

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

          @tf.function
          def accumulate(snake_case	:Optional[int],      snake_case	:int):
             with strategy.scope():
                _lowercase              =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	:Dict,      snake_case	:Optional[Any]):
             _lowercase              =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])










# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          if not self.directed:  # For undirected graphs
             # if both source vertex and destination vertex are both present in the
             # adjacency list, add destination vertex to source vertex list of adjacent
             # vertices and add source vertex to destination vertex list of adjacent
             # vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination vertex
             # to source vertex list of adjacent vertices, then create a new vertex with
             # destination vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[source_vertex]
             # if only destination vertex is present in adjacency list, add source vertex
             # to destination vertex list of adjacent vertices, then create a new vertex
             # with source vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(snake_case)
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and assign a list
             # containing the destination vertex as it's first adjacent vertex also
             # create a new vertex with destination vertex as key and assign a list
             # containing the source vertex as it's first adjacent vertex.
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[source_vertex]
          else:  # For directed graphs
             # if both source vertex and destination vertex are present in adjacency
             # list, add destination vertex to source vertex list of adjacent vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination
             # vertex to source vertex list of adjacent vertices and create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[]
             # if only destination vertex is present in adjacency list, create a new
             # vertex with source vertex as key and assign a list containing destination
             # vertex as first adjacent vertex
             elif destination_vertex in self.adj_list:
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and a list containing
             # destination vertex as it's first adjacent vertex. Then create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[]

          return self





       def __repr__(				self	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


