




"""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   UpperCAmelCase_    (			unittest.TestCase		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = 32				,						UpperCamelCase_ = True				,						UpperCamelCase_ = 1 / 2_55				,						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_=4_00				,						UpperCamelCase_=3				,						)						->       Union[str, Any]:
					__lowercase			:  Optional[int]								=       parent
					__lowercase			:  Any								=       do_resize
					__lowercase			:  Any								=       size if size is not None else {'''shortest_edge''': 2_88}
					__lowercase			:  Any								=       size_divisor
					__lowercase			:  Optional[int]								=       do_rescale
					__lowercase			:  Dict								=       rescale_factor
					__lowercase			:  Any								=       do_normalize
					__lowercase			:  Union[str, Any]								=       do_center_crop
					__lowercase			:  Any								=       image_mean
					__lowercase			:  int								=       image_std
					__lowercase			:  Dict								=       do_pad
					__lowercase			:  Dict								=       batch_size
					__lowercase			:  str								=       num_channels
					__lowercase			:  List[Any]								=       min_resolution
					__lowercase			:  int								=       max_resolution
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_=False       )						->       Any:
					if not batched:
								__lowercase			:  Optional[Any]								=       self.size['''shortest_edge''']
								__lowercase			:  Optional[Any]								=       image_inputs[0]
								if isinstance(UpperCamelCase_				,						Image.Image       ):
											__lowercase			,__lowercase			:  Optional[Any]								=       image.size
								else:
											__lowercase			,__lowercase			:  List[str]								=       image.shape[1], image.shape[2]
								__lowercase			:  List[str]								=       size / min(UpperCamelCase_				,						UpperCamelCase_       )
								if h < w:
											__lowercase			,__lowercase			:  Optional[Any]								=       size, scale * w
								else:
											__lowercase			,__lowercase			:  Dict								=       scale * h, size

								__lowercase			:  Dict								=       int((13_33 / 8_00) * size       )
								if max(UpperCamelCase_				,						UpperCamelCase_       ) > max_size:
											__lowercase			:  List[Any]								=       max_size / max(UpperCamelCase_				,						UpperCamelCase_       )
											__lowercase			:  List[str]								=       newh * scale
											__lowercase			:  int								=       neww * scale

								__lowercase			,__lowercase			:  Tuple								=       int(newh + 0.5       ), int(neww + 0.5       )
								__lowercase			,__lowercase			:  int								=       (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								__lowercase			:  List[str]								=       []
								for image in image_inputs:
											__lowercase			,__lowercase			:  Tuple								=       self.get_expected_values([image]       )
											expected_values.append((expected_height, expected_width)       )
								__lowercase			:  int								=       max(UpperCamelCase_				,						key=lambda UpperCamelCase_       : item[0]       )[0]
								__lowercase			:  Union[str, Any]								=       max(UpperCamelCase_				,						key=lambda UpperCamelCase_       : item[1]       )[1]

					return expected_height, expected_width


@require_torch
@require_vision
class   UpperCAmelCase_    (			snake_case   ,					unittest.TestCase		):
		UpperCamelCase							=BridgeTowerImageProcessor if is_vision_available() else None
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			:  List[str]								=       BridgeTowerImageProcessingTester(self       )
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					__lowercase			:  Optional[Any]								=       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 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					pass
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					# Initialize image processor
					__lowercase			:  Union[str, Any]								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					__lowercase			:  Dict								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCamelCase_       )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_				,						Image.Image       )

					# Test not batched input
					__lowercase			:  List[Any]								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Union[str, Any]								=       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
					__lowercase			:  List[Any]								=       image_processing(UpperCamelCase_				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Union[str, Any]								=       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 	_lowerCamelCase							(						self       )						->       int:
					# Initialize image processor
					__lowercase			:  List[str]								=       self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					__lowercase			:  Union[str, Any]								=       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
					__lowercase			:  Dict								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  str								=       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
					__lowercase			:  Optional[Any]								=       image_processing(UpperCamelCase_				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Tuple								=       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 	_lowerCamelCase							(						self       )						->       Tuple:
					# Initialize image processor
					__lowercase			:  str								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					__lowercase			:  Dict								=       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
					__lowercase			:  Any								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Tuple								=       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
					__lowercase			:  Optional[Any]								=       image_processing(UpperCamelCase_				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Union[str, Any]								=       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,
					    )				,						)







"""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_   (					UpperCamelCase__						:			int			):  # picklable for multiprocessing






 """simple docstring"""







 return x.sum()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]			):  # picklable for multiprocessing






 """simple docstring"""







 return i + 1





@dataclass
class  lowerCamelCase__    :
      a			:  int
      a			:  str






class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


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

       __lowercase						=						2
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)

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






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       class  lowerCamelCase__    :
             a			:  int           =		"""bar"""






       __lowercase						=						Foo()
       self.assertEqual(foo.my_attr   ,							"""bar"""				)
       with temporary_assignment(A_   ,							"""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_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 with patch("""datasets.utils.py_utils._single_map_nested"""			) as mock_single_map_nested, patch(
     """datasets.parallel.parallel.Pool"""			) as mock_multiprocessing_pool:
  __lowercase						=						{f'''{i}''': i for i in range(UpperCamelCase__			)}
  __lowercase						=						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  lowerCamelCase__    (		_a     ):
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       import tensorflow as tf
       from tensorflow.keras import layers

       __lowercase						=						layers.Dense(2				)

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

       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       import torch

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

       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


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

       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






@pytest.mark.parametrize("""input_data"""					, [{}]			)
def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						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 lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).flatten()
 assert output == expected_output


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						A(x=1					, y="""foobar"""			)
 __lowercase						=						{"""x""": 1, """y""": """foobar"""}
 assert asdict(UpperCamelCase__			) == expected_output

 __lowercase						=						{"""a""": {"""b""": A(x=10					, y="""foo"""			)}, """c""": [A(x=20					, y="""bar"""			)]}
 __lowercase						=						{"""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 lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 return text.split()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict			):






 """simple docstring"""







 yield (time.time(), content)
 time.sleep(2			)
 yield (time.time(), content)


def lowerCAmelCase_   (					):






 """simple docstring"""







 with Pool(2			) as pool:
  __lowercase						=						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:
  __lowercase						=						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:
  __lowercase						=						[]
  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
