





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerModel,
      )
      from transformers.models.roformer.modeling_tf_roformer import (
          TFRoFormerSelfAttention,
          TFRoFormerSinusoidalPositionalEmbedding,
      )


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=True ,		A=True ,		A=True ,		A=99 ,		A=32 ,		A=2 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=16 ,		A=2 ,		A=0.02 ,		A=3 ,		A=4 ,		A=None ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			13
        _SCREAMING_SNAKE_CASE									=			7
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			99
        _SCREAMING_SNAKE_CASE									=			32
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			4
        _SCREAMING_SNAKE_CASE									=			37
        _SCREAMING_SNAKE_CASE									=			"""gelu"""
        _SCREAMING_SNAKE_CASE									=			0.1
        _SCREAMING_SNAKE_CASE									=			0.1
        _SCREAMING_SNAKE_CASE									=			512
        _SCREAMING_SNAKE_CASE									=			16
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			0.02
        _SCREAMING_SNAKE_CASE									=			3
        _SCREAMING_SNAKE_CASE									=			4
        _SCREAMING_SNAKE_CASE									=			None


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size       )

        _SCREAMING_SNAKE_CASE									=			None
        if self.use_input_mask:
            _SCREAMING_SNAKE_CASE									=			random_attention_mask([self.batch_size, self.seq_length]       )

        _SCREAMING_SNAKE_CASE									=			None
        if self.use_token_type_ids:
            _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size       )

        _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			None
        if self.use_labels:
            _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size       )
            _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels       )
            _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size] ,		self.num_choices       )

        _SCREAMING_SNAKE_CASE									=			RoFormerConfig(
            vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_hidden_layers=self.num_hidden_layers ,		num_attention_heads=self.num_attention_heads ,		intermediate_size=self.intermediate_size ,		hidden_act=self.hidden_act ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		initializer_range=self.initializer_range ,		return_dict=A ,		)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerModel(config=A       )
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

        _SCREAMING_SNAKE_CASE									=			[input_ids, input_mask]
        _SCREAMING_SNAKE_CASE									=			model(A       )

        _SCREAMING_SNAKE_CASE									=			model(A       )

        self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForCausalLM(config=A       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _SCREAMING_SNAKE_CASE									=			model(A       )["""logits"""]
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape       ) ,		[self.batch_size, self.seq_length, self.vocab_size]       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForMaskedLM(config=A       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForSequenceClassification(config=A       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.num_choices
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForMultipleChoice(config=A       )
        _SCREAMING_SNAKE_CASE									=			tf.tile(tf.expand_dims(A ,		1       ) ,		(1, self.num_choices, 1)       )
        _SCREAMING_SNAKE_CASE									=			tf.tile(tf.expand_dims(A ,		1       ) ,		(1, self.num_choices, 1)       )
        _SCREAMING_SNAKE_CASE									=			tf.tile(tf.expand_dims(A ,		1       ) ,		(1, self.num_choices, 1)       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
            """token_type_ids""": multiple_choice_token_type_ids,
        }
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_choices)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForTokenClassification(config=A       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForQuestionAnswering(config=A       )
        _SCREAMING_SNAKE_CASE									=			{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.start_logits.shape ,		(self.batch_size, self.seq_length)       )
        self.parent.assertEqual(result.end_logits.shape ,		(self.batch_size, self.seq_length)       )





    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        (
            (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       (
            _SCREAMING_SNAKE_CASE
        )				,       
        )									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_tf
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase     = (
        {
            '''feature-extraction''': TFRoFormerModel,
            '''fill-mask''': TFRoFormerForMaskedLM,
            '''question-answering''': TFRoFormerForQuestionAnswering,
            '''text-classification''': TFRoFormerForSequenceClassification,
            '''text-generation''': TFRoFormerForCausalLM,
            '''token-classification''': TFRoFormerForTokenClassification,
            '''zero-shot''': TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
            return True

        return False


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		hidden_size=37       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        self.config_tester.run_common_tests()


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*A       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*A       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*A       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A       )





    @slow
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""       )
        self.assertIsNotNone(A       )




@require_tf
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @slow
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""       )
        _SCREAMING_SNAKE_CASE									=			tf.constant([[0, 1, 2, 3, 4, 5]]       )
        _SCREAMING_SNAKE_CASE									=			model(A       )[0]

        # TODO Replace vocab size
        _SCREAMING_SNAKE_CASE									=			5_0000

        _SCREAMING_SNAKE_CASE									=			[1, 6, vocab_size]
        self.assertEqual(output.shape ,		A       )

        print(output[:, :3, :3]       )

        # TODO Replace values below with what was printed above.
        _SCREAMING_SNAKE_CASE									=			tf.constant(
            [
                [
                    [-0.1205_3341, -1.026_4901, 0.2922_1946],
                    [-1.513_3783, 0.19_7433, 0.1519_0607],
                    [-5.013_5403, -3.90_0256, -0.8403_8764],
                ]
            ]       )
        tf.debugging.assert_near(output[:, :3, :3] ,		A ,		atol=1e-4       )




@require_tf
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = 1E-4


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			tf.constant([[4, 10]]       )
        _SCREAMING_SNAKE_CASE									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,		embedding_dim=6       )

        _SCREAMING_SNAKE_CASE									=			emba(input_ids.shape       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(
            [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]       )

        tf.debugging.assert_near(A ,		A ,		atol=self.tolerance       )





    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			tf.constant(
            [
                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
            ]       )
        _SCREAMING_SNAKE_CASE									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,		embedding_dim=512       )
        emba([2, 16, 512]       )
        _SCREAMING_SNAKE_CASE									=			emba.weight[:3, :5]

        tf.debugging.assert_near(A ,		A ,		atol=self.tolerance       )




@require_tf
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = 1E-4


    def 			snake_case_(		self       )		->   Union[str, Any]:
        # 2,12,16,64
        _SCREAMING_SNAKE_CASE									=			tf.reshape(tf.range(2 * 12 * 16 * 64 ,		dtype=tf.floataa       ) ,		shape=(2, 12, 16, 64)       ) / 100

        _SCREAMING_SNAKE_CASE									=			-tf.reshape(tf.range(2 * 12 * 16 * 64 ,		dtype=tf.floataa       ) ,		shape=(2, 12, 16, 64)       ) / 100

        _SCREAMING_SNAKE_CASE									=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,		embedding_dim=64       )
        _SCREAMING_SNAKE_CASE									=			embed_positions([2, 16, 768]       )[None, None, :, :]

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
            A ,		A ,		A       )

        _SCREAMING_SNAKE_CASE									=			tf.constant(
            [
                [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
            ]       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(
            [
                [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
            ]       )

        tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,		A ,		atol=self.tolerance       )
        tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,		A ,		atol=self.tolerance       )










'''simple docstring'''

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)
lowercase_					= logging.getLogger(__name__)


lowercase_					= """Hello world! cécé herlolip"""


lowercase_					= 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 :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			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					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

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

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

    _SCREAMING_SNAKE_CASE									=			new_model(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			new_model.generator(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			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__":
      lowercase_					= 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.""",
      )
      lowercase_					= parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )



