


'''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    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Any:
         super().__init__()
         __lowerCamelCase  :      Optional[Any]														=						initial_learning_rate
         __lowerCamelCase  :      Optional[Any]														=						warmup_steps
         __lowerCamelCase  :      Union[str, Any]														=						power
         __lowerCamelCase  :      Optional[int]														=						decay_schedule_fn
         __lowerCamelCase  :      Any														=						name







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         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`.
              __lowerCamelCase  :      str														=						tf.cast(SCREAMING_SNAKE_CASE_     ,	tf.floataa      )
              __lowerCamelCase  :      Optional[int]														=						tf.cast(self.warmup_steps     ,	tf.floataa      )
              __lowerCamelCase  :      List[Any]														=						global_step_float / warmup_steps_float
              __lowerCamelCase  :      Optional[Any]														=						self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_     ,	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=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	Optional[Any]:
         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_       :							float ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 0.9 ,       UpperCAmelCase_       :							float = 0.999 ,       UpperCAmelCase_       :							float = 1e-8 ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 1.0 ,       UpperCAmelCase_       :							Optional[List[str]] = None ,       )	->						int:
     __lowerCamelCase  :      int														=						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:
          __lowerCamelCase  :      str														=						WarmUp(
              initial_learning_rate=UpperCAmelCase_ ,       decay_schedule_fn=UpperCAmelCase_ ,       warmup_steps=UpperCAmelCase_ ,       )
     if weight_decay_rate > 0.0:
          __lowerCamelCase  :      List[Any]														=						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:
          __lowerCamelCase  :      Tuple														=						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_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_ = 0.0_0_1     ,	SCREAMING_SNAKE_CASE_ = 0.9     ,	SCREAMING_SNAKE_CASE_ = 0.9_9_9     ,	SCREAMING_SNAKE_CASE_ = 1E-7     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "AdamWeightDecay"     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						weight_decay_rate
         __lowerCamelCase  :      str														=						include_in_weight_decay
         __lowerCamelCase  :      List[Any]														=						exclude_from_weight_decay







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Any														=						{'WarmUp': WarmUp}
         return super(SCREAMING_SNAKE_CASE_     ,	cls      ).from_config(SCREAMING_SNAKE_CASE_     ,	custom_objects=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         super(SCREAMING_SNAKE_CASE_     ,	self      )._prepare_local(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tf.constant(
             self.weight_decay_rate     ,	name='adam_weight_decay_rate'      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      Tuple														=						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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						list(zip(*SCREAMING_SNAKE_CASE_      )      )
         return super(SCREAMING_SNAKE_CASE_     ,	self      ).apply_gradients(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )     ,	name=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if apply_state is None:
              return self._decayed_lr_t[var_dtype], {}

         __lowerCamelCase  :      Optional[int]														=						apply_state or {}
         __lowerCamelCase  :      Dict														=						apply_state.get((var_device, var_dtype)      )
         if coefficients is None:
              __lowerCamelCase  :      List[Any]														=						self._fallback_apply_state(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Optional[Any]														=						coefficients

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_dense(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_sparse(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}      )
         return config







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return True

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







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						[]
         __lowerCamelCase  :      Optional[Any]														=						None







    @property
    def  lowercase_							(				self      )							->	List[str]:
         if self._accum_steps is None:
              __lowerCamelCase  :      Tuple														=						tf.Variable(
                  tf.constant(0     ,	dtype=tf.intaa      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)

         return self._accum_steps.value()







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

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

         self._accum_steps.assign_add(1      )







    def  lowercase_							(				self      )							->	int:
         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(SCREAMING_SNAKE_CASE_      )      )












import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCamelCase   :			Tuple          = "."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__UpperCamelCase   :			List[str]          = [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

	with open(os.path.join(__lowerCamelCase				,     """utils"""				,     """tf_ops"""				,     """onnx.json"""       )       ) as f:
		a									=				json.load(__lowerCamelCase       )["""opsets"""]

	for i in range(1				,     opset + 1       ):
		onnx_ops.extend(onnx_opsets[str(__lowerCamelCase       )]       )

	with open(__lowerCamelCase				,     """rb"""       ) as f:
		saved_model.ParseFromString(f.read()       )

	a									=				set()

	# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
	for meta_graph in saved_model.meta_graphs:
		# Add operations in the graph definition
		model_op_names.update(node.op for node in meta_graph.graph_def.node       )

		# Go through the functions in the graph definition
		for func in meta_graph.graph_def.library.function:
			# Add operations in each function
			model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
	a									=				sorted(__lowerCamelCase       )
	a									=				[]

	for op in model_op_names:
		if op not in onnx_ops and op not in INTERNAL_OPS:
			incompatible_ops.append(__lowerCamelCase       )

	if strict and len(__lowerCamelCase       ) > 0:
		raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops       )
	elif len(__lowerCamelCase       ) > 0:
		print(f'Found the following incompatible ops for the opset {opset}:'       )
		print(*__lowerCamelCase				,     sep="""\n"""       )
	else:
		print(f'The saved model {saved_model_path} can properly be converted with ONNX.'       )


if __name__ == "__main__":
		__UpperCamelCase   :			Optional[Any]          = argparse.ArgumentParser()
		parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
		parser.add_argument(
		    "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
		)
		parser.add_argument(
		    "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
		)
		parser.add_argument(
		    "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)

