






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{}
UpperCamelCase        =				{}
UpperCamelCase        =				{}




def 				A		(					lowercase__  :  type ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None ,		)		-> int:
	UpperCamelCase__ :Union[str, Any]						=      aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""  )
	UpperCamelCase__ :Union[str, Any]						=      formatter_cls
	for alias in set(aliases + [format_type]  ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""  )
		UpperCamelCase__ :List[Any]						=      format_type




def 				A		(					lowercase__  :  Exception ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None  )		-> List[Any]:
	UpperCamelCase__ :Any						=      aliases if aliases is not None else []
	for alias in set(aliases + [format_type]  ):
		UpperCamelCase__ :Optional[int]						=      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
							UpperCamelCase        =				ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
							_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
							UpperCamelCase        =				ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
							_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, "jax", aliases=[])
else:
							UpperCamelCase        =				ValueError("JAX needs to be installed to be able to return JAX arrays.")
							_register_unavailable_formatter(_jax_error, "jax", aliases=[])




def 				A		(					lowercase__  :  Optional[str]  )		-> Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def 				A		(					lowercase__  :  Optional[str] ,		**lowercase__  :  Optional[Any]  )		-> Formatter:
	UpperCamelCase__ :Union[str, Any]						=      get_format_type_from_alias(lowercase__  )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowercase__  )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None  )}, but got '{format_type}'"""  )





'''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      ,       )




