



"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp



class       SCREAMING_SNAKE_CASE__  (					nn.Module ):
   __lowerCAmelCase  :   int
   __lowerCAmelCase  :   jnp.dtype						=						jnp.floataa

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	nn.Conv(
        self.out_channels      ,     kernel_size=(3, 3)      ,     strides=(1, 1)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )




   def __call__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	hidden_states.shape
    UpperCAmelCase	:					str	=	jax.image.resize(
        _SCREAMING_SNAKE_CASE      ,     shape=(batch, height * 2, width * 2, channels)      ,     method="""nearest"""      ,     )
    UpperCAmelCase	:					Optional[int]	=	self.conv(_SCREAMING_SNAKE_CASE						)
    return hidden_states



class       SCREAMING_SNAKE_CASE__  (					nn.Module ):
   __lowerCAmelCase  :   int
   __lowerCAmelCase  :   jnp.dtype						=						jnp.floataa

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	nn.Conv(
        self.out_channels      ,     kernel_size=(3, 3)      ,     strides=(2, 2)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )




   def __call__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.conv(_SCREAMING_SNAKE_CASE						)
    return hidden_states



class       SCREAMING_SNAKE_CASE__  (					nn.Module ):
   __lowerCAmelCase  :   int
   __lowerCAmelCase  :   int						=						None
   __lowerCAmelCase  :   float						=						0.0
   __lowerCAmelCase  :   bool						=						None
   __lowerCAmelCase  :   jnp.dtype						=						jnp.floataa

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.in_channels if self.out_channels is None else self.out_channels

    UpperCAmelCase	:					Any	=	nn.GroupNorm(num_groups=32      ,     epsilon=1E-5						)
    UpperCAmelCase	:					Optional[Any]	=	nn.Conv(
        _SCREAMING_SNAKE_CASE      ,     kernel_size=(3, 3)      ,     strides=(1, 1)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )

    UpperCAmelCase	:					str	=	nn.Dense(_SCREAMING_SNAKE_CASE      ,     dtype=self.dtype						)

    UpperCAmelCase	:					str	=	nn.GroupNorm(num_groups=32      ,     epsilon=1E-5						)
    UpperCAmelCase	:					List[str]	=	nn.Dropout(self.dropout_prob						)
    UpperCAmelCase	:					str	=	nn.Conv(
        _SCREAMING_SNAKE_CASE      ,     kernel_size=(3, 3)      ,     strides=(1, 1)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )

    UpperCAmelCase	:					Tuple	=	self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

    UpperCAmelCase	:					Dict	=	None
    if use_nin_shortcut:
     UpperCAmelCase	:					str	=	nn.Conv(
         _SCREAMING_SNAKE_CASE      ,     kernel_size=(1, 1)      ,     strides=(1, 1)      ,     padding="""VALID"""      ,     dtype=self.dtype      ,     )




   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=True						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	hidden_states
    UpperCAmelCase	:					Dict	=	self.norma(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	nn.swish(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	self.conva(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	self.time_emb_proj(nn.swish(_SCREAMING_SNAKE_CASE						)						)
    UpperCAmelCase	:					Optional[int]	=	jnp.expand_dims(jnp.expand_dims(_SCREAMING_SNAKE_CASE      ,     1						)      ,     1						)
    UpperCAmelCase	:					Union[str, Any]	=	hidden_states + temb

    UpperCAmelCase	:					Any	=	self.norma(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, Any]	=	nn.swish(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	self.dropout(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	self.conva(_SCREAMING_SNAKE_CASE						)

    if self.conv_shortcut is not None:
     UpperCAmelCase	:					Dict	=	self.conv_shortcut(_SCREAMING_SNAKE_CASE						)

    return hidden_states + residual








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_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   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					:							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       :       List[Any]        =				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       :       int        =				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,
	)