"""simple docstring"""




import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
				from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
				from tensorflow.keras.optimizers import Adam
class     UpperCamelCase__(			tf.keras.optimizers.schedules.LearningRateSchedule ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 1.0		,__UpperCAmelCase = None		,)					->				Dict:
													super().__init__()
													A__	     =							initial_learning_rate
													A__	     =							warmup_steps
													A__	     =							power
													A__	     =							decay_schedule_fn
													A__	     =							name





						def __call__(       self		,__UpperCAmelCase     )					->				int:
													with tf.name_scope(self.name or 'WarmUp'     ) as name:
																				# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
																				# learning rate will be `global_step/num_warmup_steps * init_lr`.
																				A__	     =							tf.cast(__UpperCAmelCase		,tf.floataa     )
																				A__	     =							tf.cast(self.warmup_steps		,tf.floataa     )
																				A__	     =							global_step_float / warmup_steps_float
																				A__	     =							self.initial_learning_rate * tf.math.pow(__UpperCAmelCase		,self.power     )
																				return tf.cond(
																				    global_step_float < warmup_steps_float		,lambda: warmup_learning_rate		,lambda: self.decay_schedule_fn(step - self.warmup_steps     )		,name=__UpperCAmelCase		,)





						def 				snake_case__	(       self     )					->				List[str]:
													return {
													    "initial_learning_rate": self.initial_learning_rate,
													    "decay_schedule_fn": self.decay_schedule_fn,
													    "warmup_steps": self.warmup_steps,
													    "power": self.power,
													    "name": self.name,
													}






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 0.0       ,    UpperCamelCase__ = 0.9       ,    UpperCamelCase__ = 0.9_9_9       ,    UpperCamelCase__ = 1E-8       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = 0.0       ,    UpperCamelCase__ = 1.0       ,    UpperCamelCase__ = None       ,    ):






							"""simple docstring"""





							A__	     =							tf.keras.optimizers.schedules.PolynomialDecay(
							    initial_learning_rate=UpperCamelCase__       ,    decay_steps=num_train_steps - num_warmup_steps       ,    end_learning_rate=init_lr * min_lr_ratio       ,    power=UpperCamelCase__       ,    )
							if num_warmup_steps:
														A__	     =							WarmUp(
														    initial_learning_rate=UpperCamelCase__       ,    decay_schedule_fn=UpperCamelCase__       ,    warmup_steps=UpperCamelCase__       ,    )
							if weight_decay_rate > 0.0:
														A__	     =							AdamWeightDecay(
														    learning_rate=UpperCamelCase__       ,    weight_decay_rate=UpperCamelCase__       ,    beta_a=UpperCamelCase__       ,    beta_a=UpperCamelCase__       ,    epsilon=UpperCamelCase__       ,    clipnorm=UpperCamelCase__       ,    global_clipnorm=UpperCamelCase__       ,    exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']       ,    include_in_weight_decay=UpperCamelCase__       ,    )
							else:
														A__	     =							tf.keras.optimizers.Adam(
														    learning_rate=UpperCamelCase__       ,    beta_a=UpperCamelCase__       ,    beta_a=UpperCamelCase__       ,    epsilon=UpperCamelCase__       ,    clipnorm=UpperCamelCase__       ,    global_clipnorm=UpperCamelCase__       ,    )
							# We return the optimizer and the LR scheduler in order to better track the
							# evolution of the LR independently of the optimizer.
							return optimizer, lr_schedule
class     UpperCamelCase__(			__A ):





						def __init__(       self		,__UpperCAmelCase = 0.0_0_1		,__UpperCAmelCase = 0.9		,__UpperCAmelCase = 0.9_9_9		,__UpperCAmelCase = 1e-7		,__UpperCAmelCase = False		,__UpperCAmelCase = 0.0		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = "AdamWeightDecay"		,**__UpperCAmelCase		,)					->				Tuple:
													super().__init__(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							weight_decay_rate
													A__	     =							include_in_weight_decay
													A__	     =							exclude_from_weight_decay





						@classmethod
						def 				snake_case__	(       cls		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							{'WarmUp': WarmUp}
													return super(__UpperCAmelCase		,cls     ).from_config(__UpperCAmelCase		,custom_objects=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													super(__UpperCAmelCase		,self     )._prepare_local(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							tf.constant(
													    self.weight_decay_rate		,name='adam_weight_decay_rate'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				str:
													A__	     =							self._do_use_weight_decay(var.name     )
													if do_decay:
																				return var.assign_sub(
																				    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']		,use_locking=self._use_locking		,)
													return tf.no_op()





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Tuple:
													A__     ,  A__	     =							list(zip(*__UpperCAmelCase     )     )
													return super(__UpperCAmelCase		,self     ).apply_gradients(zip(__UpperCAmelCase		,__UpperCAmelCase     )		,name=__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
													if apply_state is None:
																				return self._decayed_lr_t[var_dtype], {}

													A__	     =							apply_state or {}
													A__	     =							apply_state.get((var_device, var_dtype)     )
													if coefficients is None:
																				A__	     =							self._fallback_apply_state(__UpperCAmelCase		,__UpperCAmelCase     )
																				A__	     =							coefficients

													return coefficients["lr_t"], {"apply_state": apply_state}





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None     )					->				List[Any]:
													A__     ,  A__	     =							self._get_lr(var.device		,var.dtype.base_dtype		,__UpperCAmelCase     )
													A__	     =							self._decay_weights_op(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
													with tf.control_dependencies([decay]     ):
																				return super(__UpperCAmelCase		,self     )._resource_apply_dense(__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None     )					->				Optional[int]:
													A__     ,  A__	     =							self._get_lr(var.device		,var.dtype.base_dtype		,__UpperCAmelCase     )
													A__	     =							self._decay_weights_op(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
													with tf.control_dependencies([decay]     ):
																				return super(__UpperCAmelCase		,self     )._resource_apply_sparse(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							super().get_config()
													config.update({'weight_decay_rate': self.weight_decay_rate}     )
													return config





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Any:
													if self.weight_decay_rate == 0:
																				return False

													if self._include_in_weight_decay:
																				for r in self._include_in_weight_decay:
																											if re.search(__UpperCAmelCase		,__UpperCAmelCase     ) is not None:
																																		return True

													if self._exclude_from_weight_decay:
																				for r in self._exclude_from_weight_decay:
																											if re.search(__UpperCAmelCase		,__UpperCAmelCase     ) is not None:
																																		return False
													return True






class     UpperCamelCase__(			__A ):





						def __init__(       self     )					->				Dict:
													A__	     =							[]
													A__	     =							None





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													if self._accum_steps is None:
																				A__	     =							tf.Variable(
																				    tf.constant(0		,dtype=tf.intaa     )		,trainable=__UpperCAmelCase		,synchronization=tf.VariableSynchronization.ON_READ		,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA		,)

													return self._accum_steps.value()





						@property
						def 				snake_case__	(       self     )					->				int:
													if not self._gradients:
																				raise ValueError('The accumulator should be called first to initialize the gradients'     )
													return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]





						def __call__(       self		,__UpperCAmelCase     )					->				Union[str, Any]:
													if not self._gradients:
																				A__	     =							self.step  # Create the step variable.
																				self._gradients.extend(
																				    [
																				        tf.Variable(
																				            tf.zeros_like(__UpperCAmelCase     )		,trainable=__UpperCAmelCase		,synchronization=tf.VariableSynchronization.ON_READ		,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA		,)
																				        if gradient is not None
																				        else gradient
																				        for gradient in gradients
																				    ]     )
													if len(__UpperCAmelCase     ) != len(self._gradients     ):
																				raise ValueError(f'''Expected {len(self._gradients     )} gradients, but got {len(__UpperCAmelCase     )}'''     )

													for accum_gradient, gradient in zip(self._gradients		,__UpperCAmelCase     ):
																				if accum_gradient is not None and gradient is not None:
																											accum_gradient.assign_add(__UpperCAmelCase     )

													self._accum_steps.assign_add(1     )





						def 				snake_case__	(       self     )					->				str:
													if not self._gradients:
																				return
													self._accum_steps.assign(0     )
													for gradient in self._gradients:
																				if gradient is not None:
																											gradient.assign(tf.zeros_like(__UpperCAmelCase     )     )






"""simple docstring"""




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
				from transformers import MarkupLMFeatureExtractor
class     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase     )					->				str:
													A__	     =							parent





						def 				snake_case__	(       self     )					->				int:
													return {}






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

							A__	     =							'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

							return [html_string_a, html_string_a]


@require_bsa
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Dict													=		MarkupLMFeatureExtractor if is_bsa_available() else None





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							MarkupLMFeatureExtractionTester(self     )





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return self.feature_extract_tester.prepare_feat_extract_dict()





						def 				snake_case__	(       self     )					->				Any:
													# Initialize feature_extractor
													A__	     =							self.feature_extraction_class()

													# Test not batched input
													A__	     =							get_html_strings()[0]
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
													A__	     =							[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
													# fmt: on

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )

													# Test batched
													A__	     =							get_html_strings()
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							expected_nodes + [['My First Heading', 'My first paragraph.']]
													A__	     =							expected_xpaths + [['/html/body/h1', '/html/body/p']]

													self.assertEqual(len(encoding.nodes     )		,2     )
													self.assertEqual(len(encoding.xpaths     )		,2     )

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )





