
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 )		->				Any:  # picklable for multiprocessing

		"""simple docstring"""






		return x.sum()


def 							__A(lowerCAmelCase )		->				Optional[Any]:  # picklable for multiprocessing

		"""simple docstring"""






		return i + 1







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: int
						UpperCamelCase_							: str







class        lowerCAmelCase__      (   __lowercase	):

						def  A_				(			self				)					->      str:


								'''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(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a				)       ,							a				)

								_UpperCamelCase						    =	2
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)
								self.assertEqual(map_nested(a       ,							a       ,							num_proc=a				)       ,							a				)

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

						def  A_				(			self				)					->      Dict:


								'''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(a       ,							a       ,							a				)				)       ,							a				)







						def  A_				(			self				)					->      int:


								'''simple docstring'''

								class        lowerCAmelCase__      :
														UpperCamelCase_							: int	=		"bar"




								_UpperCamelCase						    =	Foo()
								self.assertEqual(foo.my_attr       ,							"""bar"""				)
								with temporary_assignment(a       ,							"""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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]						,  )
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				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:
				_UpperCamelCase						    =	{F'{i}': i for i in range(lowerCAmelCase )}
				_UpperCamelCase						    =	map_nested(lambda lowerCAmelCase : x + 1_0						,  lowerCAmelCase						,  num_proc=lowerCAmelCase						,  parallel_min_length=1_6 )
				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__      (   __lowercase	):

						@require_tf
						def  A_				(			self				)					->      List[Any]:


								'''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(a				).numpy()

								with temp_seed(42       ,							set_tensorflow=a				):
										_UpperCamelCase						    =	gen_random_output()
								with temp_seed(42       ,							set_tensorflow=a				):
										_UpperCamelCase						    =	gen_random_output()
								_UpperCamelCase						    =	gen_random_output()

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

						@require_torch
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								import torch

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

								with temp_seed(42       ,							set_pytorch=a				):
										_UpperCamelCase						    =	gen_random_output()
								with temp_seed(42       ,							set_pytorch=a				):
										_UpperCamelCase						    =	gen_random_output()
								_UpperCamelCase						    =	gen_random_output()

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







						def  A_				(			self				)					->      int:


								'''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(a       ,							a				)
								self.assertGreater(np.abs(outa - outa				).sum()       ,							0				)




@pytest.mark.parametrize("""input_data"""						,  [{}] )
def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	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 )		->				Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	NestedDataStructure(lowerCAmelCase ).flatten()
		assert output == expected_output


def 							__A()		->				List[str]:

		"""simple docstring"""






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

		_UpperCamelCase						    =	{"""a""": {"""b""": A(x=1_0						,  y="""foo""" )}, """c""": [A(x=2_0						,  y="""bar""" )]}
		_UpperCamelCase						    =	{"""a""": {"""b""": {"""x""": 1_0, """y""": """foo"""}}, """c""": [{"""x""": 2_0, """y""": """bar"""}]}
		assert asdict(lowerCAmelCase ) == expected_output

		with pytest.raises(lowerCAmelCase ):
				asdict([1, A(x=1_0						,  y="""foo""" )] )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return text.split()


def 							__A(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






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







def 							__A()		->				List[str]:

		"""simple docstring"""






		with Pool(2 ) as pool:
				_UpperCamelCase						    =	list(iflatmap_unordered(lowerCAmelCase						,  _split_text						,  kwargs_iterable=[{"""text""": """hello there"""}] * 1_0 ) )
				assert out.count("""hello""" ) == 1_0
				assert out.count("""there""" ) == 1_0
				assert len(lowerCAmelCase ) == 2_0

		# check multiprocess from pathos (uses dill for pickling)
		with multiprocess.Pool(2 ) as pool:
				_UpperCamelCase						    =	list(iflatmap_unordered(lowerCAmelCase						,  _split_text						,  kwargs_iterable=[{"""text""": """hello there"""}] * 1_0 ) )
				assert out.count("""hello""" ) == 1_0
				assert out.count("""there""" ) == 1_0
				assert len(lowerCAmelCase ) == 2_0

		# check that we get items as fast as possible
		with Pool(2 ) as pool:
				_UpperCamelCase						    =	[]
				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





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



