'''simple docstring'''




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      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""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=__lowerCAmelCase			,       )
					assert hasattr(self			,       """env""")



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    {
					    """enabled""": True,
					    """processes_per_host""": 8,
					}
					lowerCAmelCase     =    {
					    """enabled""": True,
					    """parameters""": {
					        """microbatches""": 4,
					        """placement_strategy""": """spread""",
					        """pipeline""": """interleaved""",
					        """optimize""": """speed""",
					        """partitions""": 4,
					        """ddp""": True,
					    },
					}

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

					lowerCAmelCase     =    """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=__lowerCAmelCase			,       instance_type=self.instance_type			,       debugger_hook_config=__lowerCAmelCase			,       hyperparameters={
					        **self.env.hyperparameters,
					        """model_name_or_path""": self.model_name_or_path,
					        """max_steps""": 500,
					    }			,       metric_definitions=self.env.metric_definitions			,       distribution=__lowerCAmelCase			,       py_version="""py36"""			,       )



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					TrainingJobAnalytics(__lowerCAmelCase).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")







			@parameterized.expand([(1,)])
			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    self.create_estimator(__lowerCAmelCase)

					# run training
					estimator.fit()

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

					# extract kpis
					lowerCAmelCase     =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
					lowerCAmelCase     =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowerCAmelCase     =    (
					    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""			,       999999)
					)

					# 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}			,       __lowerCAmelCase)





'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    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			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    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`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




