"""simple docstring"""




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    """files"""			,  [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]			,  )
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   tmp_path_factory.mktemp("""dset_infos_dir"""     )
		if "full:README.md" in files:
				with open(dataset_infos_dir / """README.md"""			,  """w"""     ) as f:
						f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""     )
		if "empty:README.md" in files:
				with open(dataset_infos_dir / """README.md"""			,  """w"""     ) as f:
						f.write(""""""     )
    # we want to support dataset_infos.json for backward compatibility
		if "dataset_infos.json" in files:
				with open(dataset_infos_dir / """dataset_infos.json"""			,  """w"""     ) as f:
						f.write("""{\"default\": {\"dataset_size\": 42}}"""     )
		lowerCAmelCase__        =   DatasetInfosDict.from_directory(lowerCamelCase__     )
		assert dataset_infos
		assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    """dataset_info"""			,  [
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""			,  features=Features({"""a""": Value("""int32"""     )}     )			,  builder_name="""builder"""			,  config_name="""config"""			,  version="""1.0.0"""			,  splits=[{"""name""": """train"""}]			,  download_size=42			,  ),
    ]			,  )
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   str(lowerCamelCase__     )
		dataset_info.write_to_directory(lowerCamelCase__     )
		lowerCAmelCase__        =   DatasetInfo.from_directory(lowerCamelCase__     )
		assert dataset_info == reloaded
		assert os.path.exists(os.path.join(lowerCamelCase__			,  """dataset_info.json"""     )     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   DatasetInfo(
		    description="""foo"""			,  citation="""bar"""			,  homepage="""https://foo.bar"""			,  license="""CC0"""			,  features=Features({"""a""": Value("""int32"""     )}     )			,  post_processed={}			,  supervised_keys=()			,  task_templates=[]			,  builder_name="""builder"""			,  config_name="""config"""			,  version="""1.0.0"""			,  splits=[{"""name""": """train""", """num_examples""": 42}]			,  download_checksums={}			,  download_size=1337			,  post_processing_size=442			,  dataset_size=1234			,  size_in_bytes=1337 + 442 + 1234			,  )
		lowerCAmelCase__        =   dataset_info._to_yaml_dict()
		assert sorted(lowerCamelCase__     ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML     )
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				assert key in dataset_info_yaml_dict
				assert isinstance(dataset_info_yaml_dict[key]			,  (list, dict, int, str)     )
		lowerCAmelCase__        =   yaml.safe_dump(lowerCamelCase__     )
		lowerCAmelCase__        =   yaml.safe_load(lowerCamelCase__     )
		assert dataset_info_yaml_dict == reloaded







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   DatasetInfo()
		lowerCAmelCase__        =   dataset_info._to_yaml_dict()
		assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    """dataset_infos_dict"""			,  [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}     ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}     ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""			,  features=Features({"""a""": Value("""int32"""     )}     )			,  builder_name="""builder"""			,  config_name="""config"""			,  version="""1.0.0"""			,  splits=[{"""name""": """train"""}]			,  download_size=42			,  )
            }     ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42     ),
                """v2""": DatasetInfo(dataset_size=1337     ),
            }     ),
    ]			,  )
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   str(lowerCamelCase__     )
		dataset_infos_dict.write_to_directory(lowerCamelCase__     )
		lowerCAmelCase__        =   DatasetInfosDict.from_directory(lowerCamelCase__     )

		# the config_name of the dataset_infos_dict take over the attribute
		for config_name, dataset_info in dataset_infos_dict.items():
				lowerCAmelCase__        =   config_name
				# the yaml representation doesn't include fields like description or citation
				# so we just test that we can recover what we can from the yaml
				lowerCAmelCase__        =   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()     )
		assert dataset_infos_dict == reloaded

		if dataset_infos_dict:
				assert os.path.exists(os.path.join(lowerCamelCase__			,  """README.md"""     )     )





"""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,
			)




