import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__lowercase											=      logging.get_logger(__name__)


__lowercase											=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase											=      {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__lowercase											=      {
    '''allenai/led-base-16384''': 1_6384,
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[str]      			=   VOCAB_FILES_NAMES
    a__			:		List[str]      			=   PRETRAINED_VOCAB_FILES_MAP
    a__			:		List[Any]      			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__			:		List[str]      			=   LEDTokenizer
    a__			:		Union[str, Any]      			=   ["""input_ids""", """attention_mask"""]







    def __init__(			self				,				__lowercase=None				,				__lowercase=None				,				__lowercase=None				,				__lowercase="replace"				,				__lowercase="<s>"				,				__lowercase="</s>"				,				__lowercase="</s>"				,				__lowercase="<s>"				,				__lowercase="<unk>"				,				__lowercase="<pad>"				,				__lowercase="<mask>"				,				__lowercase=False				,				__lowercase=True				,				**__lowercase				,				) ->			Optional[Any]:
        super().__init__(
            __lowercase				,				__lowercase				,				tokenizer_file=__lowercase				,				errors=__lowercase				,				bos_token=__lowercase				,				eos_token=__lowercase				,				sep_token=__lowercase				,				cls_token=__lowercase				,				unk_token=__lowercase				,				pad_token=__lowercase				,				mask_token=__lowercase				,				add_prefix_space=__lowercase				,				trim_offsets=__lowercase				,				**__lowercase				,				)

        __UpperCamelCase  :Any    						=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space'''				,				__lowercase) != add_prefix_space:
            __UpperCamelCase  :Any    						=	getattr(__lowercase				,				pre_tok_state.pop('''type'''))
            __UpperCamelCase  :Optional[Any]    						=	add_prefix_space
            __UpperCamelCase  :List[str]    						=	pre_tok_class(**__lowercase)

        __UpperCamelCase  :int    						=	add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        __UpperCamelCase  :Dict    						=	'''post_processor'''
        __UpperCamelCase  :Union[str, Any]    						=	getattr(self.backend_tokenizer				,				__lowercase				,				__lowercase)
        if tokenizer_component_instance:
            __UpperCamelCase  :Any    						=	json.loads(tokenizer_component_instance.__getstate__())

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                __UpperCamelCase  :List[Any]    						=	tuple(state['''sep'''])
            if "cls" in state:
                __UpperCamelCase  :List[Any]    						=	tuple(state['''cls'''])

            __UpperCamelCase  :Any    						=	False

            if state.get('''add_prefix_space'''				,				__lowercase) != add_prefix_space:
                __UpperCamelCase  :List[Any]    						=	add_prefix_space
                __UpperCamelCase  :Union[str, Any]    						=	True

            if state.get('''trim_offsets'''				,				__lowercase) != trim_offsets:
                __UpperCamelCase  :Optional[int]    						=	trim_offsets
                __UpperCamelCase  :Dict    						=	True

            if changes_to_apply:
                __UpperCamelCase  :List[Any]    						=	getattr(__lowercase				,				state.pop('''type'''))
                __UpperCamelCase  :List[Any]    						=	component_class(**__lowercase)
                setattr(self.backend_tokenizer				,				__lowercase				,				__lowercase)







    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def     UpperCamelCase__     (			self) ->			str:
        if self._mask_token is None:
            if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.''')
            return None
        return str(self._mask_token)







    @mask_token.setter
    def     UpperCamelCase__     (			self				,				__lowercase) ->			Tuple:
        __UpperCamelCase  :Optional[Any]    						=	AddedToken(__lowercase				,				lstrip=__lowercase				,				rstrip=__lowercase) if isinstance(__lowercase				,				__lowercase) else value
        __UpperCamelCase  :Tuple    						=	value







    def     UpperCamelCase__     (			self				,				*__lowercase				,				**__lowercase) ->			BatchEncoding:
        __UpperCamelCase  :Union[str, Any]    						=	kwargs.get('''is_split_into_words'''				,				__lowercase)

        if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                '''to use it with pretokenized inputs.''')

        return super()._batch_encode_plus(*__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				*__lowercase				,				**__lowercase) ->			BatchEncoding:
        __UpperCamelCase  :Optional[int]    						=	kwargs.get('''is_split_into_words'''				,				__lowercase)

        if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                '''to use it with pretokenized inputs.''')

        return super()._encode_plus(*__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			Tuple[str]:
        __UpperCamelCase  :str    						=	self._tokenizer.model.save(__lowercase				,				name=__lowercase)
        return tuple(__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=None) ->			Dict:
        __UpperCamelCase  :Optional[int]    						=	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
            return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None) ->			List[int]:
        __UpperCamelCase  :Tuple    						=	[self.sep_token_id]
        __UpperCamelCase  :Optional[Any]    						=	[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				,				__lowercase				,				__lowercase = None				,				__lowercase = PaddingStrategy.DO_NOT_PAD				,				__lowercase = None				,				__lowercase = None				,				) ->			dict:
        __UpperCamelCase  :Union[str, Any]    						=	super()._pad(
            encoded_inputs=__lowercase				,				max_length=__lowercase				,				padding_strategy=__lowercase				,				pad_to_multiple_of=__lowercase				,				return_attention_mask=__lowercase				,				)

        # Load from model defaults
        if return_attention_mask is None:
            __UpperCamelCase  :List[Any]    						=	'''attention_mask''' in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
            __UpperCamelCase  :List[str]    						=	encoded_inputs[self.model_input_names[0]]
            # `global_attention_mask` need to have the same length as other (sequential) inputs.
            __UpperCamelCase  :Optional[Any]    						=	len(encoded_inputs['''global_attention_mask''']) != len(__lowercase)

            if needs_to_be_padded:
                __UpperCamelCase  :List[str]    						=	len(__lowercase) - len(encoded_inputs['''global_attention_mask'''])

                if self.padding_side == "right":
                    # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                    __UpperCamelCase  :List[Any]    						=	(
                        encoded_inputs['''global_attention_mask'''] + [-1] * difference
                    )
                elif self.padding_side == "left":
                    __UpperCamelCase  :Optional[Any]    						=	[-1] * difference + encoded_inputs[
                        '''global_attention_mask'''
                    ]
                else:
                    raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

        return encoded_inputs






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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return x.sum()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return i + 1


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		int
    a__			:		str


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	{}
        __UpperCamelCase  :int    						=	[]
        __UpperCamelCase  :int    						=	1
        __UpperCamelCase  :int    						=	[1, 2]
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :Dict    						=	{'''a''': [1, 2], '''b''': [3, 4]}
        __UpperCamelCase  :Optional[int]    						=	{'''a''': {'''1''': 1}, '''b''': 2}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        __UpperCamelCase  :str    						=	{}
        __UpperCamelCase  :Any    						=	[]
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Optional[int]    						=	[2, 3]
        __UpperCamelCase  :List[Any]    						=	{'''a''': 2, '''b''': 3}
        __UpperCamelCase  :List[str]    						=	{'''a''': [2, 3], '''b''': [4, 5]}
        __UpperCamelCase  :int    						=	{'''a''': {'''1''': 2}, '''b''': 3}
        __UpperCamelCase  :Union[str, Any]    						=	{'''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  :Optional[Any]    						=	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  :Dict    						=	{'''a''': np.eye(2), '''b''': np.zeros(3), '''c''': np.ones(2)}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 2, '''b''': 0, '''c''': 2}
        __UpperCamelCase  :Optional[int]    						=	{
            '''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]:
        __UpperCamelCase  :List[str]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :int    						=	{'''a''': 3, '''b''': 4}
        __UpperCamelCase  :List[str]    						=	{'''a''': 5, '''b''': 6}
        __UpperCamelCase  :List[Any]    						=	sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
        self.assertEqual(sorted(zip_dict(__lowercase				,				__lowercase				,				__lowercase))				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:


        class 			lowerCamelCase_  :






            '''simple docstring'''






            a__			:		int      			=   """bar"""
        __UpperCamelCase  :List[str]    						=	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),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]						,    )
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''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  :Union[str, Any]    						=	{f"""{i}""": i for i in range(SCREAMING_SNAKE_CASE			)}
        __UpperCamelCase  :Dict    						=	map_nested(lambda SCREAMING_SNAKE_CASE			: x + 10						,    SCREAMING_SNAKE_CASE						,    num_proc=SCREAMING_SNAKE_CASE						,    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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        import tensorflow as tf
        from tensorflow.keras import layers

        __UpperCamelCase  :Optional[Any]    						=	layers.Dense(2)

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

        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :Tuple    						=	gen_random_output()
        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :int    						=	gen_random_output()
        __UpperCamelCase  :List[str]    						=	gen_random_output()

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







    @require_torch
    def     UpperCamelCase__     (			self) ->			List[str]:
        import torch

        def gen_random_output():
            __UpperCamelCase  :List[Any]    						=	torch.nn.Linear(3				,				2)
            __UpperCamelCase  :Optional[Any]    						=	torch.rand(1				,				3)
            return model(__lowercase).detach().numpy()

        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :Union[str, Any]    						=	gen_random_output()
        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :List[str]    						=	gen_random_output()
        __UpperCamelCase  :Dict    						=	gen_random_output()

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







    def     UpperCamelCase__     (			self) ->			str:
        def gen_random_output():
            return np.random.rand(1				,				3)

        with temp_seed(42):
            __UpperCamelCase  :List[Any]    						=	gen_random_output()
        with temp_seed(42):
            __UpperCamelCase  :Optional[Any]    						=	gen_random_output()
        __UpperCamelCase  :Optional[Any]    						=	gen_random_output()

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



@pytest.mark.parametrize('''input_data'''						,    [{}]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).flatten()
    assert output == expected_output



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	A(x=1						,    y='''foobar'''			)
    __UpperCamelCase  :Dict    						=	{'''x''': 1, '''y''': '''foobar'''}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

    __UpperCamelCase  :str    						=	{'''a''': {'''b''': A(x=10						,    y='''foo'''			)}, '''c''': [A(x=20						,    y='''bar'''			)]}
    __UpperCamelCase  :List[Any]    						=	{'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

    with pytest.raises(SCREAMING_SNAKE_CASE			):
        asdict([1, A(x=10						,    y='''foo'''			)]			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return text.split()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







    with Pool(2			) as pool:
        __UpperCamelCase  :Tuple    						=	list(iflatmap_unordered(SCREAMING_SNAKE_CASE						,    _split_text						,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
        assert out.count('''hello'''			) == 10
        assert out.count('''there'''			) == 10
        assert len(SCREAMING_SNAKE_CASE			) == 20

    # check multiprocess from pathos (uses dill for pickling)
    with multiprocess.Pool(2			) as pool:
        __UpperCamelCase  :Any    						=	list(iflatmap_unordered(SCREAMING_SNAKE_CASE						,    _split_text						,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
        assert out.count('''hello'''			) == 10
        assert out.count('''there'''			) == 10
        assert len(SCREAMING_SNAKE_CASE			) == 20

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





