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 		snake_case__	(		lowercase					):  # picklable for multiprocessing
   return x.sum()
def 		snake_case__	(		lowercase					):  # picklable for multiprocessing
   return i + 1

@dataclass
class 					_lowercase    :






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				int
    SCREAMING_SNAKE_CASE:				str





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	{}
       lowerCAmelCase_:							int       =	[]
       lowerCAmelCase_:							Tuple       =	1
       lowerCAmelCase_:							Optional[Any]       =	[1, 2]
       lowerCAmelCase_:							int       =	{"a": 1, "b": 2}
       lowerCAmelCase_:							Union[str, Any]       =	{"a": [1, 2], "b": [3, 4]}
       lowerCAmelCase_:							Optional[int]       =	{"a": {"1": 1}, "b": 2}
       lowerCAmelCase_:							str       =	{"a": 1, "b": 2, "c": 3, "d": 4}
       lowerCAmelCase_:							str       =	{}
       lowerCAmelCase_:							Any       =	[]
       lowerCAmelCase_:							Optional[int]       =	2
       lowerCAmelCase_:							str       =	[2, 3]
       lowerCAmelCase_:							Any       =	{"a": 2, "b": 3}
       lowerCAmelCase_:							Tuple       =	{"a": [2, 3], "b": [4, 5]}
       lowerCAmelCase_:							Optional[int]       =	{"a": {"1": 2}, "b": 3}
       lowerCAmelCase_:							List[str]       =	{"a": 2, "b": 3, "c": 4, "d": 5}
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__ )       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	2
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	{"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
       lowerCAmelCase_:							Optional[int]       =	{"a": 2, "b": 0, "c": 2}
       lowerCAmelCase_:							Union[str, Any]       =	{
           "a": np.eye(2 ).astype(lowerCamelCase__ ),
           "b": np.zeros(3 ).astype(lowerCamelCase__ ),
           "c": np.ones(2 ).astype(lowerCamelCase__ ),
       }
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						map_numpy=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						map_numpy=lowerCamelCase__ ).items()}       ,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,						)
       self.assertEqual(map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						map_numpy=lowerCamelCase__       ,						num_proc=lowerCamelCase__ )       ,						lowerCamelCase__ )
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(lowerCamelCase__       ,						lowerCamelCase__       ,						map_numpy=lowerCamelCase__       ,						num_proc=lowerCamelCase__ ).items()}       ,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,						)
       with self.assertRaises(lowerCamelCase__ ):  # can't pickle a local lambda
          map_nested(lambda lowerCamelCase__ : x + 1       ,						lowerCamelCase__       ,						num_proc=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	{"a": 1, "b": 2}
       lowerCAmelCase_:							Union[str, Any]       =	{"a": 3, "b": 4}
       lowerCAmelCase_:							Optional[Any]       =	{"a": 5, "b": 6}
       lowerCAmelCase_:							Tuple       =	sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
       self.assertEqual(sorted(zip_dict(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) )       ,						lowerCamelCase__ )




    def      _a    (    self ):

       class 					_lowercase    :






           '''simple docstring'''
           SCREAMING_SNAKE_CASE:				Tuple          =    'bar'





       lowerCAmelCase_:							str       =	Foo()
       self.assertEqual(foo.my_attr       ,						"bar" )
       with temporary_assignment(lowerCamelCase__       ,						"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 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   with patch("datasets.utils.py_utils._single_map_nested"					) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"					) as mock_multiprocessing_pool:
      lowerCAmelCase_:							Dict       =	{F'''{i}''': i for i in range(lowercase					)}
      lowerCAmelCase_:							Optional[int]       =	map_nested(lambda lowercase					: x + 10							,				lowercase							,				num_proc=lowercase							,				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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @require_tf
    def      _a    (    self ):
       import tensorflow as tf
       from tensorflow.keras import layers

       lowerCAmelCase_:							Union[str, Any]       =	layers.Dense(2 )

       def gen_random_output():
          lowerCAmelCase_:							Union[str, Any]       =	tf.random.uniform((1, 3) )
          return model(lowerCamelCase__ ).numpy()

       with temp_seed(42       ,						set_tensorflow=lowerCamelCase__ ):
          lowerCAmelCase_:							List[Any]       =	gen_random_output()
       with temp_seed(42       ,						set_tensorflow=lowerCamelCase__ ):
          lowerCAmelCase_:							List[Any]       =	gen_random_output()
       lowerCAmelCase_:							Dict       =	gen_random_output()

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




    @require_torch
    def      _a    (    self ):
       import torch

       def gen_random_output():
          lowerCAmelCase_:							Optional[Any]       =	torch.nn.Linear(3       ,						2 )
          lowerCAmelCase_:							Optional[int]       =	torch.rand(1       ,						3 )
          return model(lowerCamelCase__ ).detach().numpy()

       with temp_seed(42       ,						set_pytorch=lowerCamelCase__ ):
          lowerCAmelCase_:							List[str]       =	gen_random_output()
       with temp_seed(42       ,						set_pytorch=lowerCamelCase__ ):
          lowerCAmelCase_:							Dict       =	gen_random_output()
       lowerCAmelCase_:							List[str]       =	gen_random_output()

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




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

       with temp_seed(42 ):
          lowerCAmelCase_:							List[str]       =	gen_random_output()
       with temp_seed(42 ):
          lowerCAmelCase_:							List[str]       =	gen_random_output()
       lowerCAmelCase_:							List[str]       =	gen_random_output()

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





@pytest.mark.parametrize("input_data"							,				[{}]					)
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Tuple       =	NestedDataStructure(lowercase					).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 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[Any]       =	NestedDataStructure(lowercase					).flatten()
   assert output == expected_output
def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[Any]       =	A(x=1							,				y="foobar"					)
   lowerCAmelCase_:							Optional[int]       =	{"x": 1, "y": "foobar"}
   assert asdict(lowercase					) == expected_output

   lowerCAmelCase_:							Optional[Any]       =	{"a": {"b": A(x=10							,				y="foo"					)}, "c": [A(x=20							,				y="bar"					)]}
   lowerCAmelCase_:							Union[str, Any]       =	{"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
   assert asdict(lowercase					) == expected_output

   with pytest.raises(lowercase					):
      asdict([1, A(x=10							,				y="foo"					)]					)
def 		snake_case__	(		lowercase					):
   return text.split()
def 		snake_case__	(		lowercase					):
   yield (time.time(), content)
   time.sleep(2					)
   yield (time.time(), content)







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

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

   # check that we get items as fast as possible
   with Pool(2					) as pool:
      lowerCAmelCase_:							Dict       =	[]
      for yield_time, content in iflatmap_unordered(
          lowercase							,				_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(lowercase					)
      assert out.count("a"					) == 2
      assert out.count("b"					) == 2
      assert len(lowercase					) == 4
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a  :      int            =    {
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Any            =    ["""BridgeTowerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    [
     """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BridgeTowerForContrastiveLearning""",
     """BridgeTowerForImageAndTextRetrieval""",
     """BridgeTowerForMaskedLM""",
     """BridgeTowerModel""",
     """BridgeTowerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 a  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)