





"""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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Tuple:  # picklable for multiprocessing






	'''simple docstring'''






	return x.sum()






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->List[str]:  # picklable for multiprocessing






	'''simple docstring'''






	return i + 1


@dataclass
class 			__UpperCamelCase							:
							lowerCamelCase  :   Union[str, Any]    =42
							lowerCamelCase  :   List[Any]    =42


class 			__UpperCamelCase							(		a__		):






							def        __a  (  self )      ->					Dict:
								a :						List[str]      =							{}
								a :						Tuple      =							[]
								a :						Tuple      =							1
								a :						Union[str, Any]      =							[1, 2]
								a :						str      =							{'a': 1, 'b': 2}
								a :						Tuple      =							{'a': [1, 2], 'b': [3, 4]}
								a :						Optional[int]      =							{'a': {'1': 1}, 'b': 2}
								a :						Dict      =							{'a': 1, 'b': 2, 'c': 3, 'd': 4}
								a :						Any      =							{}
								a :						Tuple      =							[]
								a :						List[str]      =							2
								a :						List[str]      =							[2, 3]
								a :						Tuple      =							{'a': 2, 'b': 3}
								a :						Union[str, Any]      =							{'a': [2, 3], 'b': [4, 5]}
								a :						List[str]      =							{'a': {'1': 2}, 'b': 3}
								a :						Optional[int]      =							{'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__ )

								a :						Union[str, Any]      =							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__ )

								a :						int      =							{'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
								a :						Optional[int]      =							{'a': 2, 'b': 0, 'c': 2}
								a :						List[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 )      ->					List[Any]:
								a :						List[str]      =							{'a': 1, 'b': 2}
								a :						List[str]      =							{'a': 3, 'b': 4}
								a :						int      =							{'a': 5, 'b': 6}
								a :						Union[str, Any]      =							sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
								self.assertEqual(sorted(zip_dict(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ) ) ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:


								class 			__UpperCamelCase							:
															lowerCamelCase  :   str    ="""bar"""
								a :						Optional[Any]      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Dict       ,			_lowercase		:       Any   )					->Union[str, Any]:






	'''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 :						int      =							{F"""{i}""": i for i in range(lowercase__   )}
		a :						List[str]      =							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 			__UpperCamelCase							(		a__		):






							@require_tf
							def        __a  (  self )      ->					Optional[Any]:
								import tensorflow as tf
								from tensorflow.keras import layers

								a :						List[Any]      =							layers.Dense(2 )

								def gen_random_output():
									a :						str      =							tf.random.uniform((1, 3) )
									return model(lowerCAmelCase__ ).numpy()

								with temp_seed(42 ,   set_tensorflow=lowerCAmelCase__ ):
									a :						List[str]      =							gen_random_output()
								with temp_seed(42 ,   set_tensorflow=lowerCAmelCase__ ):
									a :						Optional[int]      =							gen_random_output()
								a :						str      =							gen_random_output()

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






							@require_torch
							def        __a  (  self )      ->					Dict:
								import torch

								def gen_random_output():
									a :						Dict      =							torch.nn.Linear(3 ,   2 )
									a :						Tuple      =							torch.rand(1 ,   3 )
									return model(lowerCAmelCase__ ).detach().numpy()

								with temp_seed(42 ,   set_pytorch=lowerCAmelCase__ ):
									a :						Optional[int]      =							gen_random_output()
								with temp_seed(42 ,   set_pytorch=lowerCAmelCase__ ):
									a :						List[str]      =							gen_random_output()
								a :						List[Any]      =							gen_random_output()

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






							def        __a  (  self )      ->					Optional[Any]:
								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 :						Tuple      =							gen_random_output()
								a :						Any      =							gen_random_output()

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






@pytest.mark.parametrize("input_data"       ,			[{}]   )
def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]   )					->Any:






	'''simple docstring'''






	a :						List[Any]      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any       ,			_lowercase		:       Optional[Any]   )					->int:






	'''simple docstring'''






	a :						Union[str, Any]      =							NestedDataStructure(lowercase__   ).flatten()
	assert output == expected_output






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						List[str]      =							A(x=1       ,			y="foobar"   )
	a :						List[str]      =							{'x': 1, 'y': 'foobar'}
	assert asdict(lowercase__   ) == expected_output

	a :						Optional[Any]      =							{'a': {'b': A(x=10       ,			y="foo"   )}, 'c': [A(x=20       ,			y="bar"   )]}
	a :						List[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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]   )					->Optional[int]:






	'''simple docstring'''






	return text.split()






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int   )					->List[str]:






	'''simple docstring'''






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







def  _SCREAMING_SNAKE_CASE						(      )					->List[str]:






	'''simple docstring'''






	with Pool(2   ) as pool:
		a :						str      =							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:
		a :						Union[str, 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:
		a :						int      =							[]
		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








"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1				,       len(lowercase__						)						):
								_lowerCamelCase	:     List[str]       =       observations_space[o]
								_lowerCamelCase	:     str       =       observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_lowerCamelCase	:     Dict       =       ''
												_lowerCamelCase	:     int       =       -1
												for k_state in states_space:
																_lowerCamelCase	:     Any       =       (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_lowerCamelCase	:     Dict       =       probability
																				_lowerCamelCase	:     int       =       k_state

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

				# argmax for given final observation
				_lowerCamelCase	:     Optional[int]       =       ''
				_lowerCamelCase	:     str       =       -1
				for k_state in states_space:
								_lowerCamelCase	:     Optional[int]       =       probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_lowerCamelCase	:     int       =       probability
												_lowerCamelCase	:     Optional[int]       =       k_state
				_lowerCamelCase	:     Any       =       arg_max

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]						):
								raise ValueError('There\'s an empty parameter'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


if __name__ == "__main__":
			from doctest import testmod

			testmod()