

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


_UpperCAmelCase		:   Tuple										=       get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			ReformerTokenizer
				__SCREAMING_SNAKE_CASE					: Dict       			=			ReformerTokenizerFast
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Tuple       			=			True




				def     a							(					self       ):
											super().setUp()

											snake_case_							=    ReformerTokenizer(snake_case      ,   keep_accents=snake_case       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    '<s>'
											snake_case_							=    1

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




				def     a							(					self       ):
											snake_case_							=    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(snake_case       )      ,   1000       )




				def     a							(					self       ):
											self.assertEqual(self.get_tokenizer().vocab_size      ,   1000       )




				def     a							(					self       ):
											if not self.test_rust_tokenizer:
																		return

											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()

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

											snake_case_							=    tokenizer.tokenize(snake_case       )
											snake_case_							=    rust_tokenizer.tokenize(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
											snake_case_							=    rust_tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    tokenizer.encode(snake_case       )
											snake_case_							=    rust_tokenizer.encode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=15       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )

																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('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(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

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

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

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

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

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




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											snake_case_							=    ReformerTokenizer(snake_case      ,   keep_accents=snake_case       )

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

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

											snake_case_							=    tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
											self.assertListEqual(
											    snake_case      ,   [
											        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',
											        '.',
											    ]      ,   )
											snake_case_							=    tokenizer.convert_tokens_to_ids(snake_case       )
											self.assertListEqual(
											    snake_case      ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,   )

											snake_case_							=    tokenizer.convert_ids_to_tokens(snake_case       )
											self.assertListEqual(
											    snake_case      ,   [
											        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     a							(					self       ):
											return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'       )




				@slow
				def     a							(					self       ):
											snake_case_							=    'Hello World!'
											snake_case_							=    [126, 32, 262, 152, 38, 72, 287]

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




				@slow
				def     a							(					self       ):
											snake_case_							=    (
											    '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'
											)
											snake_case_							=    [
											    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(snake_case      ,   self.big_tokenizer.encode(snake_case       )       )




				@require_torch
				@slow
				def     a							(					self       ):
											import torch

											from transformers import ReformerConfig, ReformerModel

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

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

											# 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(**snake_case       )
																		model(**snake_case       )




				@slow
				def     a							(					self       ):
											# fmt: off
											snake_case_							=    {'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
											snake_case_							=    [
											    'This is a very simple sentence.',
											    'The quick brown fox jumps over the lazy dog.',
											]

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







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




