

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


__lowercase     : Tuple       =						{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 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Optional[int]        =		torchvision.models.resnetaaa(pretrained=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		list(model.children()    )[:-2]
      snake_case							:		Union[str, Any]        =		nn.Sequential(*SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      snake_case							:		Optional[int]        =		self.pool(self.model(SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[int]        =		torch.flatten(SCREAMING_SNAKE_CASE_   ,start_dim=2    )
      snake_case							:		Optional[Any]        =		out.transpose(1   ,2    ).contiguous()
      return out  # BxNx2048







class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		[json.loads(SCREAMING_SNAKE_CASE_    ) for l in open(SCREAMING_SNAKE_CASE_    )]
      snake_case							:		Any        =		os.path.dirname(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		tokenizer
      snake_case							:		Optional[int]        =		labels
      snake_case							:		Tuple        =		len(SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		max_seq_length

      snake_case							:		Tuple        =		transforms





  def __len__(  self    ):

      '''simple docstring'''
      return len(self.data    )





  def __getitem__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]   ,add_special_tokens=SCREAMING_SNAKE_CASE_    )    )
      snake_case      ,		snake_case      ,		snake_case							:		Tuple        =		sentence[0], sentence[1:-1], sentence[-1]
      snake_case							:		Dict        =		sentence[: self.max_seq_length]

      snake_case							:		Union[str, Any]        =		torch.zeros(self.n_classes    )
      snake_case							:		Dict        =		1

      snake_case							:		List[Any]        =		Image.open(os.path.join(self.data_dir   ,self.data[index]["""img"""]    )    ).convert("""RGB"""    )
      snake_case							:		Optional[int]        =		self.transforms(SCREAMING_SNAKE_CASE_    )

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		Counter()
      for row in self.data:
          label_freqs.update(row["""label"""]    )
      return label_freqs







def       lowercase					(	__A :					List[Any]		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		List[str]        =		[len(row["""sentence"""]		) for row in batch]
    snake_case      ,		snake_case							:		Any        =		len(__A		), max(__A		)

    snake_case							:		Optional[Any]        =		torch.zeros(__A ,    __A ,    dtype=torch.long		)
    snake_case							:		List[str]        =		torch.zeros(__A ,    __A ,    dtype=torch.long		)

    for i_batch, (input_row, length) in enumerate(zip(__A ,    __A		)		):
        snake_case							:		List[Any]        =		input_row["""sentence"""]
        snake_case							:		List[Any]        =		1

    snake_case							:		List[Any]        =		torch.stack([row["""image"""] for row in batch]		)
    snake_case							:		int        =		torch.stack([row["""label"""] for row in batch]		)
    snake_case							:		int        =		torch.stack([row["""image_start_token"""] for row in batch]		)
    snake_case							:		List[Any]        =		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       lowercase					(	)				->			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       lowercase					(	)				->			Tuple:





    '''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 unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ComputeEnvironment.AMAZON_SAGEMAKER
      UpperCamelCase_             =      True
      UpperCamelCase_             =      '''ml.p3.2xlarge'''
      UpperCamelCase_             =      '''accelerate_sagemaker_execution_role'''
      UpperCamelCase_             =      '''hf-sm'''
      UpperCamelCase_             =      '''us-east-1'''
      UpperCamelCase_             =      1
      UpperCamelCase_             =      '''accelerate-sagemaker-1'''
      UpperCamelCase_             =      '''1.6'''
      UpperCamelCase_             =      '''4.4'''
      UpperCamelCase_             =      '''train.py'''
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''False''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''--do_test''',
          '''False''',
          '''--do_predict''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]





class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		int		)	->	Dict:


       '''simple docstring'''

       lowercase       : str						=_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
       assert isinstance(converted_args['''model_name_or_path''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''do_train''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''epochs''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''learning_rate''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''max_steps''']  ,   UpperCAmelCase		)

       with pytest.raises(UpperCAmelCase		):
        _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)



