





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     )						-> int:  # picklable for multiprocessing
 return x.sum()



def 		A_				(     _lowerCAmelCase     )						-> str:  # picklable for multiprocessing
 return i + 1



@dataclass
class 					A__   :
      _UpperCAmelCase	:int
      _UpperCAmelCase	:str



class 					A__   (       __snake_case			):





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




       UpperCamelCase	:					Dict       	=   {}
       UpperCamelCase	:					Any       	=   []
       UpperCamelCase	:					Dict       	=   1
       UpperCamelCase	:					Optional[int]       	=   [1, 2]
       UpperCamelCase	:					Union[str, Any]       	=   {"a": 1, "b": 2}
       UpperCamelCase	:					Optional[Any]       	=   {"a": [1, 2], "b": [3, 4]}
       UpperCamelCase	:					Optional[Any]       	=   {"a": {"1": 1}, "b": 2}
       UpperCamelCase	:					Optional[int]       	=   {"a": 1, "b": 2, "c": 3, "d": 4}
       UpperCamelCase	:					Dict       	=   {}
       UpperCamelCase	:					List[str]       	=   []
       UpperCamelCase	:					Union[str, Any]       	=   2
       UpperCamelCase	:					str       	=   [2, 3]
       UpperCamelCase	:					str       	=   {"a": 2, "b": 3}
       UpperCamelCase	:					Optional[Any]       	=   {"a": [2, 3], "b": [4, 5]}
       UpperCamelCase	:					List[str]       	=   {"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	:					Any       	=   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	:					Optional[int]       	=   {"a": np.eye(2   ), "b": np.zeros(3   ), "c": np.ones(2   )}
       UpperCamelCase	:					Optional[Any]       	=   {"a": 2, "b": 0, "c": 2}
       UpperCamelCase	:					Optional[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	:					List[Any]       	=   {"a": 1, "b": 2}
       UpperCamelCase	:					Union[str, Any]       	=   {"a": 3, "b": 4}
       UpperCamelCase	:					Optional[int]       	=   {"a": 5, "b": 6}
       UpperCamelCase	:					Tuple       	=   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	:int						    =   'bar'


       UpperCamelCase	:					Dict       	=   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     )						-> List[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	:					int       	=   {F"""{i}""": i for i in range(_lowerCAmelCase     )}
  UpperCamelCase	:					Optional[int]       	=   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	:					Dict       	=   layers.Dense(2   )

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

       with temp_seed(42       ,							set_tensorflow=A_   ):
        UpperCamelCase	:					Optional[Any]       	=   gen_random_output()
       with temp_seed(42       ,							set_tensorflow=A_   ):
        UpperCamelCase	:					List[str]       	=   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	:					Any       	=   torch.nn.Linear(3       ,							2   )
        UpperCamelCase	:					Union[str, Any]       	=   torch.rand(1       ,							3   )
        return model(A_   ).detach().numpy()

       with temp_seed(42       ,							set_pytorch=A_   ):
        UpperCamelCase	:					List[str]       	=   gen_random_output()
       with temp_seed(42       ,							set_pytorch=A_   ):
        UpperCamelCase	:					Dict       	=   gen_random_output()
       UpperCamelCase	:					str       	=   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	:					Tuple       	=   gen_random_output()
       with temp_seed(42   ):
        UpperCamelCase	:					int       	=   gen_random_output()
       UpperCamelCase	:					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     )						-> Dict:
 UpperCamelCase	:					Union[str, 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	:					Union[str, Any]       	=   NestedDataStructure(_lowerCAmelCase     ).flatten()
 assert output == expected_output



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

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



def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 yield (time.time(), content)
 time.sleep(2     )
 yield (time.time(), content)



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

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       A	(    _lowercase						):
						if "model" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.replace('''model.'''		,  ''''''						)
						if "norm1" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''norm2'''		,  '''output.LayerNorm'''						)
						if "norm" in orig_key:
												SCREAMING_SNAKE_CASE							:    Tuple							=							orig_key.replace('''norm'''		,  '''LayerNorm'''						)
						if "transformer" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												SCREAMING_SNAKE_CASE							:    List[str]							=							orig_key.replace(f"""transformer_{layer_num}"""		,  f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''mha.attn'''		,  '''attention.self'''						)
						if "mha" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mha'''		,  '''attention'''						)
						if "W_q" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''W_q'''		,  '''self.query'''						)
						if "W_k" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''W_k'''		,  '''self.key'''						)
						if "W_v" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''W_v'''		,  '''self.value'''						)
						if "ff1" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''ff1'''		,  '''intermediate.dense'''						)
						if "ff2" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''ff2'''		,  '''output.dense'''						)
						if "ff" in orig_key:
												SCREAMING_SNAKE_CASE							:    Dict							=							orig_key.replace('''ff'''		,  '''output.dense'''						)
						if "mlm_class" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''						)
						if "cls" not in orig_key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''yoso.''' + orig_key

						return orig_key
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							orig_state_dict.pop(_lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val

						SCREAMING_SNAKE_CASE							:    List[str]							=							orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.arange(_lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model_state_dict''']
						SCREAMING_SNAKE_CASE							:    List[Any]							=							YosoConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							YosoForMaskedLM(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_checkpoint_helper(config.max_position_embeddings		,  _lowercase						)

						print(model.load_state_dict(_lowercase						)						)
						model.eval()
						model.save_pretrained(_lowercase						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					__UpperCamelCase		:       Tuple									=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					__UpperCamelCase		:       Optional[Any]									=     parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





