"""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 		UpperCamelCase      (   unittest.TestCase						):



    def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Optional[Any]     ,  _A    :				int     ,  _A    :				Union[str, Any])   ->     Union[str, Any]:
     self.assertEqual(len(_A)     ,  len(_A))
     for a, b in zip(_A     ,  _A):
      self.assertAlmostEqual(_A     ,  _A     ,  delta=_A)



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

     with strategy.scope():
      __snake_case      :							Dict						=			GradientAccumulator()
      __snake_case      :							Any						=			tf.Variable([4.0, 3.0])
      __snake_case      :							Dict						=			create_optimizer(5E-5     ,  10     ,  5)
      __snake_case      :							Optional[int]						=			tf.Variable([0.0, 0.0]     ,  trainable=_A)

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

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

     @tf.function
     def accumulate(_A    :				Optional[Any]     ,  _A    :				Union[str, Any]):
      with strategy.scope():
       __snake_case      :							Tuple						=			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    :				Tuple     ,  _A    :				List[Any]):
      __snake_case      :							Optional[Any]						=			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])






"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





	return ConvertCommand(
	    args.model_type      ,							args.tf_checkpoint      ,							args.pytorch_dump_output      ,							args.config      ,							args.finetuning_task_name  )


_a					:					str=			"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			parser.add_parser(
					    'convert'     ,  help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'     ,  )
					train_parser.add_argument('--model_type'     ,  type=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					if self._model_type == "albert":
						try:
							from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "bert":
						try:
							from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "funnel":
						try:
							from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "t5":
						try:
							from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "transfo_xl":
						try:
							from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
							    convert_transfo_xl_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					elif self._model_type == "gpt2":
						try:
							from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
							    convert_gpta_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "xlnet":
						try:
							from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
							    convert_xlnet_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output     ,  self._finetuning_task_name)
					elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					else:
						raise ValueError(
						    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')





