


from random import randint
from tempfile import TemporaryFile

import numpy as np





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	A             =					0
	if start < end:
		A             =					randint(UpperCamelCase__				,       UpperCamelCase__    )
		A             =					a[end]
		A             =					a[pivot]
		A             =					temp

		A       ,      A             =					_in_place_partition(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
		count += _in_place_quick_sort(UpperCamelCase__				,       UpperCamelCase__				,       p - 1    )
		count += _in_place_quick_sort(UpperCamelCase__				,       p + 1				,       UpperCamelCase__    )
	return count





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Tuple    )			-> Tuple:





	"""simple docstring"""







	A             =					0
	A             =					randint(UpperCamelCase__				,       UpperCamelCase__    )
	A             =					a[end]
	A             =					a[pivot]
	A             =					temp
	A             =					start - 1
	for index in range(UpperCamelCase__				,       UpperCamelCase__    ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			A             =					new_pivot_index + 1
			A             =					a[new_pivot_index]
			A             =					a[index]
			A             =					temp

	A             =					a[new_pivot_index + 1]
	A             =					a[end]
	A             =					temp
	return new_pivot_index + 1, count


_lowercase							:   Optional[int]						=						TemporaryFile()
_lowercase							:   List[str]						=						100  # 1000 elements are to be sorted


_lowercase						,				_lowercase							:   Union[str, Any]						=						0, 1  # mean and standard deviation
_lowercase							:   str						=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_lowercase							:   int						=						np.load(outfile)
_lowercase							:   Dict						=						len(M) - 1
_lowercase							:   Any						=						_in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)
print(z)







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							:   Union[str, 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					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

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

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

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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

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

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					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 	_lowerCAmelCase					( )			-> Union[str, 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 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        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]				,       ),
	    ]    )



