
'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 				snake_case							(						UpperCAmelCase		)->	Optional[int]:







      """simple docstring"""
      __A						=	[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase_					,     lowerCAmelCase_		)





def 				snake_case							(						UpperCAmelCase		)->	List[Any]:







      """simple docstring"""
      __A						=	emb.weight.shape
      __A						=	nn.Linear(lowerCAmelCase_					,     lowerCAmelCase_					,     bias=lowerCAmelCase_		)
      __A						=	emb.weight.data
      return lin_layer





def 				snake_case							(						UpperCAmelCase		)->	Optional[int]:







      """simple docstring"""
      __A						=	torch.load(lowerCAmelCase_					,     map_location='cpu'		)
      __A						=	mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      __A						=	mam_aaa["""model"""]
      remove_ignore_keys_(lowerCAmelCase_		)
      __A						=	state_dict["""encoder.embed_tokens.weight"""].shape[0]

      __A						=	MaMaaaConfig(
          vocab_size=lowerCAmelCase_					,     max_position_embeddings=1_0_2_4					,     encoder_layers=args.encoder_layers					,     decoder_layers=args.decoder_layers					,     encoder_attention_heads=args.encoder_attention_heads					,     decoder_attention_heads=args.decoder_attention_heads					,     encoder_ffn_dim=args.encoder_ffn_embed_dim					,     decoder_ffn_dim=args.decoder_ffn_embed_dim					,     d_model=args.encoder_embed_dim					,     encoder_layerdrop=args.encoder_layerdrop					,     decoder_layerdrop=args.decoder_layerdrop					,     dropout=args.dropout					,     attention_dropout=args.attention_dropout					,     activation_dropout=args.activation_dropout					,     activation_function='relu'					,     )

      __A						=	state_dict["""decoder.embed_tokens.weight"""]
      __A						=	MaMaaaForConditionalGeneration(lowerCAmelCase_		)
      model.model.load_state_dict(lowerCAmelCase_					,     strict=lowerCAmelCase_		)
      __A						=	make_linear_from_emb(model.model.shared		)

      return model


if __name__ == "__main__":
 a__						:      Union[str, Any]   				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
 parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 a__						:      Any   				=				parser.parse_args()
 a__						:      Optional[Any]   				=				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)














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,
	)