





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_  )     ->		List[Any]:  # picklable for multiprocessing
       return x.sum()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:  # picklable for multiprocessing
       return i + 1



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					int
       _UpperCamelCase				:					str



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def lowercase (		self: List[str]		)       -> Tuple:




              """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(_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		)

              UpperCamelCase_          =  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		)

              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(_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 lowercase (		self: Tuple		)       -> str:




              """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(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)					,   _SCREAMING_SNAKE_CASE		)



       def lowercase (		self: Optional[Any]		)       -> List[Any]:




              """simple docstring"""


              class 							_UpperCamelCase					:
                     _UpperCamelCase				:					str         =   '''bar'''

              UpperCamelCase_          =  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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, 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(UpperCamelCase_  )}
              UpperCamelCase_          =  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 							_UpperCamelCase					(	lowerCAmelCase_     ):

       @require_tf
       def lowercase (		self: Optional[Any]		)       -> Optional[int]:




              """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(_SCREAMING_SNAKE_CASE		).numpy()

              with temp_seed(42					,   set_tensorflow=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              with temp_seed(42					,   set_tensorflow=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              UpperCamelCase_          =  gen_random_output()

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

       @require_torch
       def lowercase (		self: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              import torch

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

              with temp_seed(42					,   set_pytorch=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              with temp_seed(42					,   set_pytorch=_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  gen_random_output()
              UpperCamelCase_          =  gen_random_output()

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



       def lowercase (		self: Union[str, Any]		)       -> str:




              """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(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              self.assertGreater(np.abs(outa - outa		).sum()					,   0		)




@pytest.mark.parametrize("input_data"					,		[{}]  )
def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  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_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  NestedDataStructure(UpperCamelCase_  ).flatten()
       assert output == expected_output




def  lowerCAmelCase_ (							)     ->		Tuple:
       UpperCamelCase_          =  A(x=1					,		y="foobar"  )
       UpperCamelCase_          =  {"x": 1, "y": "foobar"}
       assert asdict(UpperCamelCase_  ) == expected_output

       UpperCamelCase_          =  {"a": {"b": A(x=10					,		y="foo"  )}, "c": [A(x=20					,		y="bar"  )]}
       UpperCamelCase_          =  {"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:
       return text.split()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[str]:
       yield (time.time(), content)
       time.sleep(2  )
       yield (time.time(), content)







def  lowerCAmelCase_ (							)     ->		int:
       with Pool(2  ) as pool:
              UpperCamelCase_          =  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:
              UpperCamelCase_          =  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:
              UpperCamelCase_          =  []
              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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   '''openai-gpt'''
       _UpperCamelCase				:					List[str]         =   {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }

       def __init__(		self: int					,   _SCREAMING_SNAKE_CASE: int=40478					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: List[Any]=768					,   _SCREAMING_SNAKE_CASE: str=12					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Optional[int]="gelu"					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: Optional[int]=0.1					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.1					,   _SCREAMING_SNAKE_CASE: Any=1e-5					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: str="cls_index"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> int:




              """simple docstring"""


              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  n_positions
              UpperCamelCase_          =  n_embd
              UpperCamelCase_          =  n_layer
              UpperCamelCase_          =  n_head
              UpperCamelCase_          =  afn
              UpperCamelCase_          =  resid_pdrop
              UpperCamelCase_          =  embd_pdrop
              UpperCamelCase_          =  attn_pdrop
              UpperCamelCase_          =  layer_norm_epsilon
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  summary_type
              UpperCamelCase_          =  summary_use_proj
              UpperCamelCase_          =  summary_activation
              UpperCamelCase_          =  summary_first_dropout
              UpperCamelCase_          =  summary_proj_to_labels
              super().__init__(**_SCREAMING_SNAKE_CASE		)

