

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_      =     {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'RobertaPreLayerNormForCausalLM',
			    'RobertaPreLayerNormForMaskedLM',
			    'RobertaPreLayerNormForMultipleChoice',
			    'RobertaPreLayerNormForQuestionAnswering',
			    'RobertaPreLayerNormForSequenceClassification',
			    'RobertaPreLayerNormForTokenClassification',
			    'RobertaPreLayerNormModel',
			    'RobertaPreLayerNormPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFRobertaPreLayerNormForCausalLM',
			    'TFRobertaPreLayerNormForMaskedLM',
			    'TFRobertaPreLayerNormForMultipleChoice',
			    'TFRobertaPreLayerNormForQuestionAnswering',
			    'TFRobertaPreLayerNormForSequenceClassification',
			    'TFRobertaPreLayerNormForTokenClassification',
			    'TFRobertaPreLayerNormMainLayer',
			    'TFRobertaPreLayerNormModel',
			    'TFRobertaPreLayerNormPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'FlaxRobertaPreLayerNormForCausalLM',
			    'FlaxRobertaPreLayerNormForMaskedLM',
			    'FlaxRobertaPreLayerNormForMultipleChoice',
			    'FlaxRobertaPreLayerNormForQuestionAnswering',
			    'FlaxRobertaPreLayerNormForSequenceClassification',
			    'FlaxRobertaPreLayerNormForTokenClassification',
			    'FlaxRobertaPreLayerNormModel',
			    'FlaxRobertaPreLayerNormPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_roberta_prelayernorm import (
			    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    RobertaPreLayerNormConfig,
			    RobertaPreLayerNormOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta_prelayernorm import (
						    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaPreLayerNormForCausalLM,
						    RobertaPreLayerNormForMaskedLM,
						    RobertaPreLayerNormForMultipleChoice,
						    RobertaPreLayerNormForQuestionAnswering,
						    RobertaPreLayerNormForSequenceClassification,
						    RobertaPreLayerNormForTokenClassification,
						    RobertaPreLayerNormModel,
						    RobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta_prelayernorm import (
						    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaPreLayerNormForCausalLM,
						    TFRobertaPreLayerNormForMaskedLM,
						    TFRobertaPreLayerNormForMultipleChoice,
						    TFRobertaPreLayerNormForQuestionAnswering,
						    TFRobertaPreLayerNormForSequenceClassification,
						    TFRobertaPreLayerNormForTokenClassification,
						    TFRobertaPreLayerNormMainLayer,
						    TFRobertaPreLayerNormModel,
						    TFRobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						    FlaxRobertaPreLayerNormPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


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)
a_      =     logging.getLogger(__name__)


a_      =     'Hello world! cécé herlolip'


a_      =     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 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							BertAbsConfig(
	    temp_dir="."				,      finetune_bert=_a				,      large=_a				,      share_emb=_a				,      use_bert_emb=_a				,      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				,      )
	SCREAMING_SNAKE_CASE :   Dict					  =							torch.load(_a				,      lambda _a				,      _a: storage)
	SCREAMING_SNAKE_CASE :   str					  =							AbsSummarizer(_a				,      torch.device("cpu")				,      _a)
	original.eval()

	SCREAMING_SNAKE_CASE :   List[str]					  =							BertAbsSummarizer(_a				,      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")
	SCREAMING_SNAKE_CASE :   List[str]					  =							BertTokenizer.from_pretrained("bert-base-uncased")

	# prepare the model inputs
	SCREAMING_SNAKE_CASE :   List[str]					  =							tokenizer.encode("This is sample éàalj'-.")
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.encode("This is sample 3 éàalj'-.")
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).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
	SCREAMING_SNAKE_CASE :   List[Any]					  =							encoder_input_ids
	SCREAMING_SNAKE_CASE :   List[Any]					  =							decoder_input_ids
	SCREAMING_SNAKE_CASE :   Dict					  =							None
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
	SCREAMING_SNAKE_CASE :   List[Any]					  =							None
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
	SCREAMING_SNAKE_CASE :   Dict					  =							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
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							original(_a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Dict					  =							original.generator(_a)

	SCREAMING_SNAKE_CASE :   Any					  =							new_model(
	    _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Tuple					  =							new_model.generator(_a)

	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.max(torch.abs(output_converted_model - output_original_model)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))

	SCREAMING_SNAKE_CASE :   int					  =							torch.allclose(_a				,      _a				,      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__":
			a_      =     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.',
			)
			a_      =     parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)