


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 __UpperCAmelCase       (		__A			)						->					Optional[int]:  # picklable for multiprocessing



       '''simple docstring'''





       return x.sum()





def __UpperCAmelCase       (		__A			)						->					List[str]:  # picklable for multiprocessing



       '''simple docstring'''





       return i + 1


@dataclass
class     lowercase__  :
  A__=  42
  A__=  42




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """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(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase )		,						_lowercase )

         UpperCAmelCase__										=     2
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase )		,						_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """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(_lowercase		,						_lowercase		,						_lowercase ) )		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         class     lowercase__  :
           A__=  'bar'




         UpperCAmelCase__										=     Foo()
         self.assertEqual(foo.my_attr		,						"bar" )
         with temporary_assignment(_lowercase		,						"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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					str:



       '''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(__A			)}
              UpperCAmelCase__										=     map_nested(lambda __A			: x + 1_0 ,						__A ,						num_proc=__A ,						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     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  @require_tf
  def 			_UpperCAmelCase  (					self      :					str ):




         """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(_lowercase ).numpy()

         with temp_seed(42		,						set_tensorflow=_lowercase ):
                UpperCAmelCase__										=     gen_random_output()
         with temp_seed(42		,						set_tensorflow=_lowercase ):
                UpperCAmelCase__										=     gen_random_output()
         UpperCAmelCase__										=     gen_random_output()

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






  @require_torch
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         import torch

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

         with temp_seed(42		,						set_pytorch=_lowercase ):
                UpperCAmelCase__										=     gen_random_output()
         with temp_seed(42		,						set_pytorch=_lowercase ):
                UpperCAmelCase__										=     gen_random_output()
         UpperCAmelCase__										=     gen_random_output()

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






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




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





@pytest.mark.parametrize("input_data" ,						[{}]			)
def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     NestedDataStructure(__A			).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 __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     NestedDataStructure(__A			).flatten()
       assert output == expected_output





def __UpperCAmelCase       (		)						->					Optional[Any]:



       '''simple docstring'''





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

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





def __UpperCAmelCase       (		__A			)						->					Optional[Any]:



       '''simple docstring'''





       return text.split()





def __UpperCAmelCase       (		__A			)						->					Optional[Any]:



       '''simple docstring'''





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





def __UpperCAmelCase       (		)						->					List[Any]:



       '''simple docstring'''





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

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

       # check that we get items as fast as possible
       with Pool(2			) as pool:
              UpperCAmelCase__										=     []
              for yield_time, content in iflatmap_unordered(
                  __A ,						_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(__A			)
              assert out.count("a"			) == 2
              assert out.count("b"			) == 2
              assert len(__A			) == 4









import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       if (
           (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
           or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
           or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
           or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
           or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
           or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
       ):  #
              return True

       return False





def __UpperCAmelCase       (		__A			)						->					List[str]:



       '''simple docstring'''





       for char in word:
              UpperCAmelCase__										=     ord(__A			)
              if not _is_chinese_char(__A			):
                     return 0
       return 1





def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     set()

       for token in tokens:
              UpperCAmelCase__										=     len(__A			) > 1 and is_chinese(__A			)
              if chinese_word:
                     word_set.add(__A			)
       UpperCAmelCase__										=     list(__A			)
       return word_list





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       if not chinese_word_set:
              return bert_tokens
       UpperCAmelCase__										=     max([len(__A			) for w in chinese_word_set]			)

       UpperCAmelCase__										=     bert_tokens
       UpperCAmelCase__						,			UpperCAmelCase__										=     0, len(__A			)
       while start < end:
              UpperCAmelCase__										=     True
              if is_chinese(bert_word[start]			):
                     UpperCAmelCase__										=     min(end - start ,						__A			)
                     for i in range(__A ,						1 ,						-1			):
                            UpperCAmelCase__										=     "".join(bert_word[start : start + i]			)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,						start + i			):
                                          UpperCAmelCase__										=     "##" + bert_word[j]
                                   UpperCAmelCase__										=     start + i
                                   UpperCAmelCase__										=     False
                                   break
              if single_word:
                     start += 1
       return bert_word





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
              UpperCAmelCase__										=     [get_chinese_word(__A			) for r in res]
              ltp_res.extend(__A			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     bert_tokenizer(lines[i : i + 1_0_0] ,						add_special_tokens=__A ,						truncation=__A ,						max_length=5_1_2			)
              bert_res.extend(res["input_ids"]			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for input_ids, chinese_word in zip(__A ,						__A			):
              UpperCAmelCase__										=     []
              for id in input_ids:
                     UpperCAmelCase__										=     bert_tokenizer._convert_id_to_token(__A			)
                     input_tokens.append(__A			)
              UpperCAmelCase__										=     add_sub_symbol(__A ,						__A			)
              UpperCAmelCase__										=     []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__A			):
                     if token[:2] == "##":
                            UpperCAmelCase__										=     token[2:]
                            # save chinese tokens' pos
                            if len(__A			) == 1 and _is_chinese_char(ord(__A			)			):
                                   ref_id.append(__A			)
              ref_ids.append(__A			)

       assert len(__A			) == len(__A			)

       return ref_ids





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





       with open(args.file_name ,						"r" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     f.readlines()
       UpperCAmelCase__										=     [line.strip() for line in data if len(__A			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       UpperCAmelCase__										=     LTP(args.ltp			)  # faster in GPU device
       UpperCAmelCase__										=     BertTokenizer.from_pretrained(args.bert			)

       UpperCAmelCase__										=     prepare_ref(__A ,						__A ,						__A			)

       with open(args.save_path ,						"w" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     [json.dumps(__A			) + "\n" for ref in ref_ids]
              f.writelines(__A			)


if __name__ == "__main__":
    A      = argparse.ArgumentParser(description="prepare_chinese_ref")
    parser.add_argument(
        "--file_name",
        type=str,
        default="./resources/chinese-demo.txt",
        help="file need process, same as training data in lm",
    )
    parser.add_argument(
        "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
    )
    parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
    parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

    A      = parser.parse_args()
    main(args)





