
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)
SCREAMING_SNAKE_CASE :		str       			=							logging.getLogger(__name__)


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


SCREAMING_SNAKE_CASE :		Optional[int]       			=							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_	( __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	BertAbsConfig(
				    temp_dir='''.'''			,    finetune_bert=UpperCamelCase__			,    large=UpperCamelCase__			,    share_emb=UpperCamelCase__			,    use_bert_emb=UpperCamelCase__			,    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			,    )
				A_													=	torch.load(UpperCamelCase__			,    lambda __UpperCamelCase			,    __UpperCamelCase  : storage  )
				A_													=	AbsSummarizer(UpperCamelCase__			,    torch.device('''cpu'''  )			,    UpperCamelCase__  )
				original.eval()

				A_													=	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'''  )
				A_													=	BertTokenizer.from_pretrained('''bert-base-uncased'''  )

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

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

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

				A_													=	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__":
						SCREAMING_SNAKE_CASE :		Tuple       			=							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.",
						)
						SCREAMING_SNAKE_CASE :		Union[str, Any]       			=							parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)



"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
       import jax.numpy as jnp

if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch





class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						{
           """task_specific_params""": {
               """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_2_8, """min_length""": 1_2, """num_beams""": 4},
               """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_4_2, """min_length""": 5_6, """num_beams""": 4},
               """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 6_2, """min_length""": 1_1, """num_beams""": 6},
           }
       }
       __lowercase						=						{
           """task_specific_params.summarization.length_penalty""": 1.0,
           """task_specific_params.summarization.max_length""": 1_2_8,
           """task_specific_params.summarization.min_length""": 1_2,
           """task_specific_params.summarization.num_beams""": 4,
           """task_specific_params.summarization_cnn.length_penalty""": 2.0,
           """task_specific_params.summarization_cnn.max_length""": 1_4_2,
           """task_specific_params.summarization_cnn.min_length""": 5_6,
           """task_specific_params.summarization_cnn.num_beams""": 4,
           """task_specific_params.summarization_xsum.length_penalty""": 1.0,
           """task_specific_params.summarization_xsum.max_length""": 6_2,
           """task_specific_params.summarization_xsum.min_length""": 1_1,
           """task_specific_params.summarization_xsum.num_beams""": 6,
       }

       self.assertEqual(flatten_dict(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							x.transpose()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							x.transpose((1, 2, 0)				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							transpose(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							transpose(A_   ,							axes=(1, 2, 0)				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							transpose(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							transpose(A_   ,							axes=(1, 2, 0)				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							np.asarray(transpose(A_				)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							np.asarray(transpose(A_   ,							axes=(1, 2, 0)				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							np.reshape(A_   ,							(4, 3)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							np.reshape(A_   ,							(1_2, 5)				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							reshape(A_   ,							(4, 3)				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							reshape(A_   ,							(1_2, 5)				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							reshape(A_   ,							(4, 3)				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							reshape(A_   ,							(1_2, 5)				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							np.asarray(reshape(A_   ,							(4, 3)				)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							np.asarray(reshape(A_   ,							(1_2, 5)				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							np.squeeze(A_				)				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							np.squeeze(A_   ,							axis=2				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							squeeze(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							squeeze(A_   ,							axis=2				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							squeeze(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							squeeze(A_   ,							axis=2				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							np.asarray(squeeze(A_				)				)				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							np.asarray(squeeze(A_   ,							axis=2				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							np.expand_dims(A_   ,							axis=1				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							expand_dims(A_   ,							axis=1				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							expand_dims(A_   ,							axis=1				).numpy()				)				)






      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							np.asarray(expand_dims(A_   ,							axis=1				)				)				)				)
