
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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_5_0, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ] )
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				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=lowercase      ,  )
				assert hasattr(self      ,  'env'     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
				# distributed data settings
				_snake_case          =      {'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

				# 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=lowercase      ,  instance_count=lowercase      ,  instance_type=self.instance_type      ,  debugger_hook_config=lowercase      ,  hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}      ,  metric_definitions=self.env.metric_definitions      ,  distribution=lowercase      ,  py_version='py36'      ,  )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Any     ):


				'''simple docstring'''


				TrainingJobAnalytics(lowercase     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )





	@parameterized.expand([(2,)]     )
	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.create_estimator(lowercase     )

				# run training
				estimator.fit()

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

				# extract kpis
				_snake_case          =      list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']     )
				_snake_case          =      list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']     )
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				_snake_case          =      (
				    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}      ,  lowercase     )

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)