


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)
__UpperCamelCase    		=       logging.getLogger(__name__)


__UpperCamelCase    		=       'Hello world! cécé herlolip'


__UpperCamelCase    		=       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_(				_A   :List[str]				,     _A   :int  )->		Tuple:
			UpperCamelCase__					=       BertAbsConfig(
			    temp_dir="."				,     finetune_bert=_A				,     large=_A				,     share_emb=_A				,     use_bert_emb=_A				,     encoder="bert"				,     max_pos=5_12				,     enc_layers=6				,     enc_hidden_size=5_12				,     enc_heads=8				,     enc_ff_size=5_12				,     enc_dropout=0.2				,     dec_layers=6				,     dec_hidden_size=7_68				,     dec_heads=8				,     dec_ff_size=20_48				,     dec_dropout=0.2				,     )
			UpperCamelCase__					=       torch.load(_A				,     lambda _A				,     _A  : storage  )
			UpperCamelCase__					=       AbsSummarizer(_A				,     torch.device("cpu"  )				,     _A  )
			original.eval()

			UpperCamelCase__					=       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"  )
			UpperCamelCase__					=       BertTokenizer.from_pretrained("bert-base-uncased"  )

			# prepare the model inputs
			UpperCamelCase__					=       tokenizer.encode("This is sample éàalj'-."  )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_A  ))  )
			UpperCamelCase__					=       torch.tensor(_A  ).unsqueeze(0  )
			UpperCamelCase__					=       tokenizer.encode("This is sample 3 éàalj'-."  )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_A  ))  )
			UpperCamelCase__					=       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
			UpperCamelCase__					=       encoder_input_ids
			UpperCamelCase__					=       decoder_input_ids
			UpperCamelCase__					=       UpperCamelCase__					=       None
			UpperCamelCase__					=       None
			UpperCamelCase__					=       UpperCamelCase__					=       None
			UpperCamelCase__					=       UpperCamelCase__					=       None
			UpperCamelCase__					=       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
			UpperCamelCase__					=       original(_A				,     _A				,     _A				,     _A				,     _A				,     _A				,     _A  )[0]
			UpperCamelCase__					=       original.generator(_A  )

			UpperCamelCase__					=       new_model(
			    _A				,     _A				,     _A				,     _A				,     _A  )[0]
			UpperCamelCase__					=       new_model.generator(_A  )

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

			UpperCamelCase__					=       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__":
						__UpperCamelCase    		=       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.',
						)
						__UpperCamelCase    		=       parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


