

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 SCREAMING_SNAKE_CASE  (		tf.keras.optimizers.schedules.LearningRateSchedule							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = None ,				):

				"""simple docstring"""


				super().__init__()
				snake_case_      		= initial_learning_rate
				snake_case_      		= warmup_steps
				snake_case_      		= power
				snake_case_      		= decay_schedule_fn
				snake_case_      		= name


			def __call__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				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`.
					snake_case_      		= tf.cast(__UpperCamelCase ,				tf.floataa							)
					snake_case_      		= tf.cast(self.warmup_steps ,				tf.floataa							)
					snake_case_      		= global_step_float / warmup_steps_float
					snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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 a(lowercase__			, lowercase__			, lowercase__			, lowercase__ = 0.0			, lowercase__ = 0.9			, lowercase__ = 0.999			, lowercase__ = 1e-8			, lowercase__ = None			, lowercase__ = None			, lowercase__ = 0.0			, lowercase__ = 1.0			, lowercase__ = None			, ):





	'''simple docstring'''
	snake_case_      		= tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=lowercase__			, decay_steps=num_train_steps - num_warmup_steps			, end_learning_rate=init_lr * min_lr_ratio			, power=lowercase__			, )
	if num_warmup_steps:
		snake_case_      		= WarmUp(
		    initial_learning_rate=lowercase__			, decay_schedule_fn=lowercase__			, warmup_steps=lowercase__			, )
	if weight_decay_rate > 0.0:
		snake_case_      		= AdamWeightDecay(
		    learning_rate=lowercase__			, weight_decay_rate=lowercase__			, beta_a=lowercase__			, beta_a=lowercase__			, epsilon=lowercase__			, clipnorm=lowercase__			, global_clipnorm=lowercase__			, exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']			, include_in_weight_decay=lowercase__			, )
	else:
		snake_case_      		= tf.keras.optimizers.Adam(
		    learning_rate=lowercase__			, beta_a=lowercase__			, beta_a=lowercase__			, epsilon=lowercase__			, clipnorm=lowercase__			, global_clipnorm=lowercase__			, )
	# 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 SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase = 0.001 ,				__UpperCamelCase = 0.9 ,				__UpperCamelCase = 0.999 ,				__UpperCamelCase = 1E-7 ,				__UpperCamelCase = False ,				__UpperCamelCase = 0.0 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "AdamWeightDecay" ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				**__UpperCamelCase							)
				snake_case_      		= weight_decay_rate
				snake_case_      		= include_in_weight_decay
				snake_case_      		= exclude_from_weight_decay


			@classmethod
			def 			__lowerCAmelCase (    cls ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {'WarmUp': WarmUp}
				return super(__UpperCamelCase ,				cls							).from_config(__UpperCamelCase ,				custom_objects=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				super(__UpperCamelCase ,				self							)._prepare_local(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)
				snake_case_      		= tf.constant(
				    self.weight_decay_rate ,				name='adam_weight_decay_rate'							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= list(zip(*__UpperCamelCase							)							)
				return super(__UpperCamelCase ,				self							).apply_gradients(zip(__UpperCamelCase ,				__UpperCamelCase							) ,				name=__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				if apply_state is None:
					return self._decayed_lr_t[var_dtype], {}

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

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self._get_lr(var.device ,				var.dtype.base_dtype ,				__UpperCamelCase							)
				snake_case_      		= self._decay_weights_op(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)
				with tf.control_dependencies([decay]							):
					return super(__UpperCamelCase ,				self							)._resource_apply_dense(__UpperCamelCase ,				__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self._get_lr(var.device ,				var.dtype.base_dtype ,				__UpperCamelCase							)
				snake_case_      		= self._decay_weights_op(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)
				with tf.control_dependencies([decay]							):
					return super(__UpperCamelCase ,				self							)._resource_apply_sparse(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= super().get_config()
				config.update({'weight_decay_rate': self.weight_decay_rate}							)
				return config







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				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 SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self							):

				"""simple docstring"""


				snake_case_      		= []
				snake_case_      		= None


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if self._accum_steps is None:
					snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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							):

				"""simple docstring"""


				if not self._gradients:
					snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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							)							)



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= TapasModel(config=lowercase__	)
	else:
		raise ValueError(f"""Task {task} not supported."""	)

	print(f"""Building PyTorch model from configuration: {config}"""	)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(lowercase__			, lowercase__			, lowercase__	)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(lowercase__	)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""	)
	snake_case_      		= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'			, model_max_length=512	)
	tokenizer.save_pretrained(lowercase__	)

	print('Used relative position embeddings:'			, model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
		)
		parser.add_argument(
		    '--reset_position_index_per_cell',
		    default=False,
		    action='store_true',
		    help='Whether to use relative position embeddings or not. Defaults to True.',
		)
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--tapas_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained TAPAS model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		A											=     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
