'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase__	:   str										=       logging.get_logger(__name__)







class lowerCamelCase				(						lowerCamelCase       ):




  '''simple docstring'''



  def __init__(   self      :							Any	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							float	,				**UpperCAmelCase__      :							List[str]	)					->List[str]:
        UpperCAmelCase_      =	feature_size
        UpperCAmelCase_      =	sampling_rate
        UpperCAmelCase_      =	padding_value

        UpperCAmelCase_      =	kwargs.pop('''padding_side'''	,				'''right'''	)
        UpperCAmelCase_      =	kwargs.pop('''return_attention_mask'''	,				_lowercase	)

        super().__init__(**_lowercase	)






  def 			lowerCAmelCase__			(   self      :							Any	,				UpperCAmelCase__      :							Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]	,				UpperCAmelCase__      :							Union[bool, str, PaddingStrategy] = True	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							bool = False	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							Optional[bool] = None	,				UpperCAmelCase__      :							Optional[Union[str, TensorType]] = None	,				)					->BatchFeature:
        # If we have a list of dicts, let's convert it in a dict of lists
        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
        if isinstance(_lowercase	,				(list, tuple)	) and isinstance(processed_features[0]	,				(dict, BatchFeature)	):
              UpperCAmelCase_      =	{
                  key: [example[key] for example in processed_features] for key in processed_features[0].keys()
              }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
              raise ValueError(
                  '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
                  f""" to this method that includes {self.model_input_names[0]}, but you provided"""
                  f""" {list(processed_features.keys()	)}"""	)

        UpperCAmelCase_      =	processed_features[self.model_input_names[0]]
        UpperCAmelCase_      =	(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(_lowercase	) == 0:
              if return_attention_mask:
                    UpperCAmelCase_      =	[]
              return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        UpperCAmelCase_      =	required_input[0]
        if isinstance(_lowercase	,				(list, tuple)	):
              # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
              UpperCAmelCase_      =	0
              while len(required_input[index]	) == 0:
                    index += 1
              if index < len(_lowercase	):
                    UpperCAmelCase_      =	required_input[index][0]

        if return_tensors is None:
              if is_tf_tensor(_lowercase	):
                    UpperCAmelCase_      =	'''tf'''
              elif is_torch_tensor(_lowercase	):
                    UpperCAmelCase_      =	'''pt'''
              elif isinstance(_lowercase	,				(int, float, list, tuple, np.ndarray)	):
                    UpperCAmelCase_      =	'''np'''
              else:
                    raise ValueError(
                        f"""type of {first_element} unknown: {type(_lowercase	)}. """
                        '''Should be one of a python, numpy, pytorch or tensorflow object.'''	)

        for key, value in processed_features.items():
              if isinstance(value[0]	,				(int, float)	):
                    UpperCAmelCase_      =	to_numpy(_lowercase	)
              else:
                    UpperCAmelCase_      =	[to_numpy(_lowercase	) for v in value]

        # Convert padding_strategy in PaddingStrategy
        UpperCAmelCase_      =	self._get_padding_strategies(padding=_lowercase	,				max_length=_lowercase	)

        UpperCAmelCase_      =	processed_features[self.model_input_names[0]]

        UpperCAmelCase_      =	len(_lowercase	)
        if not all(len(_lowercase	) == batch_size for v in processed_features.values()	):
              raise ValueError('''Some items in the output dictionary have a different batch size than others.'''	)

        UpperCAmelCase_      =	[]
        for i in range(_lowercase	):
              UpperCAmelCase_      =	{k: v[i] for k, v in processed_features.items()}
              # truncation
              UpperCAmelCase_      =	self._truncate(
                  _lowercase	,				max_length=_lowercase	,				pad_to_multiple_of=_lowercase	,				truncation=_lowercase	,				)
              truncated_inputs.append(_lowercase	)

        if padding_strategy == PaddingStrategy.LONGEST:
              # make sure that `max_length` cannot be longer than the longest truncated length
              UpperCAmelCase_      =	max(len(input_slice[self.model_input_names[0]]	) for input_slice in truncated_inputs	)
              UpperCAmelCase_      =	PaddingStrategy.MAX_LENGTH

        UpperCAmelCase_      =	{}
        for i in range(_lowercase	):
              # padding
              UpperCAmelCase_      =	self._pad(
                  truncated_inputs[i]	,				max_length=_lowercase	,				padding_strategy=_lowercase	,				pad_to_multiple_of=_lowercase	,				return_attention_mask=_lowercase	,				)

              for key, value in outputs.items():
                    if key not in batch_outputs:
                          UpperCAmelCase_      =	[]
                    if value.dtype is np.dtype(np.floataa	):
                          UpperCAmelCase_      =	value.astype(np.floataa	)
                    batch_outputs[key].append(_lowercase	)

        return BatchFeature(_lowercase	,				tensor_type=_lowercase	)






  def 			lowerCAmelCase__			(   self      :							List[str]	,				UpperCAmelCase__      :							Union[Dict[str, np.ndarray], BatchFeature]	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							Optional[bool] = None	,				)					->dict:
        UpperCAmelCase_      =	processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
              UpperCAmelCase_      =	len(_lowercase	)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              UpperCAmelCase_      =	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        UpperCAmelCase_      =	padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase	) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
              UpperCAmelCase_      =	np.ones(len(_lowercase	)	,				dtype=np.intaa	)

        if needs_to_be_padded:
              UpperCAmelCase_      =	max_length - len(_lowercase	)
              if self.padding_side == "right":
                    if return_attention_mask:
                          UpperCAmelCase_      =	np.pad(
                              processed_features['''attention_mask''']	,				(0, difference)	)
                    UpperCAmelCase_      =	((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    UpperCAmelCase_      =	np.pad(
                        _lowercase	,				_lowercase	,				'''constant'''	,				constant_values=self.padding_value	)
              elif self.padding_side == "left":
                    if return_attention_mask:
                          UpperCAmelCase_      =	np.pad(
                              processed_features['''attention_mask''']	,				(difference, 0)	)
                    UpperCAmelCase_      =	((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    UpperCAmelCase_      =	np.pad(
                        _lowercase	,				_lowercase	,				'''constant'''	,				constant_values=self.padding_value	)
              else:
                    raise ValueError('''Invalid padding strategy:''' + str(self.padding_side	)	)

        return processed_features






  def 			lowerCAmelCase__			(   self      :							str	,				UpperCAmelCase__      :							Union[Dict[str, np.ndarray], BatchFeature]	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							Optional[int] = None	,				UpperCAmelCase__      :							Optional[bool] = None	,				)					->Optional[int]:
        if not truncation:
              return processed_features
        elif truncation and max_length is None:
              raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''	)

        UpperCAmelCase_      =	processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              UpperCAmelCase_      =	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        UpperCAmelCase_      =	len(_lowercase	) > max_length

        if needs_to_be_truncated:
              UpperCAmelCase_      =	processed_features[self.model_input_names[0]][:max_length]
              if "attention_mask" in processed_features:
                    UpperCAmelCase_      =	processed_features['''attention_mask'''][:max_length]

        return processed_features






  def 			lowerCAmelCase__			(   self      :							int	,				UpperCAmelCase__      :							Optional[int]=False	,				UpperCAmelCase__      :							Optional[int]=None	)					->Optional[int]:

        # Get padding strategy
        if padding is not False:
              if padding is True:
                    UpperCAmelCase_      =	PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(_lowercase	,				_lowercase	):
                    UpperCAmelCase_      =	PaddingStrategy(_lowercase	)
              elif isinstance(_lowercase	,				_lowercase	):
                    UpperCAmelCase_      =	padding
        else:
              UpperCAmelCase_      =	PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
              if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""	)

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
              raise ValueError(
                  '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
                  ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''	)

        return padding_strategy











'''simple docstring'''
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE			=						get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ReformerTokenizer
					_UpperCamelCase							=		ReformerTokenizerFast
					_UpperCamelCase							=		True
					_UpperCamelCase							=		False
					_UpperCamelCase							=		True



					def     __snake_case						(	self     :						Optional[int])    ->							int:
								super().setUp()

								A_									= ReformerTokenizer(_lowercase      ,       keep_accents=_lowercase)
								tokenizer.save_pretrained(self.tmpdirname)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= '<s>'
								A_									= 1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase)      ,       _lowercase)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase)      ,       _lowercase)



					def     __snake_case						(	self     :						Any)    ->							Optional[Any]:
								A_									= list(self.get_tokenizer().get_vocab().keys())

								self.assertEqual(vocab_keys[0]      ,       '<unk>')
								self.assertEqual(vocab_keys[1]      ,       '<s>')
								self.assertEqual(vocab_keys[-1]      ,       'j')
								self.assertEqual(len(_lowercase)      ,       1_000)



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								self.assertEqual(self.get_tokenizer().vocab_size      ,       1_000)



					def     __snake_case						(	self     :						List[Any])    ->							Any:
								if not self.test_rust_tokenizer:
											return

								A_									= self.get_tokenizer()
								A_									= self.get_rust_tokenizer()

								A_									= 'I was born in 92000, and this is falsé.'

								A_									= tokenizer.tokenize(_lowercase)
								A_									= rust_tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokenizer.encode(_lowercase      ,       add_special_tokens=_lowercase)
								A_									= rust_tokenizer.encode(_lowercase      ,       add_special_tokens=_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= self.get_rust_tokenizer()
								A_									= tokenizer.encode(_lowercase)
								A_									= rust_tokenizer.encode(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[int]=15)    ->							str:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
														A_									= self.rust_tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)

														# Simple input
														A_									= 'This is a simple input'
														A_									= ['This is a simple input 1', 'This is a simple input 2']
														A_									= ('This is a simple input', 'This is a pair')
														A_									= [
														    ('This is a simple input 1', 'This is a simple input 2'),
														    ('This is a simple pair 1', 'This is a simple pair 2'),
														]

														# Simple input tests
														self.assertRaises(_lowercase      ,       tokenizer_r.encode      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length')

														# Simple input
														self.assertRaises(_lowercase      ,       tokenizer_r.encode_plus      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length')

														# Simple input
														self.assertRaises(
														    _lowercase      ,       tokenizer_r.batch_encode_plus      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length'      ,       )

														# Pair input
														self.assertRaises(_lowercase      ,       tokenizer_r.encode      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length')

														# Pair input
														self.assertRaises(_lowercase      ,       tokenizer_r.encode_plus      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length')

														# Pair input
														self.assertRaises(
														    _lowercase      ,       tokenizer_r.batch_encode_plus      ,       _lowercase      ,       max_length=_lowercase      ,       padding='max_length'      ,       )



					def     __snake_case						(	self     :						Optional[int])    ->							Optional[int]:
								pass



					def     __snake_case						(	self     :						Tuple)    ->							Optional[Any]:
								A_									= ReformerTokenizer(_lowercase      ,       keep_accents=_lowercase)

								A_									= tokenizer.tokenize('This is a test')
								self.assertListEqual(_lowercase      ,       ['▁This', '▁is', '▁a', '▁t', 'est'])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(_lowercase)      ,       [285, 46, 10, 170, 382]      ,       )

								A_									= tokenizer.tokenize('I was born in 92000, and this is falsé.')
								self.assertListEqual(
								    _lowercase      ,       [
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '9',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        'é',
								        '.',
								    ]      ,       )
								A_									= tokenizer.convert_tokens_to_ids(_lowercase)
								self.assertListEqual(
								    _lowercase      ,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,       )

								A_									= tokenizer.convert_ids_to_tokens(_lowercase)
								self.assertListEqual(
								    _lowercase      ,       [
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '<unk>',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        '<unk>',
								        '.',
								    ]      ,       )



					@cached_property
					def     __snake_case						(	self     :						str)    ->							Any:
								return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')



					@slow
					def     __snake_case						(	self     :						Tuple)    ->							Union[str, Any]:
								A_									= 'Hello World!'
								A_									= [126, 32, 262, 152, 38, 72, 287]

								self.assertListEqual(_lowercase      ,       self.big_tokenizer.encode(_lowercase))



					@slow
					def     __snake_case						(	self     :						Optional[int])    ->							int:
								A_									= (
								    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
								    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
								)
								A_									= [
								    108,
								    265,
								    24,
								    111,
								    4,
								    258,
								    156,
								    35,
								    28,
								    275,
								    3,
								    259,
								    297,
								    260,
								    84,
								    4,
								    35,
								    110,
								    44,
								    8,
								    259,
								    91,
								    268,
								    21,
								    11,
								    209,
								    274,
								    109,
								    266,
								    277,
								    117,
								    86,
								    93,
								    315,
								    258,
								    278,
								    258,
								    277,
								    258,
								    0,
								    258,
								    288,
								    258,
								    319,
								    258,
								    0,
								    258,
								    0,
								    258,
								    0,
								    258,
								    0,
								    258,
								    287,
								    258,
								    315,
								    258,
								    289,
								    258,
								    278,
								    99,
								    269,
								    266,
								    262,
								    8,
								    259,
								    241,
								    4,
								    217,
								    230,
								    268,
								    266,
								    55,
								    168,
								    106,
								    75,
								    193,
								    266,
								    223,
								    27,
								    49,
								    26,
								    282,
								    25,
								    264,
								    299,
								    19,
								    26,
								    0,
								    258,
								    277,
								    117,
								    86,
								    93,
								    176,
								    183,
								    270,
								    11,
								    262,
								    42,
								    61,
								    265,
								]

								self.assertListEqual(_lowercase      ,       self.big_tokenizer.encode(_lowercase))



					@require_torch
					@slow
					def     __snake_case						(	self     :						List[str])    ->							Tuple:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								A_									= list(self.big_tokenizer.get_vocab().keys())[:10]
								A_									= ' '.join(_lowercase)
								A_									= self.big_tokenizer.encode_plus(_lowercase      ,       return_tensors='pt')
								A_									= self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,       return_tensors='pt')

								A_									= ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								A_									= encoded_sequence['input_ids'].shape
								A_									= ReformerModel(_lowercase)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
											model(**_lowercase)
											model(**_lowercase)



					@slow
					def     __snake_case						(	self     :						str)    ->							Union[str, Any]:
								# fmt: off
								A_									= {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								A_									= [
								    'This is a very simple sentence.',
								    'The quick brown fox jumps over the lazy dog.',
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=_lowercase      ,       model_name='google/reformer-crime-and-punishment'      ,       revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'      ,       padding=_lowercase      ,       sequences=_lowercase      ,       )




