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		(      _A       ):  # picklable for multiprocessing



 """simple docstring"""


 return x.sum()



def 	__A		(      _A       ):  # picklable for multiprocessing



 """simple docstring"""


 return i + 1
@dataclass
class  A_			:
  _SCREAMING_SNAKE_CASE     					=      42
  _SCREAMING_SNAKE_CASE     					=      42
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	{}
   __a							=	[]
   __a							=	1
   __a							=	[1, 2]
   __a							=	{"a": 1, "b": 2}
   __a							=	{"a": [1, 2], "b": [3, 4]}
   __a							=	{"a": {"1": 1}, "b": 2}
   __a							=	{"a": 1, "b": 2, "c": 3, "d": 4}
   __a							=	{}
   __a							=	[]
   __a							=	2
   __a							=	[2, 3]
   __a							=	{"a": 2, "b": 3}
   __a							=	{"a": [2, 3], "b": [4, 5]}
   __a							=	{"a": {"1": 2}, "b": 3}
   __a							=	{"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							)

   __a							=	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							)

   __a							=	{"a": np.eye(2							), "b": np.zeros(3							), "c": np.ones(2							)}
   __a							=	{"a": 2, "b": 0, "c": 2}
   __a							=	{
       "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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	{"a": 1, "b": 2}
   __a							=	{"a": 3, "b": 4}
   __a							=	{"a": 5, "b": 6}
   __a							=	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 		_UpperCAmelCase (		self			:   List[str]							):
   class  A_			:
     _SCREAMING_SNAKE_CASE     					=      """bar"""
   __a							=	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 	__A		(      _A				,					_A				,					_A       ):



 """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:
  __a							=	{f"""{i}""": i for i in range(_A       )}
  __a							=	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  A_			(							a_			):

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

   __a							=	layers.Dense(2							)

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

   with temp_seed(42  ,				set_tensorflow=__SCREAMING_SNAKE_CASE							):
    __a							=	gen_random_output()
   with temp_seed(42  ,				set_tensorflow=__SCREAMING_SNAKE_CASE							):
    __a							=	gen_random_output()
   __a							=	gen_random_output()

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

  @require_torch
  def 		_UpperCAmelCase (		self			:   str							):
   import torch

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

   with temp_seed(42  ,				set_pytorch=__SCREAMING_SNAKE_CASE							):
    __a							=	gen_random_output()
   with temp_seed(42  ,				set_pytorch=__SCREAMING_SNAKE_CASE							):
    __a							=	gen_random_output()
   __a							=	gen_random_output()

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

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

   with temp_seed(42							):
    __a							=	gen_random_output()
   with temp_seed(42							):
    __a							=	gen_random_output()
   __a							=	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 	__A		(      _A       ):



 """simple docstring"""


 __a							=	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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	NestedDataStructure(_A       ).flatten()
 assert output == expected_output



def 	__A		(      ):



 """simple docstring"""


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

 __a							=	{"a": {"b": A(x=10				,					y="foo"       )}, "c": [A(x=20				,					y="bar"       )]}
 __a							=	{"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 	__A		(      _A       ):



 """simple docstring"""


 return text.split()



def 	__A		(      _A       ):



 """simple docstring"""


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






def 	__A		(      ):



 """simple docstring"""


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



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


