import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase_     :   Optional[Any] 				=				[
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     SCREAMING_SNAKE_CASE__:   Dict=      os.path.abspath(os.path.join('''examples''' , '''by_feature'''   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath('''examples'''   )
     for item in os.listdir(lowerCAmelCase   ):
      if item not in EXCLUDE_EXAMPLES:
       SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(lowerCAmelCase , lowerCAmelCase   )
       if os.path.isfile(lowerCAmelCase   ) and ".py" in item_path:
        with self.subTest(
            tested_script=lowerCAmelCase , feature_script=lowerCAmelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
         SCREAMING_SNAKE_CASE__:   List[Any]=      compare_against_test(
             os.path.join(lowerCAmelCase , lowerCAmelCase   ) , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
         SCREAMING_SNAKE_CASE__:   str=      '''\n'''.join(lowerCAmelCase   )
         if special_strings is not None:
          for string in special_strings:
           SCREAMING_SNAKE_CASE__:   Union[str, Any]=      diff.replace(lowerCAmelCase , ''''''   )
         self.assertEqual(lowerCAmelCase , ''''''   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )



    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath(os.path.join('''examples''' , '''cv_example.py'''   )   )
     SCREAMING_SNAKE_CASE__:   Dict=      [
         ''' ''' * 16 + '''{\n\n''',
         ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
         ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
         ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
         ''' ''' * 20 + '''"epoch": epoch,\n\n''',
         ''' ''' * 16 + '''},\n\n''',
         ''' ''' * 16 + '''step=epoch,\n''',
         ''' ''' * 12,
         ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
     ]
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )






@mock.patch.dict(os.environ    ,     {"TESTING_MOCKED_DATALOADERS": "1"}    )
class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      False
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().setUpClass()
     SCREAMING_SNAKE_CASE__:   List[Any]=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(cls._tmpdir , '''default_config.yml'''   )

     write_basic_config(save_location=cls.configPath   )
     SCREAMING_SNAKE_CASE__:   Dict=      ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().tearDownClass()
     shutil.rmtree(cls._tmpdir   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
     run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   List[Any]=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
     SCREAMING_SNAKE_CASE__:   List[Any]=      run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Tuple=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
     self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     if torch.cuda.is_available():
      SCREAMING_SNAKE_CASE__:   List[str]=      torch.cuda.device_count()
     else:
      SCREAMING_SNAKE_CASE__:   List[Any]=      1
     if num_processes > 1:
      self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
     else:
      self.assertIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
     with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
      SCREAMING_SNAKE_CASE__:   Any=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Dict=      re.findall('''({.+})''' , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      [r for r in results if '''accuracy''' in r][-1]
      SCREAMING_SNAKE_CASE__:   List[str]=      ast.literal_eval(lowerCAmelCase   )
      self.assertGreaterEqual(results['''accuracy'''] , 0.75   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      ['''examples/by_feature/multi_process_metrics.py''']
     run_command(self._launch_args + testargs   )
    @require_trackers
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''}   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     with tempfile.TemporaryDirectory() as tmpdir:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
      run_command(self._launch_args + testargs   )
      self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase , '''tracking'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   int=      ['''examples/by_feature/gradient_accumulation.py''']
     run_command(self._launch_args + testargs   )



    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      ['''examples/by_feature/local_sgd.py''']
     run_command(self._launch_args + testargs   )


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_     :   List[str] 				=				{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 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

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



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      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__      (	):
 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__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

