


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_    (       _a	,   _a	,   _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			str         =					TapasConfig.from_json_file(__lowerCamelCase       )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__   :			Union[str, Any]         =					reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__   :			str         =					TapasForQuestionAnswering(config=__lowerCamelCase       )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__   :			Union[str, Any]         =					4
				lowerCAmelCase__   :			int         =					True
				# hparam_utils.py hparams
				lowerCAmelCase__   :			Optional[Any]         =					0.66_46_94
				lowerCAmelCase__   :			Tuple         =					0.20_79_51
				lowerCAmelCase__   :			Dict         =					0.12_11_94
				lowerCAmelCase__   :			Optional[Any]         =					True
				lowerCAmelCase__   :			Optional[Any]         =					True
				lowerCAmelCase__   :			int         =					False
				lowerCAmelCase__   :			str         =					0.0_35_25_13

				lowerCAmelCase__   :			List[Any]         =					TapasForQuestionAnswering(config=__lowerCamelCase       )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__   :			List[str]         =					4
				lowerCAmelCase__   :			List[str]         =					False
				# hparam_utils.py hparams
				lowerCAmelCase__   :			List[Any]         =					36.45_19
				lowerCAmelCase__   :			int         =					0.90_34_21
				lowerCAmelCase__   :			Union[str, Any]         =					2_22.0_88
				lowerCAmelCase__   :			List[str]         =					True
				lowerCAmelCase__   :			str         =					True
				lowerCAmelCase__   :			str         =					True
				lowerCAmelCase__   :			Tuple         =					0.76_31_41

				lowerCAmelCase__   :			Dict         =					TapasForQuestionAnswering(config=__lowerCamelCase       )
		elif task == "TABFACT":
				lowerCAmelCase__   :			List[Any]         =					TapasForSequenceClassification(config=__lowerCamelCase       )
		elif task == "MLM":
				lowerCAmelCase__   :			Optional[Any]         =					TapasForMaskedLM(config=__lowerCamelCase       )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__   :			int         =					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__   :			Union[str, Any]         =					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						=		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						=		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"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a		       =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""albert"""







  def __init__(		self				,   lowercase_=3_0000				,   lowercase_=128				,   lowercase_=4096				,   lowercase_=12				,   lowercase_=1				,   lowercase_=64				,   lowercase_=1_6384				,   lowercase_=1				,   lowercase_="gelu_new"				,   lowercase_=0				,   lowercase_=0				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0.1				,   lowercase_="absolute"				,   lowercase_=0				,   lowercase_=2				,   lowercase_=3				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     UpperCAmelCase_					:					int			     =    vocab_size
     UpperCAmelCase_					:					Optional[int]			     =    embedding_size
     UpperCAmelCase_					:					List[str]			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					Union[str, Any]			     =    num_hidden_groups
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					Any			     =    inner_group_num
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Union[str, Any]			     =    intermediate_size
     UpperCAmelCase_					:					List[str]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					Any			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    layer_norm_eps
     UpperCAmelCase_					:					List[Any]			     =    classifier_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    position_embedding_type




class A_	(lowercase__      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					int			     =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]  )


