






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 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       Tuple     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       Union[str, Any]       )		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						TapasConfig.from_json_file(lowercase__       )
				# 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=lowercase__       )
				elif task == "WTQ":
								# run_task_main.py hparams
								lowerCAmelCase__      			=						4
								lowerCAmelCase__      			=						True
								# hparam_utils.py hparams
								lowerCAmelCase__      			=						0.664694
								lowerCAmelCase__      			=						0.207951
								lowerCAmelCase__      			=						0.121194
								lowerCAmelCase__      			=						True
								lowerCAmelCase__      			=						True
								lowerCAmelCase__      			=						False
								lowerCAmelCase__      			=						0.0352513

								lowerCAmelCase__      			=						TapasForQuestionAnswering(config=lowercase__       )
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								lowerCAmelCase__      			=						4
								lowerCAmelCase__      			=						False
								# hparam_utils.py hparams
								lowerCAmelCase__      			=						36.4519
								lowerCAmelCase__      			=						0.903421
								lowerCAmelCase__      			=						222.088
								lowerCAmelCase__      			=						True
								lowerCAmelCase__      			=						True
								lowerCAmelCase__      			=						True
								lowerCAmelCase__      			=						0.763141

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

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

				# 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=5_12       )
				tokenizer.save_pretrained(lowercase__       )

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


if __name__ == "__main__":
			_UpperCAmelCase			:		int   				=    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."
			)
			_UpperCAmelCase			:		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,
			)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





