
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__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			lowerCAmelCase_						=	BertAbsConfig(
			    temp_dir="."    ,							finetune_bert=__lowerCAmelCase    ,							large=__lowerCAmelCase    ,							share_emb=__lowerCAmelCase    ,							use_bert_emb=__lowerCAmelCase    ,							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(__lowerCAmelCase    ,							lambda __lowerCAmelCase    ,							__lowerCAmelCase				: storage				)
			lowerCAmelCase_						=	AbsSummarizer(__lowerCAmelCase    ,							torch.device("cpu"				)    ,							__lowerCAmelCase				)
			original.eval()

			lowerCAmelCase_						=	BertAbsSummarizer(__lowerCAmelCase    ,							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(__lowerCAmelCase				))				)
			lowerCAmelCase_						=	torch.tensor(__lowerCAmelCase				).unsqueeze(0				)
			lowerCAmelCase_						=	tokenizer.encode("This is sample 3 éàalj'-."				)
			decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCAmelCase				))				)
			lowerCAmelCase_						=	torch.tensor(__lowerCAmelCase				).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(__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)[0]
			lowerCAmelCase_						=	original.generator(__lowerCAmelCase				)

			lowerCAmelCase_						=	new_model(
			    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)[0]
			lowerCAmelCase_						=	new_model.generator(__lowerCAmelCase				)

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

			lowerCAmelCase_						=	torch.allclose(__lowerCAmelCase    ,							__lowerCAmelCase    ,							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,
			)





from typing import TYPE_CHECKING

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


_A   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["DistilBertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "DistilBertForMaskedLM",
			    "DistilBertForMultipleChoice",
			    "DistilBertForQuestionAnswering",
			    "DistilBertForSequenceClassification",
			    "DistilBertForTokenClassification",
			    "DistilBertModel",
			    "DistilBertPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFDistilBertForMaskedLM",
			    "TFDistilBertForMultipleChoice",
			    "TFDistilBertForQuestionAnswering",
			    "TFDistilBertForSequenceClassification",
			    "TFDistilBertForTokenClassification",
			    "TFDistilBertMainLayer",
			    "TFDistilBertModel",
			    "TFDistilBertPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxDistilBertForMaskedLM",
			    "FlaxDistilBertForMultipleChoice",
			    "FlaxDistilBertForQuestionAnswering",
			    "FlaxDistilBertForSequenceClassification",
			    "FlaxDistilBertForTokenClassification",
			    "FlaxDistilBertModel",
			    "FlaxDistilBertPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_distilbert import (
			    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DistilBertConfig,
			    DistilBertOnnxConfig,
			)
			from .tokenization_distilbert import DistilBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_distilbert_fast import DistilBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_distilbert import (
						    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DistilBertForMaskedLM,
						    DistilBertForMultipleChoice,
						    DistilBertForQuestionAnswering,
						    DistilBertForSequenceClassification,
						    DistilBertForTokenClassification,
						    DistilBertModel,
						    DistilBertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_distilbert import (
						    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDistilBertForMaskedLM,
						    TFDistilBertForMultipleChoice,
						    TFDistilBertForQuestionAnswering,
						    TFDistilBertForSequenceClassification,
						    TFDistilBertForTokenClassification,
						    TFDistilBertMainLayer,
						    TFDistilBertModel,
						    TFDistilBertPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						    FlaxDistilBertPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



