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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		BertAbsConfig(
			    temp_dir='''.''',		finetune_bert=UpperCAmelCase,		large=UpperCAmelCase,		share_emb=UpperCAmelCase,		use_bert_emb=UpperCAmelCase,		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,		)
			lowercase							=		torch.load(UpperCAmelCase,		lambda UpperCAmelCase,		UpperCAmelCase       : storage       )
			lowercase							=		AbsSummarizer(UpperCAmelCase,		torch.device('''cpu'''       ),		UpperCAmelCase       )
			original.eval()

			lowercase							=		BertAbsSummarizer(UpperCAmelCase,		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'''       )
			lowercase							=		BertTokenizer.from_pretrained('''bert-base-uncased'''       )

			# prepare the model inputs
			lowercase							=		tokenizer.encode('''This is sample éàalj\'-.'''       )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCAmelCase       ))       )
			lowercase							=		torch.tensor(UpperCAmelCase       ).unsqueeze(0       )
			lowercase							=		tokenizer.encode('''This is sample 3 éàalj\'-.'''       )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCAmelCase       ))       )
			lowercase							=		torch.tensor(UpperCAmelCase       ).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
			lowercase							=		encoder_input_ids
			lowercase							=		decoder_input_ids
			lowercase							=		lowercase							=		None
			lowercase							=		None
			lowercase							=		lowercase							=		None
			lowercase							=		lowercase							=		None
			lowercase							=		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
			lowercase							=		original(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )[0]
			lowercase							=		original.generator(UpperCAmelCase       )

			lowercase							=		new_model(
			    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )[0]
			lowercase							=		new_model.generator(UpperCAmelCase       )

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

			lowercase							=		torch.allclose(UpperCAmelCase,		UpperCAmelCase,		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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





