


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=sys.maxsize       )     ->   str:
      _lowercase							      ='''bilinear'''
      _lowercase							      =max_size
      _lowercase							      =short_edge_length





     def __call__(self		,     UpperCAmelCase       )     ->   int:
      _lowercase							      =[]
      for img in imgs:
       _lowercase      ,    _lowercase							      =img.shape[:2]
       # later: provide list and randomly choose index for resize
       _lowercase							      =np.random.randint(self.short_edge_length[0]		,     self.short_edge_length[1] + 1       )
       if size == 0:
        return img
       _lowercase							      =size * 1.0 / min(UpperCAmelCase		,     UpperCAmelCase       )
       if h < w:
        _lowercase      ,    _lowercase							      =size, scale * w
       else:
        _lowercase      ,    _lowercase							      =scale * h, size
       if max(UpperCAmelCase		,     UpperCAmelCase       ) > self.max_size:
        _lowercase							      =self.max_size * 1.0 / max(UpperCAmelCase		,     UpperCAmelCase       )
        _lowercase							      =newh * scale
        _lowercase							      =neww * scale
       _lowercase							      =int(neww + 0.5       )
       _lowercase							      =int(newh + 0.5       )

       if img.dtype == np.uinta:
        _lowercase							      =Image.fromarray(UpperCAmelCase       )
        _lowercase							      =pil_image.resize((neww, newh)		,     PILImageResampling.BILINEAR       )
        _lowercase							      =np.asarray(UpperCAmelCase       )
       else:
        _lowercase							      =img.permute(2		,     0		,     1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
        _lowercase							      =nn.functional.interpolate(
            UpperCAmelCase		,     (newh, neww)		,     mode=self.interp_method		,     align_corners=UpperCAmelCase       ).squeeze(0       )
       img_augs.append(UpperCAmelCase       )

      return img_augs







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,     cfg.INPUT.MAX_SIZE_TEST       )
      _lowercase							      =cfg.INPUT.FORMAT
      _lowercase							      =cfg.SIZE_DIVISIBILITY
      _lowercase							      =cfg.PAD_VALUE
      _lowercase							      =cfg.INPUT.MAX_SIZE_TEST
      _lowercase							      =cfg.MODEL.DEVICE
      _lowercase							      =torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,     1		,     1       )
      _lowercase							      =torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,     1		,     1       )
      _lowercase							      =lambda UpperCAmelCase       : (x - self.pixel_mean) / self.pixel_std





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =tuple(max(UpperCAmelCase       ) for s in zip(*[img.shape for img in images]       )       )
      _lowercase							      =[im.shape[-2:] for im in images]
      _lowercase							      =[
          nn.functional.pad(
              UpperCAmelCase		,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,     value=self.pad_value		,     )
          for size, im in zip(UpperCAmelCase		,     UpperCAmelCase       )
      ]

      return torch.stack(UpperCAmelCase       ), torch.tensor(UpperCAmelCase       )





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase=False       )     ->   str:
      with torch.no_grad():
       if not isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =[images]
       if single_image:
        assert len(UpperCAmelCase       ) == 1
       for i in range(len(UpperCAmelCase       )       ):
        if isinstance(images[i]		,     torch.Tensor       ):
         images.insert(UpperCAmelCase		,     images.pop(UpperCAmelCase       ).to(self.device       ).float()       )
        elif not isinstance(images[i]		,     torch.Tensor       ):
         images.insert(
             UpperCAmelCase		,     torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase       )		,     input_format=self.input_format       )       )
             .to(self.device       )
             .float()		,     )
            # resize smallest edge
       _lowercase							      =torch.tensor([im.shape[:2] for im in images]       )
       _lowercase							      =self.aug(UpperCAmelCase       )
       # transpose images and convert to torch tensors
       # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
       # now normalize before pad to avoid useless arithmetic
       _lowercase							      =[self.normalizer(UpperCAmelCase       ) for x in images]
       # now pad them to do the following operations
       _lowercase      ,    _lowercase							      =self.pad(UpperCAmelCase       )
       # Normalize

       if self.size_divisibility > 0:
        raise NotImplementedError()
       # pad
       _lowercase							      =torch.true_divide(UpperCAmelCase		,     UpperCAmelCase       )
       if single_image:
        return images[0], sizes[0], scales_yx[0]
       else:
        return images, sizes, scales_yx



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 assert torch.isfinite(__snake_case						).all(), "Box tensor contains infinite or NaN!"
 _lowercase      ,    _lowercase							      =box_size
 tensor[:, 0].clamp_(min=0     ,						max=__snake_case						)
 tensor[:, 1].clamp_(min=0     ,						max=__snake_case						)
 tensor[:, 2].clamp_(min=0     ,						max=__snake_case						)
 tensor[:, 3].clamp_(min=0     ,						max=__snake_case						)



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_600, """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_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]			)
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							Optional[int]  )     ->							List[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=SCREAMING_SNAKE_CASE__							,			)
						assert hasattr(self							,			'''env'''  )






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Optional[Any]:
						# configuration for running training on smdistributed Model Parallel
						__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=SCREAMING_SNAKE_CASE__							,			instance_type=self.instance_type							,			debugger_hook_config=SCREAMING_SNAKE_CASE__							,			hyperparameters={
						        **self.env.hyperparameters,
						        '''model_name_or_path''': self.model_name_or_path,
						        '''max_steps''': 5_00,
						    }							,			metric_definitions=self.env.metric_definitions							,			distribution=SCREAMING_SNAKE_CASE__							,			py_version='''py36'''							,			)






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							List[str]:
						TrainingJobAnalytics(SCREAMING_SNAKE_CASE__  ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''  )







		@parameterized.expand([(1,)]  )
		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Optional[int]:
						# create estimator
						__lowerCamelCase    					=	self.create_estimator(SCREAMING_SNAKE_CASE__  )

						# 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'''							,			99_99_99  )
						)

						# 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}							,			SCREAMING_SNAKE_CASE__  )



