





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  (      lowerCamelCase   ):  # picklable for multiprocessing
   return x.sum()






def       _A  (      lowerCamelCase   ):  # picklable for multiprocessing
   return i + 1







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						int
   _UpperCamelCase :						str







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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

      a__	:						Dict      		=  2
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )

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





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						str      		=  {"a": 1, "b": 2}
      a__	:						Optional[Any]      		=  {"a": 3, "b": 4}
      a__	:						Dict      		=  {"a": 5, "b": 6}
      a__	:						int      		=  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]      )
      self.assertEqual(sorted(zip_dict(snake_case					,			snake_case					,			snake_case      )      )					,			snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      class   __lowerCAmelCase			:
         _UpperCamelCase :						Union[str, Any]     		=				"""bar"""







      a__	:						int      		=  Foo()
      self.assertEqual(foo.my_attr					,			"bar"      )
      with temporary_assignment(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  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   with patch("datasets.utils.py_utils._single_map_nested"   ) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"   ) as mock_multiprocessing_pool:
      a__	:						Union[str, Any]      		=  {F"""{i}""": i for i in range(lowerCamelCase   )}
      a__	:						Any      		=  map_nested(lambda lowerCamelCase   : x + 10							, lowerCamelCase							, num_proc=lowerCamelCase							, 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			(					_UpperCamelCase  ):





   @require_tf
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      import tensorflow as tf
      from tensorflow.keras import layers

      a__	:						Any      		=  layers.Dense(2      )

      def gen_random_output():
         a__	:						List[Any]      		=  tf.random.uniform((1, 3)      )
         return model(snake_case      ).numpy()

      with temp_seed(42					,			set_tensorflow=snake_case      ):
         a__	:						List[Any]      		=  gen_random_output()
      with temp_seed(42					,			set_tensorflow=snake_case      ):
         a__	:						List[Any]      		=  gen_random_output()
      a__	:						Optional[Any]      		=  gen_random_output()

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





   @require_torch
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      import torch

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

      with temp_seed(42					,			set_pytorch=snake_case      ):
         a__	:						Tuple      		=  gen_random_output()
      with temp_seed(42					,			set_pytorch=snake_case      ):
         a__	:						Dict      		=  gen_random_output()
      a__	:						Optional[Any]      		=  gen_random_output()

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





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





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

      with temp_seed(42      ):
         a__	:						Union[str, Any]      		=  gen_random_output()
      with temp_seed(42      ):
         a__	:						Optional[int]      		=  gen_random_output()
      a__	:						List[Any]      		=  gen_random_output()

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







@pytest.mark.parametrize("input_data"							, [{}]   )
def       _A  (      lowerCamelCase   ):
   a__	:						int      		=  NestedDataStructure(lowerCamelCase   ).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  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  NestedDataStructure(lowerCamelCase   ).flatten()
   assert output == expected_output






def       _A  (      ):
   a__	:						List[str]      		=  A(x=1							, y="foobar"   )
   a__	:						Dict      		=  {"x": 1, "y": "foobar"}
   assert asdict(lowerCamelCase   ) == expected_output

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

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






def       _A  (      lowerCamelCase   ):
   return text.split()






def       _A  (      lowerCamelCase   ):
   yield (time.time(), content)
   time.sleep(2   )
   yield (time.time(), content)






def       _A  (      ):
   with Pool(2   ) as pool:
      a__	:						str      		=  list(iflatmap_unordered(lowerCamelCase							, _split_text							, kwargs_iterable=[{"text": "hello there"}] * 10   )   )
      assert out.count("hello"   ) == 10
      assert out.count("there"   ) == 10
      assert len(lowerCamelCase   ) == 20

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

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








# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

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

