"""simple docstring"""




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
__lowerCAmelCase    :							int													=						logging.getLogger(__name__)


__lowerCAmelCase    :							Optional[Any]													=						"Hello world! cécé herlolip"


__lowerCAmelCase    :							Union[str, Any]													=						namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)







def 							_UpperCAmelCase		(       lowerCAmelCase_			,  lowerCAmelCase_     ):





		"""simple docstring"""




		lowerCAmelCase__        =   BertAbsConfig(
		    temp_dir="""."""			,  finetune_bert=__snake_case			,  large=__snake_case			,  share_emb=__snake_case			,  use_bert_emb=__snake_case			,  encoder="""bert"""			,  max_pos=512			,  enc_layers=6			,  enc_hidden_size=512			,  enc_heads=8			,  enc_ff_size=512			,  enc_dropout=0.2			,  dec_layers=6			,  dec_hidden_size=768			,  dec_heads=8			,  dec_ff_size=2048			,  dec_dropout=0.2			,  )
		lowerCAmelCase__        =   torch.load(__snake_case			,  lambda lowerCAmelCase_			,  lowerCAmelCase_     : storage     )
		lowerCAmelCase__        =   AbsSummarizer(__snake_case			,  torch.device("""cpu"""     )			,  __snake_case     )
		original.eval()

		lowerCAmelCase__        =   BertAbsSummarizer(__snake_case			,  torch.device("""cpu"""     )     )
		new_model.eval()

		# -------------------
		# Convert the weights
		# -------------------

		logging.info("""convert the model"""     )
		new_model.bert.load_state_dict(original.bert.state_dict()     )
		new_model.decoder.load_state_dict(original.decoder.state_dict()     )
		new_model.generator.load_state_dict(original.generator.state_dict()     )

		# ----------------------------------
		# Make sure the outpus are identical
		# ----------------------------------

		logging.info("""Make sure that the models\' outputs are identical"""     )
		lowerCAmelCase__        =   BertTokenizer.from_pretrained("""bert-base-uncased"""     )

		# prepare the model inputs
		lowerCAmelCase__        =   tokenizer.encode("""This is sample éàalj\'-."""     )
		encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__snake_case     ))     )
		lowerCAmelCase__        =   torch.tensor(__snake_case     ).unsqueeze(0     )
		lowerCAmelCase__        =   tokenizer.encode("""This is sample 3 éàalj\'-."""     )
		decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__snake_case     ))     )
		lowerCAmelCase__        =   torch.tensor(__snake_case     ).unsqueeze(0     )

		# failsafe to make sure the weights reset does not affect the
		# loaded weights.
		assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight     )     ) == 0

		# forward pass
		lowerCAmelCase__        =   encoder_input_ids
		lowerCAmelCase__        =   decoder_input_ids
		lowerCAmelCase__        =   lowerCAmelCase__        =   None
		lowerCAmelCase__        =   None
		lowerCAmelCase__        =   lowerCAmelCase__        =   None
		lowerCAmelCase__        =   lowerCAmelCase__        =   None
		lowerCAmelCase__        =   None

		# The original model does not apply the geneator layer immediatly but rather in
		# the beam search (where it combines softmax + linear layer). Since we already
		# apply the softmax in our generation process we only apply the linear layer here.
		# We make sure that the outputs of the full stack are identical
		lowerCAmelCase__        =   original(__snake_case			,  __snake_case			,  __snake_case			,  __snake_case			,  __snake_case			,  __snake_case			,  __snake_case     )[0]
		lowerCAmelCase__        =   original.generator(__snake_case     )

		lowerCAmelCase__        =   new_model(
		    __snake_case			,  __snake_case			,  __snake_case			,  __snake_case			,  __snake_case     )[0]
		lowerCAmelCase__        =   new_model.generator(__snake_case     )

		lowerCAmelCase__        =   torch.max(torch.abs(output_converted_model - output_original_model     )     ).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(__snake_case     )     )
		lowerCAmelCase__        =   torch.max(torch.abs(output_converted_generator - output_original_generator     )     ).item()
		print("""Maximum absolute difference beween weights: {:.2f}""".format(__snake_case     )     )

		lowerCAmelCase__        =   torch.allclose(__snake_case			,  __snake_case			,  atol=1e-3     )
		if are_identical:
				logging.info("""all weights are equal up to 1e-3"""     )
		else:
				raise ValueError("""the weights are different. The new model is likely different from the original one."""     )

		# The model has been saved with torch.save(model) and this is bound to the exact
		# directory structure. We save the state_dict instead.
		logging.info("""saving the model\'s state dictionary"""     )
		torch.save(
		    new_model.state_dict()			,  """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Optional[int]													=						argparse.ArgumentParser()
			parser.add_argument(
			    "--bertabs_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch dump.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path to the output PyTorch model.",
			)
			__lowerCAmelCase    :							List[str]													=						parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)





"""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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# 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=lowerCamelCase__     )
		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=lowerCamelCase__     )
		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=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   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__        =   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[str]													=						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    :							Union[str, Any]													=						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,
			)




