
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


lowerCAmelCase__							=				{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  snake_case     (							nn.Module   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







class  snake_case     (							__snake_case   ):

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

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = transforms

							def __len__(self    ):

									"""simple docstring"""







									return len(self.data    )

							def __getitem__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']       ,       add_special_tokens=__UpperCamelCase    )    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = sentence[0], sentence[1:-1], sentence[-1]
									SCREAMING_SNAKE_CASE_						 = sentence[: self.max_seq_length]

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

									SCREAMING_SNAKE_CASE_						 = Image.open(os.path.join(self.data_dir       ,       self.data[index]['''img''']    )    ).convert('''RGB'''    )
									SCREAMING_SNAKE_CASE_						 = self.transforms(__UpperCamelCase    )

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







							def _lowercase					(self    ):

									"""simple docstring"""







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







def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = [len(row['''sentence''']    ) for row in batch]
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = len(lowercase__    ), max(lowercase__    )

		SCREAMING_SNAKE_CASE_						 = torch.zeros(lowercase__,							lowercase__,							dtype=torch.long    )
		SCREAMING_SNAKE_CASE_						 = torch.zeros(lowercase__,							lowercase__,							dtype=torch.long    )

		for i_batch, (input_row, length) in enumerate(zip(lowercase__,							lowercase__    )    ):
				SCREAMING_SNAKE_CASE_						 = input_row['''sentence''']
				SCREAMING_SNAKE_CASE_						 = 1

		SCREAMING_SNAKE_CASE_						 = torch.stack([row['''image'''] for row in batch]    )
		SCREAMING_SNAKE_CASE_						 = torch.stack([row['''label'''] for row in batch]    )
		SCREAMING_SNAKE_CASE_						 = torch.stack([row['''image_start_token'''] for row in batch]    )
		SCREAMING_SNAKE_CASE_						 = 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 ( ):
		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 ( ):
		return transforms.Compose(
		    [
		        transforms.Resize(256    ),
		        transforms.CenterCrop(224    ),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7],							std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9],							),
		    ]    )

"""simple docstring"""




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return x.sum()



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return i + 1





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = [1, 2]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [1, 2], '''b''': [3, 4]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 1}, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = [2, 3]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [2, 3], '''b''': [4, 5]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 2}, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = 2
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = {'''a''': np.eye(2    ), '''b''': np.zeros(3    ), '''c''': np.ones(2    )}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 0, '''c''': 2}
									SCREAMING_SNAKE_CASE_						 = {
									    '''a''': np.eye(2    ).astype(SCREAMING_SNAKE_CASE_    ),
									    '''b''': np.zeros(3    ).astype(SCREAMING_SNAKE_CASE_    ),
									    '''c''': np.ones(2    ).astype(SCREAMING_SNAKE_CASE_    ),
									}
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_    ).items()}       ,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,       )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    ).items()}       ,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,       )
									with self.assertRaises(SCREAMING_SNAKE_CASE_    ):  # can't pickle a local lambda
											map_nested(lambda SCREAMING_SNAKE_CASE_    : x + 1       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 3, '''b''': 4}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 5, '''b''': 6}
									SCREAMING_SNAKE_CASE_						 = sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]    )
									self.assertEqual(sorted(zip_dict(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									class  snake_case     :
																UpperCAmelCase__						     =      '''bar'''







									SCREAMING_SNAKE_CASE_						 = Foo()
									self.assertEqual(foo.my_attr       ,       '''bar'''    )
									with temporary_assignment(SCREAMING_SNAKE_CASE_       ,       '''my_attr'''       ,       '''BAR'''    ):
											self.assertEqual(foo.my_attr       ,       '''BAR'''    )
									self.assertEqual(foo.my_attr       ,       '''bar'''    )







@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc''',							[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ],							)
def        _lowerCamelCase ( __a,							__a,							__a    ):
		with patch('''datasets.utils.py_utils._single_map_nested'''    ) as mock_single_map_nested, patch(
		    '''datasets.parallel.parallel.Pool'''    ) as mock_multiprocessing_pool:
				SCREAMING_SNAKE_CASE_						 = {F'{i}': i for i in range(__a    )}
				SCREAMING_SNAKE_CASE_						 = map_nested(lambda __a    : x + 10,							__a,							num_proc=__a,							parallel_min_length=16    )
				if expected_num_proc == 1:
						assert mock_single_map_nested.called
						assert not mock_multiprocessing_pool.called
				else:
						assert not mock_single_map_nested.called
						assert mock_multiprocessing_pool.called
						assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class  snake_case     (							__lowercase   ):

							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									import tensorflow as tf
									from tensorflow.keras import layers

									SCREAMING_SNAKE_CASE_						 = layers.Dense(2    )

									def gen_random_output():
											SCREAMING_SNAKE_CASE_						 = tf.random.uniform((1, 3)    )
											return model(SCREAMING_SNAKE_CASE_    ).numpy()

									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									def gen_random_output():
											SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(3       ,       2    )
											SCREAMING_SNAKE_CASE_						 = torch.rand(1       ,       3    )
											return model(SCREAMING_SNAKE_CASE_    ).detach().numpy()

									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )







							def _lowercase					(self    ):

									"""simple docstring"""







									def gen_random_output():
											return np.random.rand(1       ,       3    )

									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )







@pytest.mark.parametrize('''input_data''',							[{}]    )
def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).data
		assert output_data == input_data



@pytest.mark.parametrize(
    '''data, expected_output''',							[
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ],							)
def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).flatten()
		assert output == expected_output



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = A(x=1,							y='''foobar'''    )
		SCREAMING_SNAKE_CASE_						 = {'''x''': 1, '''y''': '''foobar'''}
		assert asdict(__a    ) == expected_output

		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': A(x=10,							y='''foo'''    )}, '''c''': [A(x=20,							y='''bar'''    )]}
		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
		assert asdict(__a    ) == expected_output

		with pytest.raises(__a    ):
				asdict([1, A(x=10,							y='''foo'''    )]    )



def        _lowerCamelCase ( __a    ):
		return text.split()



def        _lowerCamelCase ( __a    ):
		yield (time.time(), content)
		time.sleep(2    )
		yield (time.time(), content)



def        _lowerCamelCase ( ):
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = list(iflatmap_unordered(__a,							_split_text,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10    )    )
				assert out.count('''hello'''    ) == 10
				assert out.count('''there'''    ) == 10
				assert len(__a    ) == 20

		# check multiprocess from pathos (uses dill for pickling)
		with multiprocess.Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = list(iflatmap_unordered(__a,							_split_text,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10    )    )
				assert out.count('''hello'''    ) == 10
				assert out.count('''there'''    ) == 10
				assert len(__a    ) == 20

		# check that we get items as fast as possible
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = []
				for yield_time, content in iflatmap_unordered(
				    __a,							_aseconds_generator_of_aitems_with_timing,							kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]    ):
						assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
						out.append(__a    )
				assert out.count('''a'''    ) == 2
				assert out.count('''b'''    ) == 2
				assert len(__a    ) == 4