






'''simple docstring'''

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


SCREAMING_SNAKE_CASE__     =       {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     a_						(      nn.Module    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






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


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






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






class     a_						(      lowerCamelCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






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

        UpperCamelCase      = transforms

  def __len__(				self				)  ->				Any:



        """simple docstring"""






        return len(self.data				)

  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






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

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

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

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


  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






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







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = [len(row["""sentence"""]   ) for row in batch]
      UpperCamelCase  ,UpperCamelCase      = len(__UpperCamelCase   ), max(__UpperCamelCase   )

      UpperCamelCase      = torch.zeros(__UpperCamelCase		,						__UpperCamelCase		,						dtype=torch.long   )
      UpperCamelCase      = torch.zeros(__UpperCamelCase		,						__UpperCamelCase		,						dtype=torch.long   )

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

      UpperCamelCase      = torch.stack([row["""image"""] for row in batch]   )
      UpperCamelCase      = torch.stack([row["""label"""] for row in batch]   )
      UpperCamelCase      = torch.stack([row["""image_start_token"""] for row in batch]   )
      UpperCamelCase      = 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    lowercase__			(				)->			str:
      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    lowercase__			(				)->			List[str]:
      return transforms.Compose(
          [
              transforms.Resize(256   ),
              transforms.CenterCrop(224   ),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]		,						std=[0.12_221_994, 0.12_145_835, 0.14_380_469]		,						),
          ]   )








'''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    lowercase__			(				__UpperCamelCase   )->			Dict:  # picklable for multiprocessing
      return x.sum()







def    lowercase__			(				__UpperCamelCase   )->			Tuple:  # picklable for multiprocessing
      return i + 1






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42






class     a_						(      lowerCamelCase    ):

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 1
        UpperCamelCase      = [1, 2]
        UpperCamelCase      = {"""a""": 1, """b""": 2}
        UpperCamelCase      = {"""a""": [1, 2], """b""": [3, 4]}
        UpperCamelCase      = {"""a""": {"""1""": 1}, """b""": 2}
        UpperCamelCase      = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 2
        UpperCamelCase      = [2, 3]
        UpperCamelCase      = {"""a""": 2, """b""": 3}
        UpperCamelCase      = {"""a""": [2, 3], """b""": [4, 5]}
        UpperCamelCase      = {"""a""": {"""1""": 2}, """b""": 3}
        UpperCamelCase      = {"""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				)

        UpperCamelCase      = 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				)

        UpperCamelCase      = {"""a""": np.eye(2				), """b""": np.zeros(3				), """c""": np.ones(2				)}
        UpperCamelCase      = {"""a""": 2, """b""": 0, """c""": 2}
        UpperCamelCase      = {
            """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   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = {"""a""": 1, """b""": 2}
        UpperCamelCase      = {"""a""": 3, """b""": 4}
        UpperCamelCase      = {"""a""": 5, """b""": 6}
        UpperCamelCase      = 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   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        class     a_						:
          lowercase							=				"""bar"""


        UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      with patch("""datasets.utils.py_utils._single_map_nested"""   ) as mock_single_map_nested, patch(
          """datasets.parallel.parallel.Pool"""   ) as mock_multiprocessing_pool:
            UpperCamelCase      = {F"{i}": i for i in range(__UpperCamelCase   )}
            UpperCamelCase      = map_nested(lambda __UpperCamelCase   : x + 10		,						__UpperCamelCase		,						num_proc=__UpperCamelCase		,						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     a_						(      lowerCamelCase    ):

  @require_tf
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        import tensorflow as tf
        from tensorflow.keras import layers

        UpperCamelCase      = layers.Dense(2				)

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

        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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

  @require_torch
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        import torch

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

        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






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

        with temp_seed(42				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).flatten()
      assert output == expected_output







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = A(x=1		,						y="""foobar"""   )
      UpperCamelCase      = {"""x""": 1, """y""": """foobar"""}
      assert asdict(__UpperCamelCase   ) == expected_output

      UpperCamelCase      = {"""a""": {"""b""": A(x=10		,						y="""foo"""   )}, """c""": [A(x=20		,						y="""bar"""   )]}
      UpperCamelCase      = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
      assert asdict(__UpperCamelCase   ) == expected_output

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







def    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      return text.split()







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      yield (time.time(), content)
      time.sleep(2   )
      yield (time.time(), content)







def    lowercase__			(				)->			int:
      with Pool(2   ) as pool:
            UpperCamelCase      = list(iflatmap_unordered(__UpperCamelCase		,						_split_text		,						kwargs_iterable=[{"""text""": """hello there"""}] * 10   )   )
            assert out.count("""hello"""   ) == 10
            assert out.count("""there"""   ) == 10
            assert len(__UpperCamelCase   ) == 20

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

      # check that we get items as fast as possible
      with Pool(2   ) as pool:
            UpperCamelCase      = []
            for yield_time, content in iflatmap_unordered(
                __UpperCamelCase		,						_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(__UpperCamelCase   )
            assert out.count("""a"""   ) == 2
            assert out.count("""b"""   ) == 2
            assert len(__UpperCamelCase   ) == 4
