





'''simple docstring'''

import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
      )


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=True ,		A=False ,		A=True ,		A=99 ,		A=32 ,		A=5 ,		A=4 ,		A=64 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=16 ,		A=2 ,		A=0.02 ,		A=3 ,		A=4 ,		A=None ,		A=2 ,		A=2 ,		A=2 ,		A=2 ,		A=4 ,		A=1 ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			seq_length
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_input_mask
        _SCREAMING_SNAKE_CASE									=			use_token_type_ids
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			num_labels
        _SCREAMING_SNAKE_CASE									=			num_choices
        _SCREAMING_SNAKE_CASE									=			scope
        _SCREAMING_SNAKE_CASE									=			q_groups
        _SCREAMING_SNAKE_CASE									=			k_groups
        _SCREAMING_SNAKE_CASE									=			v_groups
        _SCREAMING_SNAKE_CASE									=			post_attention_groups
        _SCREAMING_SNAKE_CASE									=			intermediate_groups
        _SCREAMING_SNAKE_CASE									=			output_groups


    def 			snake_case_(		self       )		->   Dict:
        _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
        _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									=			self.get_config()

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


    def 			snake_case_(		self       )		->   Optional[int]:
        return SqueezeBertConfig(
            embedding_size=self.hidden_size ,		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 ,		attention_probs_dropout_prob=self.hidden_dropout_prob ,		attention_dropout=self.attention_probs_dropout_prob ,		max_position_embeddings=self.max_position_embeddings ,		initializer_range=self.initializer_range ,		q_groups=self.q_groups ,		k_groups=self.k_groups ,		v_groups=self.v_groups ,		post_attention_groups=self.post_attention_groups ,		intermediate_groups=self.intermediate_groups ,		output_groups=self.output_groups ,		)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			SqueezeBertModel(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		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       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForMaskedLM(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		labels=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       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForQuestionAnswering(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		attention_mask=A ,		start_positions=A ,		end_positions=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 ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForSequenceClassification(A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForTokenClassification(config=A       )
        model.to(A       )
        model.eval()

        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		labels=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       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.num_choices
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForMultipleChoice(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			input_ids.unsqueeze(1       ).expand(-1 ,		self.num_choices ,		-1       ).contiguous()
        _SCREAMING_SNAKE_CASE									=			input_mask.unsqueeze(1       ).expand(-1 ,		self.num_choices ,		-1       ).contiguous()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		attention_mask=A ,		labels=A ,		)
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_choices)       )





    def 			snake_case_(		self       )		->   Dict:
        _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))									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict




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

    '''simple docstring'''
    UpperCamelCase     = (
        (
            SqueezeBertModel,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    UpperCamelCase     = (
        {
            '''feature-extraction''': SqueezeBertModel,
            '''fill-mask''': SqueezeBertForMaskedLM,
            '''question-answering''': SqueezeBertForQuestionAnswering,
            '''text-classification''': SqueezeBertForSequenceClassification,
            '''token-classification''': SqueezeBertForTokenClassification,
            '''zero-shot''': SqueezeBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCamelCase     = False
    UpperCamelCase     = True
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			SqueezeBertModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		dim=37       )


    def 			snake_case_(		self       )		->   List[Any]:
        self.config_tester.run_common_tests()


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


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


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*A       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A       )


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


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





    @slow
    def 			snake_case_(		self       )		->   Optional[Any]:
        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			SqueezeBertModel.from_pretrained(A       )
            self.assertIsNotNone(A       )




@require_sentencepiece
@require_tokenizers
@require_torch
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @slow
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""       )

        _SCREAMING_SNAKE_CASE									=			torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]       )
        _SCREAMING_SNAKE_CASE									=			model(A       )[0]
        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 3)       )
        self.assertEqual(output.shape ,		A       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor([[0.6401, -0.0349, -0.6041]]       )
        self.assertTrue(torch.allclose(A ,		A ,		atol=1e-4       )       )










'''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,
      )



