





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


__lowerCamelCase :  List[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  int										=  {
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'glpn'





      def __init__(				self ,							A_=3 ,							A_=4 ,							A_=[2, 2, 2, 2] ,							A_=[8, 4, 2, 1] ,							A_=[32, 64, 160, 256] ,							A_=[7, 3, 3, 3] ,							A_=[4, 2, 2, 2] ,							A_=[1, 2, 5, 8] ,							A_=[4, 4, 4, 4] ,							A_="gelu" ,							A_=0.0 ,							A_=0.0 ,							A_=0.02 ,							A_=0.1 ,							A_=1e-6 ,							A_=64 ,							A_=10 ,							A_=-1 ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )

       UpperCamelCase			:      Tuple 			=     num_channels
       UpperCamelCase			:      Tuple 			=     num_encoder_blocks
       UpperCamelCase			:      Any 			=     depths
       UpperCamelCase			:      Union[str, Any] 			=     sr_ratios
       UpperCamelCase			:      Optional[Any] 			=     hidden_sizes
       UpperCamelCase			:      List[str] 			=     patch_sizes
       UpperCamelCase			:      str 			=     strides
       UpperCamelCase			:      Tuple 			=     mlp_ratios
       UpperCamelCase			:      str 			=     num_attention_heads
       UpperCamelCase			:      Tuple 			=     hidden_act
       UpperCamelCase			:      List[str] 			=     hidden_dropout_prob
       UpperCamelCase			:      List[str] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      str 			=     initializer_range
       UpperCamelCase			:      List[Any] 			=     drop_path_rate
       UpperCamelCase			:      str 			=     layer_norm_eps
       UpperCamelCase			:      Tuple 			=     decoder_hidden_size
       UpperCamelCase			:      Tuple 			=     max_depth
       UpperCamelCase			:      List[str] 			=     head_in_index







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  )		->				Union[str, Any]:  # picklable for multiprocessing
 return x.sum()



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:  # picklable for multiprocessing
 return i + 1



@dataclass
class       A__     :
      _UpperCAmelCase			:int
      _UpperCAmelCase			:str



class       A__     ( __snake_case    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     {}
       UpperCamelCase			:      Optional[Any] 			=     []
       UpperCamelCase			:      List[Any] 			=     1
       UpperCamelCase			:      Tuple 			=     [1, 2]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      Optional[Any] 			=     {"a": [1, 2], "b": [3, 4]}
       UpperCamelCase			:      Any 			=     {"a": {"1": 1}, "b": 2}
       UpperCamelCase			:      List[str] 			=     {"a": 1, "b": 2, "c": 3, "d": 4}
       UpperCamelCase			:      Dict 			=     {}
       UpperCamelCase			:      Any 			=     []
       UpperCamelCase			:      Any 			=     2
       UpperCamelCase			:      Any 			=     [2, 3]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 2, "b": 3}
       UpperCamelCase			:      List[Any] 			=     {"a": [2, 3], "b": [4, 5]}
       UpperCamelCase			:      Tuple 			=     {"a": {"1": 2}, "b": 3}
       UpperCamelCase			:      Dict 			=     {"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			:      List[str] 			=     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			:      List[str] 			=     {"a": np.eye(2     ), "b": np.zeros(3     ), "c": np.ones(2     )}
       UpperCamelCase			:      int 			=     {"a": 2, "b": 0, "c": 2}
       UpperCamelCase			:      Union[str, Any] 			=     {
           "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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      List[Any] 			=     {"a": 3, "b": 4}
       UpperCamelCase			:      Tuple 			=     {"a": 5, "b": 6}
       UpperCamelCase			:      Union[str, Any] 			=     sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]     )
       self.assertEqual(sorted(zip_dict(A_ ,							A_ ,							A_     )     ) ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       class       A__     :
             _UpperCAmelCase			:str							=     'bar'


       UpperCamelCase			:      List[Any] 			=     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),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]							,     )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[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			:      Union[str, Any] 			=     {F"""{i}""": i for i in range(_lowerCAmelCase  )}
  UpperCamelCase			:      List[str] 			=     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       A__     ( __snake_case    ):





      @require_tf
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       import tensorflow as tf
       from tensorflow.keras import layers

       UpperCamelCase			:      int 			=     layers.Dense(2     )

       def gen_random_output():
        UpperCamelCase			:      Optional[Any] 			=     tf.random.uniform((1, 3)     )
        return model(A_     ).numpy()

       with temp_seed(42 ,							set_tensorflow=A_     ):
        UpperCamelCase			:      List[Any] 			=     gen_random_output()
       with temp_seed(42 ,							set_tensorflow=A_     ):
        UpperCamelCase			:      Dict 			=     gen_random_output()
       UpperCamelCase			:      Optional[int] 			=     gen_random_output()

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





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       import torch

       def gen_random_output():
        UpperCamelCase			:      Optional[Any] 			=     torch.nn.Linear(3 ,							2     )
        UpperCamelCase			:      Dict 			=     torch.rand(1 ,							3     )
        return model(A_     ).detach().numpy()

       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Dict 			=     gen_random_output()
       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Optional[int] 			=     gen_random_output()
       UpperCamelCase			:      List[Any] 			=     gen_random_output()

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

       with temp_seed(42     ):
        UpperCamelCase			:      Optional[Any] 			=     gen_random_output()
       with temp_seed(42     ):
        UpperCamelCase			:      Optional[Any] 			=     gen_random_output()
       UpperCamelCase			:      Optional[Any] 			=     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[Any]:
 UpperCamelCase			:      Optional[Any] 			=     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  )		->				Tuple:
 UpperCamelCase			:      Dict 			=     NestedDataStructure(_lowerCAmelCase  ).flatten()
 assert output == expected_output



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      str 			=     A(x=1							,     y="foobar"  )
 UpperCamelCase			:      Tuple 			=     {"x": 1, "y": "foobar"}
 assert asdict(_lowerCAmelCase  ) == expected_output

 UpperCamelCase			:      List[str] 			=     {"a": {"b": A(x=10							,     y="foo"  )}, "c": [A(x=20							,     y="bar"  )]}
 UpperCamelCase			:      Tuple 			=     {"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  )		->				Tuple:
 return text.split()



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 yield (time.time(), content)
 time.sleep(2  )
 yield (time.time(), content)



def 	A_       (  )		->				str:
 with Pool(2  ) as pool:
  UpperCamelCase			:      List[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:
  UpperCamelCase			:      Dict 			=     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:
  UpperCamelCase			:      Any 			=     []
  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
