
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A		: List[str]								=     logging.get_logger(__name__)



class    a_ (			_a    ):
			a	:			List[Any]					   =							['''pixel_values''']






			def __init__(      self				,			__UpperCamelCase = True				,			__UpperCamelCase = 1 / 255				,			__UpperCamelCase = True				,			__UpperCamelCase = 8				,			**__UpperCamelCase				,			):
									super().__init__(**__UpperCamelCase  )

									_lowercase					       =					do_rescale
									_lowercase					       =					rescale_factor
									_lowercase					       =					do_pad
									_lowercase					       =					pad_size






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = None				,			**__UpperCamelCase  ):
									return rescale(__UpperCamelCase				,			scale=__UpperCamelCase				,			data_format=__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									_lowercase       ,						_lowercase					       =					get_image_size(__UpperCamelCase  )
									_lowercase					       =					(old_height // size + 1) * size - old_height
									_lowercase					       =					(old_width // size + 1) * size - old_width

									return pad(__UpperCamelCase				,			((0, pad_height), (0, pad_width))				,			mode="""symmetric"""				,			data_format=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = ChannelDimension.FIRST				,			**__UpperCamelCase				,			):
									_lowercase					       =					do_rescale if do_rescale is not None else self.do_rescale
									_lowercase					       =					rescale_factor if rescale_factor is not None else self.rescale_factor
									_lowercase					       =					do_pad if do_pad is not None else self.do_pad
									_lowercase					       =					pad_size if pad_size is not None else self.pad_size

									_lowercase					       =					make_list_of_images(__UpperCamelCase  )

									if not valid_images(__UpperCamelCase  ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

									# All transformations expect numpy arrays.
									_lowercase					       =					[to_numpy_array(__UpperCamelCase  ) for image in images]

									if do_rescale:
															_lowercase					       =					[self.rescale(image=__UpperCamelCase				,			scale=__UpperCamelCase  ) for image in images]

									if do_pad:
															_lowercase					       =					[self.pad(__UpperCamelCase				,			size=__UpperCamelCase  ) for image in images]

									_lowercase					       =					[to_channel_dimension_format(__UpperCamelCase				,			__UpperCamelCase  ) for image in images]

									_lowercase					       =					{"""pixel_values""": images}
									return BatchFeature(data=__UpperCamelCase				,			tensor_type=__UpperCamelCase  )

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  )