import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=3 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=0.9 , lowerCAmelCase=None , lowerCAmelCase=True , lowerCAmelCase=[0.5, 0.5, 0.5] , lowerCAmelCase=[0.5, 0.5, 0.5] , )       ->					str:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      size if size is not None else {'''shortest_edge''': 30}
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
     SCREAMING_SNAKE_CASE__:   List[str]=      parent
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      batch_size
     SCREAMING_SNAKE_CASE__:   str=      num_channels
     SCREAMING_SNAKE_CASE__:   Any=      min_resolution
     SCREAMING_SNAKE_CASE__:   Optional[int]=      max_resolution
     SCREAMING_SNAKE_CASE__:   Dict=      do_resize_and_center_crop
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      size
     SCREAMING_SNAKE_CASE__:   List[Any]=      crop_pct
     SCREAMING_SNAKE_CASE__:   List[str]=      crop_size
     SCREAMING_SNAKE_CASE__:   List[str]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Dict=      image_mean
     SCREAMING_SNAKE_CASE__:   Tuple=      image_std



    def 						UpperCamelCase_				(  self   )       ->					Any:
     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      PoolFormerImageProcessor if is_vision_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Any=      PoolFormerImageProcessingTester(self   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase , '''do_resize_and_center_crop'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''size'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''crop_pct'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''do_normalize'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''image_mean'''   )   )
     self.assertTrue(hasattr(lowerCAmelCase , '''image_std'''   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size , {'''shortest_edge''': 30}   )
     self.assertEqual(image_processor.crop_size , {'''height''': 30, '''width''': 30}   )

     SCREAMING_SNAKE_CASE__:   Any=      self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84   )
     self.assertEqual(image_processor.size , {'''shortest_edge''': 42}   )
     self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84}   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     pass
    def 						UpperCamelCase_				(  self   )       ->					str:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     SCREAMING_SNAKE_CASE__:   Dict=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , Image.Image   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   Optional[int]=      image_processing(image_inputs[0] , return_tensors='''pt'''   ).pixel_values

     self.assertEqual(
         encoded_images.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )

     # Test batched
     SCREAMING_SNAKE_CASE__:   List[Any]=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__:   str=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , np.ndarray   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   List[str]=      image_processing(image_inputs[0] , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )

     # Test batched
     SCREAMING_SNAKE_CASE__:   int=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )



    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase   )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase , torch.Tensor   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   int=      image_processing(image_inputs[0] , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Any=      image_processing(lowerCAmelCase , return_tensors='''pt'''   ).pixel_values
     self.assertEqual(
         encoded_images.shape , (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) , )


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]       ,				),
     ]      )

