
"""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 							A		(       snake_case   :Dict	)   ->			Dict:  # picklable for multiprocessing
 return x.sum()






def 							A		(       snake_case   :str	)   ->			Optional[int]:  # picklable for multiprocessing
 return i + 1

@dataclass
class __lowerCAmelCase	:
     lowercase		     =		42
     lowercase		     =		42


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def 		UpperCAmelCase      (			self   ):

      '''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(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase   )			,  __UpperCAmelCase   )

      __UpperCamelCase       						= 2
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )
      self.assertEqual(map_nested(__UpperCAmelCase			,  __UpperCAmelCase			,  num_proc=__UpperCAmelCase   )			,  __UpperCAmelCase   )

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





     def 		UpperCAmelCase      (			self   ):

      '''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(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )   )			,  __UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      class __lowerCAmelCase	:
           lowercase		     =		"bar"


      __UpperCamelCase       						= Foo()
      self.assertEqual(foo.my_attr			,  'bar'   )
      with temporary_assignment(__UpperCAmelCase			,  '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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]       ,      )
def 							A		(       snake_case   :Tuple       ,      snake_case   :Tuple       ,      snake_case   :Union[str, Any]	)   ->			List[Any]:
 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(snake_case	)}
  __UpperCamelCase       						= map_nested(lambda snake_case	: x + 1_0       ,      snake_case       ,      num_proc=snake_case       ,      parallel_min_length=1_6	)
  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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     @require_tf
     def 		UpperCAmelCase      (			self   ):

      '''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(__UpperCAmelCase   ).numpy()

      with temp_seed(42			,  set_tensorflow=__UpperCAmelCase   ):
       __UpperCamelCase       						= gen_random_output()
      with temp_seed(42			,  set_tensorflow=__UpperCAmelCase   ):
       __UpperCamelCase       						= gen_random_output()
      __UpperCamelCase       						= gen_random_output()

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





     @require_torch
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      import torch

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

      with temp_seed(42			,  set_pytorch=__UpperCAmelCase   ):
       __UpperCamelCase       						= gen_random_output()
      with temp_seed(42			,  set_pytorch=__UpperCAmelCase   ):
       __UpperCamelCase       						= gen_random_output()
      __UpperCamelCase       						= gen_random_output()

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







     def 		UpperCAmelCase      (			self   ):

      '''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(__UpperCAmelCase			,  __UpperCAmelCase   )
      self.assertGreater(np.abs(outa - outa   ).sum()			,  0   )






@pytest.mark.parametrize('input_data'       ,      [{}]	)
def 							A		(       snake_case   :Optional[Any]	)   ->			int:
 __UpperCamelCase       						= NestedDataStructure(snake_case	).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 							A		(       snake_case   :str       ,      snake_case   :Optional[int]	)   ->			Tuple:
 __UpperCamelCase       						= NestedDataStructure(snake_case	).flatten()
 assert output == expected_output






def 							A		(       )   ->			Dict:
 __UpperCamelCase       						= A(x=1       ,      y='foobar'	)
 __UpperCamelCase       						= {'x': 1, 'y': 'foobar'}
 assert asdict(snake_case	) == expected_output

 __UpperCamelCase       						= {'a': {'b': A(x=1_0       ,      y='foo'	)}, 'c': [A(x=2_0       ,      y='bar'	)]}
 __UpperCamelCase       						= {'a': {'b': {'x': 1_0, 'y': 'foo'}}, 'c': [{'x': 2_0, 'y': 'bar'}]}
 assert asdict(snake_case	) == expected_output

 with pytest.raises(snake_case	):
  asdict([1, A(x=1_0       ,      y='foo'	)]	)






def 							A		(       snake_case   :str	)   ->			Union[str, Any]:
 return text.split()






def 							A		(       snake_case   :str	)   ->			Union[str, Any]:
 yield (time.time(), content)
 time.sleep(2	)
 yield (time.time(), content)






def 							A		(       )   ->			int:
 with Pool(2	) as pool:
  __UpperCamelCase       						= list(iflatmap_unordered(snake_case       ,      _split_text       ,      kwargs_iterable=[{'text': 'hello there'}] * 1_0	)	)
  assert out.count('hello'	) == 1_0
  assert out.count('there'	) == 1_0
  assert len(snake_case	) == 2_0

 # check multiprocess from pathos (uses dill for pickling)
 with multiprocess.Pool(2	) as pool:
  __UpperCamelCase       						= list(iflatmap_unordered(snake_case       ,      _split_text       ,      kwargs_iterable=[{'text': 'hello there'}] * 1_0	)	)
  assert out.count('hello'	) == 1_0
  assert out.count('there'	) == 1_0
  assert len(snake_case	) == 2_0

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





"""simple docstring"""

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class __lowerCAmelCase	(   unittest.TestCase    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1 / 255			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,  __UpperCAmelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,  __UpperCAmelCase = True			,  __UpperCAmelCase=7			,  __UpperCAmelCase=30			,  __UpperCAmelCase=400			,  __UpperCAmelCase=3			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= size if size is not None else {'shortest_edge': 288}
      __UpperCamelCase       						= size_divisor
      __UpperCamelCase       						= do_rescale
      __UpperCamelCase       						= rescale_factor
      __UpperCamelCase       						= do_normalize
      __UpperCamelCase       						= do_center_crop
      __UpperCamelCase       						= image_mean
      __UpperCamelCase       						= image_std
      __UpperCamelCase       						= do_pad
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= min_resolution
      __UpperCamelCase       						= max_resolution





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False   ):

      '''simple docstring'''




      if not batched:
       __UpperCamelCase       						= self.size['shortest_edge']
       __UpperCamelCase       						= image_inputs[0]
       if isinstance(__UpperCAmelCase			,  Image.Image   ):
        __UpperCamelCase						,			__UpperCamelCase       						= image.size
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= image.shape[1], image.shape[2]
       __UpperCamelCase       						= size / min(__UpperCAmelCase			,  __UpperCAmelCase   )
       if h < w:
        __UpperCamelCase						,			__UpperCamelCase       						= size, scale * w
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= scale * h, size

       __UpperCamelCase       						= int((1333 / 800) * size   )
       if max(__UpperCAmelCase			,  __UpperCAmelCase   ) > max_size:
        __UpperCamelCase       						= max_size / max(__UpperCAmelCase			,  __UpperCAmelCase   )
        __UpperCamelCase       						= newh * scale
        __UpperCamelCase       						= neww * scale

       __UpperCamelCase						,			__UpperCamelCase       						= int(newh + 0.5   ), int(neww + 0.5   )
       __UpperCamelCase						,			__UpperCamelCase       						= (
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __UpperCamelCase       						= []
       for image in image_inputs:
        __UpperCamelCase						,			__UpperCamelCase       						= self.get_expected_values([image]   )
        expected_values.append((expected_height, expected_width)   )
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[0]   )[0]
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[1]   )[1]

      return expected_height, expected_width


@require_torch
@require_vision
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		BridgeTowerImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BridgeTowerImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_mean'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_std'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_normalize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_resize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size_divisor'   )   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  Image.Image   )

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      __UpperCamelCase       						= prepare_image_inputs(self.image_processor_tester			,  equal_resolution=__UpperCAmelCase			,  numpify=__UpperCAmelCase   )
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase			,  np.ndarray   )

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )



