






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


__UpperCamelCase   :   Optional[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 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				int							,					_lowerCAmelCase   :				Dict      )		->			Optional[Any]:
		"""simple docstring"""







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







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				List[str]      )		->			int:
		"""simple docstring"""







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


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				int							,					_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				List[Any]							,					_lowerCAmelCase   :				Any      )		->			List[str]:
		"""simple docstring"""







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

		__lowercase   							=	transforms







	def __len__(	self   :				int      )		->			Tuple:
		"""simple docstring"""







		return len(self.data      )







	def __getitem__(	self   :				Any							,					_lowerCAmelCase   :				Any      )		->			str:
		"""simple docstring"""







		__lowercase   							=	torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]							,					add_special_tokens=_lowerCAmelCase      )      )
		__lowercase							,    __lowercase							,    __lowercase   							=	sentence[0], sentence[1:-1], sentence[-1]
		__lowercase   							=	sentence[: self.max_seq_length]

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

		__lowercase   							=	Image.open(os.path.join(self.data_dir							,					self.data[index]["""img"""]      )      ).convert("""RGB"""      )
		__lowercase   							=	self.transforms(_lowerCAmelCase      )

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







	def 			_a (	self   :				List[Any]      )		->			Any:
		"""simple docstring"""







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





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[len(row["""sentence"""]       ) for row in batch]
	__lowercase							,    __lowercase   							=	len(lowerCamelCase       ), max(lowerCamelCase       )

	__lowercase   							=	torch.zeros(lowerCamelCase					,	lowerCamelCase					,	dtype=torch.long       )
	__lowercase   							=	torch.zeros(lowerCamelCase					,	lowerCamelCase					,	dtype=torch.long       )

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

	__lowercase   							=	torch.stack([row["""image"""] for row in batch]       )
	__lowercase   							=	torch.stack([row["""label"""] for row in batch]       )
	__lowercase   							=	torch.stack([row["""image_start_token"""] for row in batch]       )
	__lowercase   							=	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 					snake_case	(  ):




	'''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 					snake_case	(  ):




	'''simple docstring'''







	return transforms.Compose(
	    [
	        transforms.Resize(256       ),
	        transforms.CenterCrop(224       ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46777044, 0.44531429, 0.40661017]					,	std=[0.12221994, 0.12145835, 0.14380469]					,	),
	    ]       )





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 A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             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 A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =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}					)

             _lowerCamelCase			:						List[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 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						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
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						Dict            =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
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						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
             _lowerCamelCase			:						Tuple            =image_processing(lowercase_     ,  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'],
                 )     ,  )


