




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


snake_case_ : Any    		=       {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}







class __snake_case			(			nn.Module		):







   def __init__(      self		:						Dict					,						_snake_case		:						int):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							torchvision.models.resnetaaa(pretrained=_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							list(model.children())[:-2]
        UpperCAmelCase_          =							nn.Sequential(*_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.pool(self.model(_SCREAMING_SNAKE_CASE))
        UpperCAmelCase_          =							torch.flatten(_SCREAMING_SNAKE_CASE					,						start_dim=2)
        UpperCAmelCase_          =							out.transpose(1					,						2).contiguous()
        return out  # BxNx2048







class __snake_case			(			__SCREAMING_SNAKE_CASE		):







   def __init__(      self		:						int					,						_snake_case		:						Any					,						_snake_case		:						List[str]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							[json.loads(_SCREAMING_SNAKE_CASE) for l in open(_SCREAMING_SNAKE_CASE)]
        UpperCAmelCase_          =							os.path.dirname(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							tokenizer
        UpperCAmelCase_          =							labels
        UpperCAmelCase_          =							len(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							max_seq_length

        UpperCAmelCase_          =							transforms







   def __len__(      self		:						int):

        """simple docstring"""


        return len(self.data)







   def __getitem__(      self		:						Optional[Any]					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']					,						add_special_tokens=_SCREAMING_SNAKE_CASE))
        UpperCAmelCase_          =							sentence[0], sentence[1:-1], sentence[-1]
        UpperCAmelCase_          =							sentence[: self.max_seq_length]

        UpperCAmelCase_          =							torch.zeros(self.n_classes)
        UpperCAmelCase_          =							1

        UpperCAmelCase_          =							Image.open(os.path.join(self.data_dir					,						self.data[index]['''img'''])).convert('''RGB''')
        UpperCAmelCase_          =							self.transforms(_SCREAMING_SNAKE_CASE)

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							Counter()
        for row in self.data:
             label_freqs.update(row['''label'''])
        return label_freqs







def    A						(__A   :    Union[str, Any]       )   ->      Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[len(row['''sentence''']       ) for row in batch]
     UpperCAmelCase_          =							len(__snake_case       ), max(__snake_case       )

     UpperCAmelCase_          =							torch.zeros(__snake_case   ,				__snake_case   ,				dtype=torch.long       )
     UpperCAmelCase_          =							torch.zeros(__snake_case   ,				__snake_case   ,				dtype=torch.long       )

     for i_batch, (input_row, length) in enumerate(zip(__snake_case   ,				__snake_case       )       ):
          UpperCAmelCase_          =							input_row["sentence"]
          UpperCAmelCase_          =							1

     UpperCAmelCase_          =							torch.stack([row['''image'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''label'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''image_start_token'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''image_end_token'''] for row in batch]       )

     return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor


def    A						()   ->      List[Any]:







     """simple docstring"""


     return [
         "Crime",
         "Drama",
         "Thriller",
         "Action",
         "Comedy",
         "Romance",
         "Documentary",
         "Short",
         "Mystery",
         "History",
         "Family",
         "Adventure",
         "Fantasy",
         "Sci-Fi",
         "Western",
         "Horror",
         "Sport",
         "War",
         "Music",
         "Musical",
         "Animation",
         "Biography",
         "Film-Noir",
     ]


def    A						()   ->      List[str]:







     """simple docstring"""


     return transforms.Compose(
         [
             transforms.Resize(256       ),
             transforms.CenterCrop(224       ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]   ,				std=[0.12_221_994, 0.12_145_835, 0.14_380_469]   ,				),
         ]       )











"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				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}-single"""       ,			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}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# 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)