
"""simple docstring"""


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  :   Any	,A_  :   Optional[int]	,A_  :   Optional[Any]	,A_  :   Optional[int]   )  -> Dict:
							self.assertEqual(len(A_   )	,len(A_   )   )
							for a, b in zip(A_	,A_   ):
								self.assertAlmostEqual(A_	,A_	,delta=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							A     = None
							ops.enable_eager_execution_internal()
							A     = 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()]   )
							A     = tf.config.list_logical_devices(device_type='CPU'   )
							A     = tf.distribute.MirroredStrategy(devices=devices[:2]   )

							with strategy.scope():
								A     = GradientAccumulator()
								A     = tf.Variable([4.0, 3.0]   )
								A ,   A     = create_optimizer(5e-5	,10	,5   )
								A     = 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_  :   Dict	,A_  :   Tuple   ):
								with strategy.scope():
									A     = 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_  :   Optional[int]	,A_  :   int   ):
								A     = 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]   )



from typing import Any

def 			lowerCAmelCase__					(     a__:						list     ,    a__:						list     ,    a__:						dict     ,    a__:						dict     ,    a__:						dict     ,    )    ->						list:


							'''simple docstring'''





							_validation(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							# Creates data structures and fill initial step
							_UpperCAmelCase											=     {}
							_UpperCAmelCase											=     {}
							for state in states_space:
														_UpperCAmelCase											=     observations_space[0]
														_UpperCAmelCase											=     (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														_UpperCAmelCase											=     None

							# Fills the data structure with the probabilities of
							# different transitions and pointers to previous states
							for o in range(1     ,    len(a__			)			):
														_UpperCAmelCase											=     observations_space[o]
														_UpperCAmelCase											=     observations_space[o - 1]
														for state in states_space:
																					# Calculates the argmax for probability function
																					_UpperCAmelCase											=     ''
																					_UpperCAmelCase											=     -1
																					for k_state in states_space:
																												_UpperCAmelCase											=     (
																												    probabilities[(k_state, prior_observation)]
																												    * transition_probabilities[k_state][state]
																												    * emission_probabilities[state][observation]
																												)
																												if probability > max_probability:
																																			_UpperCAmelCase											=     probability
																																			_UpperCAmelCase											=     k_state

            # Update probabilities and pointers dicts
																					_UpperCAmelCase											=     (
																					    probabilities[(arg_max, prior_observation)]
																					    * transition_probabilities[arg_max][state]
																					    * emission_probabilities[state][observation]
																					)

																					_UpperCAmelCase											=     arg_max

    # The final observation
							_UpperCAmelCase											=     observations_space[len(a__			) - 1]

							# argmax for given final observation
							_UpperCAmelCase											=     ''
							_UpperCAmelCase											=     -1
							for k_state in states_space:
														_UpperCAmelCase											=     probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					_UpperCAmelCase											=     probability
																					_UpperCAmelCase											=     k_state
							_UpperCAmelCase											=     arg_max

							# Process pointers backwards
							_UpperCAmelCase											=     last_state
							_UpperCAmelCase											=     []
							for o in range(len(a__			) - 1     ,    -1     ,    -1			):
														result.append(a__			)
														_UpperCAmelCase											=     pointers[previous, observations_space[o]]
							result.reverse()

							return result

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_not_empty(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							_validate_lists(a__     ,    a__			)
							_validate_dicts(
							    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ]			):
														raise ValueError('There\'s an empty parameter'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any			)    ->						None:


							'''simple docstring'''





							_validate_list(a__     ,    'observations_space'			)
							_validate_list(a__     ,    'states_space'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a list'''
														raise ValueError(a__			)
							else:
														for x in _object:
																					if not isinstance(a__     ,    a__			):
																												_UpperCAmelCase											=     F'''{var_name} must be a list of strings'''
																												raise ValueError(a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_dict(a__     ,    'initial_probabilities'     ,    a__			)
							_validate_nested_dict(a__     ,    'transition_probabilities'			)
							_validate_nested_dict(a__     ,    'emission_probabilities'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							_validate_dict(_object     ,    a__     ,    a__			)
							for x in _object.values():
														_validate_dict(a__     ,    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str     ,    a__:						type     ,    a__:						bool = False			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a dict'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object			):
														_UpperCAmelCase											=     F'''{var_name} all keys must be strings'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object.values()			):
														_UpperCAmelCase											=     'nested dictionary ' if nested else ''
														_UpperCAmelCase											=     F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a__			)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
