

"""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)
__UpperCamelCase    : List[str]				 =						logging.getLogger(__name__)


__UpperCamelCase    : str				 =						'''Hello world! cécé herlolip'''


__UpperCamelCase    : Dict				 =						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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Any    							=      BertAbsConfig(
	    temp_dir='''.'''		,	finetune_bert=SCREAMING_SNAKE_CASE_		,	large=SCREAMING_SNAKE_CASE_		,	share_emb=SCREAMING_SNAKE_CASE_		,	use_bert_emb=SCREAMING_SNAKE_CASE_		,	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		,	)
	lowerCAmelCase__      :		Union[str, Any]    							=      torch.load(SCREAMING_SNAKE_CASE_		,	lambda A_		,	A_					: storage					)
	lowerCAmelCase__      :		Any    							=      AbsSummarizer(SCREAMING_SNAKE_CASE_		,	torch.device('''cpu'''					)		,	SCREAMING_SNAKE_CASE_					)
	original.eval()

	lowerCAmelCase__      :		Tuple    							=      BertAbsSummarizer(SCREAMING_SNAKE_CASE_		,	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'''					)
	lowerCAmelCase__      :		List[str]    							=      BertTokenizer.from_pretrained('''bert-base-uncased'''					)

	# prepare the model inputs
	lowerCAmelCase__      :		Optional[int]    							=      tokenizer.encode('''This is sample éàalj\'-.'''					)
	encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(SCREAMING_SNAKE_CASE_					))					)
	lowerCAmelCase__      :		str    							=      torch.tensor(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
	lowerCAmelCase__      :		Dict    							=      tokenizer.encode('''This is sample 3 éàalj\'-.'''					)
	decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(SCREAMING_SNAKE_CASE_					))					)
	lowerCAmelCase__      :		str    							=      torch.tensor(SCREAMING_SNAKE_CASE_					).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
	lowerCAmelCase__      :		Any    							=      encoder_input_ids
	lowerCAmelCase__      :		Dict    							=      decoder_input_ids
	lowerCAmelCase__      :		int    							=      None
	lowerCAmelCase__      :		Union[str, Any]    							=      None
	lowerCAmelCase__      :		Optional[Any]    							=      None
	lowerCAmelCase__      :		Tuple    							=      None
	lowerCAmelCase__      :		Optional[int]    							=      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
	lowerCAmelCase__      :		int    							=      original(SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_					)[0]
	lowerCAmelCase__      :		str    							=      original.generator(SCREAMING_SNAKE_CASE_					)

	lowerCAmelCase__      :		Tuple    							=      new_model(
	    SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_					)[0]
	lowerCAmelCase__      :		List[Any]    							=      new_model.generator(SCREAMING_SNAKE_CASE_					)

	lowerCAmelCase__      :		Dict    							=      torch.max(torch.abs(output_converted_model - output_original_model					)					).item()
	print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE_					)					)
	lowerCAmelCase__      :		List[str]    							=      torch.max(torch.abs(output_converted_generator - output_original_generator					)					).item()
	print('''Maximum absolute difference beween weights: {:.2f}'''.format(SCREAMING_SNAKE_CASE_					)					)

	lowerCAmelCase__      :		List[str]    							=      torch.allclose(SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	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    : List[str]				 =						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    : Tuple				 =						parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''					,			'''False'''    )    ) is not True					,			reason='''Skipping test because should only be run when releasing minor transformers version'''					,			)
@pytest.mark.usefixtures('''sm_env'''    )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]    )
class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									if self.framework == "pytorch":
															subprocess.run(
															    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,			encoding="""utf-8"""				,			check=__UpperCamelCase				,			)
									assert hasattr(self				,			"""env"""  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# configuration for running training on smdistributed Model Parallel
									_lowercase					       =					{
									    """enabled""": True,
									    """processes_per_host""": 8,
									}
									_lowercase					       =					{
									    """enabled""": True,
									    """parameters""": {
									        """microbatches""": 4,
									        """placement_strategy""": """spread""",
									        """pipeline""": """interleaved""",
									        """optimize""": """speed""",
									        """partitions""": 4,
									        """ddp""": True,
									    },
									}

									_lowercase					       =					{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

									_lowercase					       =					"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,			source_dir=self.env.test_path				,			role=self.env.role				,			image_uri=self.env.image_uri				,			base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""				,			instance_count=__UpperCamelCase				,			instance_type=self.instance_type				,			debugger_hook_config=__UpperCamelCase				,			hyperparameters={
									        **self.env.hyperparameters,
									        """model_name_or_path""": self.model_name_or_path,
									        """max_steps""": 500,
									    }				,			metric_definitions=self.env.metric_definitions				,			distribution=__UpperCamelCase				,			py_version="""py36"""				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									TrainingJobAnalytics(__UpperCamelCase  ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""  )






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

									# result dataframe
									_lowercase					       =					TrainingJobAnalytics(estimator.latest_training_job.name  ).dataframe()

									# extract kpis
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_lowercase					       =					(
									    Session().describe_training_job(estimator.latest_training_job.name  ).get("""TrainingTimeInSeconds"""				,			999_999  )
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy  )
									assert all(t <= self.results["""eval_loss"""] for t in eval_loss  )

									# dump tests result into json file to share in PR
									with open(f"""{estimator.latest_training_job.name}.json"""				,			"""w"""  ) as outfile:
															json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}				,			__UpperCamelCase  )