



'''simple docstring'''




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
						import torch






class 						_UpperCamelCase  (							__lowercase    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		'''char'''
			_A      :		str			    =		'''bpe'''
			_A      :		str			    =		'''wp'''
UpperCamelCase__       :	int    = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 						_UpperCamelCase  (							__lowercase    ):


			'''simple docstring'''

			_A      :		Optional[Any]			    =		['''image_processor''', '''char_tokenizer''']
			_A      :		Any			    =		'''ViTImageProcessor'''
			_A      :		List[str]			    =		'''MgpstrTokenizer'''

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : str=None							,     lowerCAmelCase__     : List[str]=None							,     **lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,     lowerCAmelCase__							,     )
							__SCREAMING_SNAKE_CASE       :		List[str]           =		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`."""						)
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`."""						)

					__SCREAMING_SNAKE_CASE       :		str           =		tokenizer
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		AutoTokenizer.from_pretrained("""gpt2"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		AutoTokenizer.from_pretrained("""bert-base-uncased"""						)

					super().__init__(lowerCAmelCase__							,     lowerCAmelCase__						)

			def __call__(							self     : List[str]							,     lowerCAmelCase__     : Any=None							,     lowerCAmelCase__     : Optional[int]=None							,     lowerCAmelCase__     : Tuple=None							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					if images is None and text is None:
							raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

					if images is not None:
							__SCREAMING_SNAKE_CASE       :		int           =		self.image_processor(lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     **lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.char_tokenizer(lowerCAmelCase__							,     return_tensors=lowerCAmelCase__							,     **lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		encodings["input_ids"]
							return inputs

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		sequences
					__SCREAMING_SNAKE_CASE       :		Dict           =		char_preds.size(0						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		self._decode_helper(lowerCAmelCase__							,     """char"""						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._decode_helper(lowerCAmelCase__							,     """bpe"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		self._decode_helper(lowerCAmelCase__							,     """wp"""						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		[]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[]
					for i in range(lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Dict           =		[char_scores[i], bpe_scores[i], wp_scores[i]]
							__SCREAMING_SNAKE_CASE       :		Dict           =		[char_strs[i], bpe_strs[i], wp_strs[i]]
							__SCREAMING_SNAKE_CASE       :		Any           =		scores.index(max(lowerCAmelCase__						)						)
							final_strs.append(strs[max_score_index]						)
							final_scores.append(scores[max_score_index]						)

					__SCREAMING_SNAKE_CASE       :		int           =		{}
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		final_strs
					__SCREAMING_SNAKE_CASE       :		Any           =		final_scores
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		char_strs
					__SCREAMING_SNAKE_CASE       :		Tuple           =		bpe_strs
					__SCREAMING_SNAKE_CASE       :		Dict           =		wp_strs
					return out

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					if format == DecodeType.CHARACTER:
							__SCREAMING_SNAKE_CASE       :		str           =		self.char_decode
							__SCREAMING_SNAKE_CASE       :		str           =		1
							__SCREAMING_SNAKE_CASE       :		int           =		"[s]"
					elif format == DecodeType.BPE:
							__SCREAMING_SNAKE_CASE       :		Dict           =		self.bpe_decode
							__SCREAMING_SNAKE_CASE       :		Any           =		2
							__SCREAMING_SNAKE_CASE       :		str           =		"#"
					elif format == DecodeType.WORDPIECE:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.wp_decode
							__SCREAMING_SNAKE_CASE       :		int           =		1_0_2
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		"[SEP]"
					else:
							raise ValueError(F"Format {format} is not supported."						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[], []
					__SCREAMING_SNAKE_CASE       :		Dict           =		pred_logits.size(0						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pred_logits.size(1						)
					__SCREAMING_SNAKE_CASE       :		int           =		pred_logits.topk(1							,     dim=-1							,     largest=lowerCAmelCase__							,     sorted=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		preds_index.view(-1							,     lowerCAmelCase__						)[:, 1:]
					__SCREAMING_SNAKE_CASE       :		Dict           =		decoder(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		torch.nn.functional.softmax(lowerCAmelCase__							,     dim=2						).max(dim=2						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		preds_max_prob[:, 1:]

					for index in range(lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		List[str]           =		preds_str[index].find(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		List[str]           =		preds_str[index][:pred_eos]
							__SCREAMING_SNAKE_CASE       :		Any           =		preds_index[index].cpu().tolist()
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		pred_index.index(lowerCAmelCase__						) if eos_token in pred_index else -1
							__SCREAMING_SNAKE_CASE       :		Any           =		preds_max_prob[index][: pred_eos_index + 1]
							__SCREAMING_SNAKE_CASE       :		Tuple           =		pred_max_prob.cumprod(dim=0						)[-1] if pred_max_prob.nelement() != 0 else 0.0
							dec_strs.append(lowerCAmelCase__						)
							conf_scores.append(lowerCAmelCase__						)

					return dec_strs, conf_scores

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		[seq.replace(""" """							,     """"""						) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__						)]
					return decode_strs

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					return self.bpe_tokenizer.batch_decode(lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[seq.replace(""" """							,     """"""						) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__						)]
					return decode_strs

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  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[Any]						)		-> Any:
								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=a     ,   )
								assert hasattr(self     ,   "env"						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:Optional[Any]						)		-> Dict:
								__UpperCamelCase	:   str			   =					f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
								# distributed data settings
								__UpperCamelCase	:   Optional[int]			   =					{"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=a     ,   instance_count=a     ,   instance_type=self.instance_type     ,   debugger_hook_config=a     ,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}     ,   metric_definitions=self.env.metric_definitions     ,   distribution=a     ,   py_version="py36"     ,   )







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> Optional[int]:
								TrainingJobAnalytics(a						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)







	@parameterized.expand([(2,)]						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Tuple						)		-> List[Any]:
								# create estimator
								__UpperCamelCase	:   int			   =					self.create_estimator(a						)

								# run training
								estimator.fit()

								# result dataframe
								__UpperCamelCase	:   Optional[int]			   =					TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

								# extract kpis
								__UpperCamelCase	:   Optional[Any]			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
								__UpperCamelCase	:   Tuple			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								__UpperCamelCase	:   int			   =					(
								    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"     ,   9_9_9_9_9_9						)
								)

								# 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}     ,   a						)