




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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		Any												=				TapasConfig.from_json_file(_lowerCamelCase				)
		# set absolute/relative position embeddings parameter
		__lowercase				:		Optional[int]												=				reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				__lowercase				:		List[Any]												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "WTQ":
				# run_task_main.py hparams
				__lowercase				:		str												=				4
				__lowercase				:		Optional[Any]												=				True
				# hparam_utils.py hparams
				__lowercase				:		Union[str, Any]												=				0.6_6_4_6_9_4
				__lowercase				:		Tuple												=				0.2_0_7_9_5_1
				__lowercase				:		Any												=				0.1_2_1_1_9_4
				__lowercase				:		List[str]												=				True
				__lowercase				:		List[str]												=				True
				__lowercase				:		List[Any]												=				False
				__lowercase				:		List[str]												=				0.0_3_5_2_5_1_3

				__lowercase				:		Any												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				__lowercase				:		List[str]												=				4
				__lowercase				:		Dict												=				False
				# hparam_utils.py hparams
				__lowercase				:		int												=				36.45_19
				__lowercase				:		Tuple												=				0.9_0_3_4_2_1
				__lowercase				:		Optional[Any]												=				2_22.0_88
				__lowercase				:		Dict												=				True
				__lowercase				:		Union[str, Any]												=				True
				__lowercase				:		Union[str, Any]												=				True
				__lowercase				:		List[Any]												=				0.7_6_3_1_4_1

				__lowercase				:		str												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "TABFACT":
				__lowercase				:		Optional[Any]												=				TapasForSequenceClassification(config=_lowerCamelCase				)
		elif task == "MLM":
				__lowercase				:		List[str]												=				TapasForMaskedLM(config=_lowerCamelCase				)
		elif task == "INTERMEDIATE_PRETRAINING":
				__lowercase				:		List[str]												=				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}'				)
		__lowercase				:		Optional[int]												=				TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt",  model_max_length=5_12				)
		tokenizer.save_pretrained(_lowerCamelCase				)

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


if __name__ == "__main__":
						__A   :  Tuple					=      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   :  List[str]					=      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,
						)






"""simple docstring"""







import os
import sys
import unittest


__A   :  Optional[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
__A   :  Optional[Any]					=      os.path.join(git_repo_path, 'src', 'diffusers')




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		int												=				find_backend("    if not is_torch_available():"						)
						self.assertEqual(lowercase__    ,   "torch"						)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						__lowercase				:		int												=				find_backend("    if not (is_torch_available() and is_transformers_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers"						)

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

						__lowercase				:		str												=				find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers_and_onnx"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"    ,   lowercase__						)
						self.assertIn("torch_and_transformers"    ,   lowercase__						)
						self.assertIn("flax_and_transformers"    ,   lowercase__						)
						self.assertIn("torch_and_transformers_and_onnx"    ,   lowercase__						)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn("UNet2DModel"    ,   objects["torch"]						)
						self.assertIn("FlaxUNet2DConditionModel"    ,   objects["flax"]						)
						self.assertIn("StableDiffusionPipeline"    ,   objects["torch_and_transformers"]						)
						self.assertIn("FlaxStableDiffusionPipeline"    ,   objects["flax_and_transformers"]						)
						self.assertIn("LMSDiscreteScheduler"    ,   objects["torch_and_scipy"]						)
						self.assertIn("OnnxStableDiffusionPipeline"    ,   objects["torch_and_transformers_and_onnx"]						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Tuple												=				create_dummy_object("CONSTANT"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   "\nCONSTANT = None\n"						)

						__lowercase				:		Union[str, Any]												=				create_dummy_object("function"    ,   "'torch'"						)
						self.assertEqual(
						    lowercase__    ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

						__lowercase				:		Tuple												=				"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
						__lowercase				:		Dict												=				create_dummy_object("FakeClass"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Optional[int]												=				"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
						__lowercase				:		List[str]												=				create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
						self.assertEqual(dummy_files["torch"]    ,   lowercase__						)
