
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 (      lowercase				)     ->						Dict:  # picklable for multiprocessing
		'''simple docstring'''

		return x.sum()


def    A (      lowercase				)     ->						Tuple:  # picklable for multiprocessing
		'''simple docstring'''

		return i + 1


@dataclass
class        lowercase      :
		__lowercase : int
		__lowercase : str






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __UpperCamelCase				(				self )						->      List[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(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 __UpperCamelCase				(				self )						->      List[str]:
				"""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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""


				class        lowercase      :
						__lowercase : 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),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]					, )
def    A (      lowercase					, lowercase					, lowercase				)     ->						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(lowercase				)}
				UpperCamelCase						=  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        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@require_tf
		def __UpperCamelCase				(				self )						->      List[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(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 __UpperCamelCase				(				self )						->      List[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 __UpperCamelCase				(				self )						->      Union[str, 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(A_       ,   A_ )
				self.assertGreater(np.abs(outa - outa ).sum()       ,   0 )






@pytest.mark.parametrize('input_data'					, [{}]				)
def    A (      lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  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    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  NestedDataStructure(lowercase				).flatten()
		assert output == expected_output


def    A (      )     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  A(x=1					, y='foobar'				)
		UpperCamelCase						=  {'x': 1, 'y': 'foobar'}
		assert asdict(lowercase				) == expected_output

		UpperCamelCase						=  {'a': {'b': A(x=10					, y='foo'				)}, 'c': [A(x=20					, y='bar'				)]}
		UpperCamelCase						=  {'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    A (      lowercase				)     ->						str:
		'''simple docstring'''

		return text.split()


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

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


def    A (      )     ->						Optional[Any]:
		'''simple docstring'''

		with Pool(2				) as pool:
				UpperCamelCase						=  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:
				UpperCamelCase						=  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:
				UpperCamelCase						=  []
				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


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_camembert import CamembertTokenizer
else:
					_UpperCAmelCase      :				List[str]          = None


_UpperCAmelCase      :				Any          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase      :				List[str]          = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_UpperCAmelCase      :				Optional[int]          = {
    "camembert-base": 512,
}

_UpperCAmelCase      :				Union[str, Any]          = "▁"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[str]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : List[str]			  =       ["input_ids", "attention_mask"]
		__lowercase : Tuple			  =       CamembertTokenizer



		def __init__(				self       ,   A_=None       ,   A_=None       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=["<s>NOTUSED", "</s>NOTUSED"]       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else mask_token

				super().__init__(
				    A_       ,   tokenizer_file=A_       ,   bos_token=A_       ,   eos_token=A_       ,   sep_token=A_       ,   cls_token=A_       ,   unk_token=A_       ,   pad_token=A_       ,   mask_token=A_       ,   additional_special_tokens=A_       ,   **A_       ,   )

				UpperCamelCase						=  vocab_file
				UpperCamelCase						=  False if not self.vocab_file else True



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]
				UpperCamelCase						=  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				UpperCamelCase						=  [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.' )

				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

				if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ):
						copyfile(self.vocab_file       ,   A_ )

				return (out_vocab_file,)
