






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      :    Any   		=   logging.getLogger(__name__)


__lowerCamelCase      :    Union[str, Any]   		=   """Hello world! cécé herlolip"""


__lowerCamelCase      :    str   		=   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   SCREAMING_SNAKE_CASE       (				snake_case_      :							str  ,						snake_case_      :							str     ):
	snake_case__				:   Tuple     =						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  ,						)
	snake_case__				:   Dict     =						torch.load(snake_case_  ,						lambda snake_case_  ,						snake_case_     : storage     )
	snake_case__				:   Union[str, Any]     =						AbsSummarizer(snake_case_  ,						torch.device("cpu"     )  ,						snake_case_     )
	original.eval()

	snake_case__				:   List[str]     =						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"     )
	snake_case__				:   int     =						BertTokenizer.from_pretrained("bert-base-uncased"     )

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

	snake_case__				:   Optional[Any]     =						new_model(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_     )[0]
	snake_case__				:   int     =						new_model.generator(snake_case_     )

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

	snake_case__				:   Tuple     =						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      :    List[Any]   		=   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,
	)











from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



