


"""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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    TapasConfig.from_json_file(_UpperCamelCase   )
			# 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=_UpperCamelCase   )
			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=_UpperCamelCase   )
			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=_UpperCamelCase   )
			elif task == "TABFACT":
						__lowerCAmelCase       					=    TapasForSequenceClassification(config=_UpperCamelCase   )
			elif task == "MLM":
						__lowerCAmelCase       					=    TapasForMaskedLM(config=_UpperCamelCase   )
			elif task == "INTERMEDIATE_PRETRAINING":
						__lowerCAmelCase       					=    TapasModel(config=_UpperCamelCase   )
			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(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )

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

			# 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(_UpperCamelCase   )

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


if __name__ == "__main__":
	A  :     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."
	)
	A  :     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 flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




