"""simple docstring"""




import os
import sys
import unittest


__lowerCAmelCase    :							Union[str, Any]													=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCAmelCase    :							List[Any]													=						os.path.join(git_repo_path, "src", "transformers")

__lowerCAmelCase    :							List[str]													=						"\n{0} = None\n"

__lowerCAmelCase    :							Optional[int]													=						"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__lowerCAmelCase    :							Optional[Any]													=						"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""     )
							self.assertIsNone(snake_case__     )

							lowerCAmelCase__        =   find_backend("""    if not is_tokenizers_available():"""     )
							self.assertEqual(snake_case__  ,						"""tokenizers"""     )

							lowerCAmelCase__        =   find_backend("""    if not is_tensorflow_text_available():"""     )
							self.assertEqual(snake_case__  ,						"""tensorflow_text"""     )

							lowerCAmelCase__        =   find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""     )
							self.assertEqual(snake_case__  ,						"""sentencepiece_and_tokenizers"""     )

							lowerCAmelCase__        =   find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""     )
							self.assertEqual(snake_case__  ,						"""sentencepiece_and_tensorflow_text"""     )

							lowerCAmelCase__        =   find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""     )
							self.assertEqual(snake_case__  ,						"""sentencepiece_and_tokenizers_and_vision"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""  ,						snake_case__     )
							self.assertIn("""tensorflow_text"""  ,						snake_case__     )
							self.assertIn("""sentencepiece_and_tokenizers"""  ,						snake_case__     )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""  ,						objects["""torch"""]     )
							self.assertIn("""TFBertModel"""  ,						objects["""tf"""]     )
							self.assertIn("""FlaxBertModel"""  ,						objects["""flax"""]     )
							self.assertIn("""BertModel"""  ,						objects["""torch"""]     )
							self.assertIn("""TFBertTokenizer"""  ,						objects["""tensorflow_text"""]     )
							self.assertIn("""convert_slow_tokenizer"""  ,						objects["""sentencepiece_and_tokenizers"""]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   create_dummy_object("""CONSTANT"""  ,						"""'torch'"""     )
							self.assertEqual(snake_case__  ,						"""\nCONSTANT = None\n"""     )

							lowerCAmelCase__        =   create_dummy_object("""function"""  ,						"""'torch'"""     )
							self.assertEqual(
							    snake_case__  ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""     )

							lowerCAmelCase__        =   """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							lowerCAmelCase__        =   create_dummy_object("""FakeClass"""  ,						"""'torch'"""     )
							self.assertEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							lowerCAmelCase__        =   create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}     )
							self.assertEqual(dummy_files["""torch"""]  ,						snake_case__     )





"""simple docstring"""




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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

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

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

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




