
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


lowercase						:      str									= get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 				lowerCamelCase__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  ReformerTokenizer
	_A			=  ReformerTokenizerFast
	_A			=  True
	_A			=  False
	_A			=  True







	def      _lowerCamelCase       (      self	:str		)	->				int:
								super().setUp()

								__UpperCamelCase			:     Tuple         =      ReformerTokenizer(a  ,     keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[int]         =      "<s>"
								__UpperCamelCase			:     Tuple         =      1

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







	def      _lowerCamelCase       (      self	:int		)	->				int:
								__UpperCamelCase			:     List[Any]         =      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(a		)  ,     1_0_0_0		)







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				List[Any]:
								self.assertEqual(self.get_tokenizer().vocab_size  ,     1_0_0_0		)







	def      _lowerCamelCase       (      self	:Tuple		)	->				List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase			:     str         =      self.get_tokenizer()
								__UpperCamelCase			:     str         =      self.get_rust_tokenizer()

								__UpperCamelCase			:     Optional[int]         =      "I was born in 92000, and this is falsé."

								__UpperCamelCase			:     List[str]         =      tokenizer.tokenize(a		)
								__UpperCamelCase			:     Any         =      rust_tokenizer.tokenize(a		)
								self.assertListEqual(a  ,     a		)

								__UpperCamelCase			:     Dict         =      tokenizer.encode(a  ,     add_special_tokens=a		)
								__UpperCamelCase			:     List[str]         =      rust_tokenizer.encode(a  ,     add_special_tokens=a		)
								self.assertListEqual(a  ,     a		)

								__UpperCamelCase			:     Any         =      self.get_rust_tokenizer()
								__UpperCamelCase			:     Tuple         =      tokenizer.encode(a		)
								__UpperCamelCase			:     Optional[int]         =      rust_tokenizer.encode(a		)
								self.assertListEqual(a  ,     a		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[str]=1_5		)	->				Tuple:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase			:     Optional[Any]         =      self.rust_tokenizer_class.from_pretrained(a  ,     **a		)

																						# Simple input
																						__UpperCamelCase			:     Union[str, Any]         =      "This is a simple input"
																						__UpperCamelCase			:     List[Any]         =      ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase			:     Tuple         =      ("This is a simple input", "This is a pair")
																						__UpperCamelCase			:     str         =      [
																						    ("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(a  ,     tokenizer_r.encode  ,     a  ,     max_length=a  ,     padding="max_length"		)

																						# Simple input
																						self.assertRaises(a  ,     tokenizer_r.encode_plus  ,     a  ,     max_length=a  ,     padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a  ,     tokenizer_r.batch_encode_plus  ,     a  ,     max_length=a  ,     padding="max_length"  ,     )

																						# Pair input
																						self.assertRaises(a  ,     tokenizer_r.encode  ,     a  ,     max_length=a  ,     padding="max_length"		)

																						# Pair input
																						self.assertRaises(a  ,     tokenizer_r.encode_plus  ,     a  ,     max_length=a  ,     padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a  ,     tokenizer_r.batch_encode_plus  ,     a  ,     max_length=a  ,     padding="max_length"  ,     )







	def      _lowerCamelCase       (      self	:List[Any]		)	->				int:
								pass







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Any:
								__UpperCamelCase			:     List[Any]         =      ReformerTokenizer(a  ,     keep_accents=a		)

								__UpperCamelCase			:     Optional[Any]         =      tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)  ,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]  ,     )

								__UpperCamelCase			:     Dict         =      tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a  ,     [
								        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",
								        ".",
								    ]  ,     )
								__UpperCamelCase			:     Union[str, Any]         =      tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a  ,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]  ,     )

								__UpperCamelCase			:     List[Any]         =      tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a  ,     [
								        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      _lowerCamelCase       (      self	:Dict		)	->				List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				List[Any]:
								__UpperCamelCase			:     int         =      "Hello World!"
								__UpperCamelCase			:     Optional[Any]         =      [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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







	@slow
	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[Any]:
								__UpperCamelCase			:     List[str]         =      (
								    "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"
								)
								__UpperCamelCase			:     Tuple         =      [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

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







	@require_torch
	@slow
	def      _lowerCamelCase       (      self	:Any		)	->				int:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase			:     str         =      list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase			:     Optional[Any]         =      " ".join(a		)
								__UpperCamelCase			:     Union[str, Any]         =      self.big_tokenizer.encode_plus(a  ,     return_tensors="pt"		)
								__UpperCamelCase			:     Dict         =      self.big_tokenizer.batch_encode_plus([sequence, sequence]  ,     return_tensors="pt"		)

								__UpperCamelCase			:     Tuple         =      ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase			:     Optional[Any]         =      encoded_sequence["input_ids"].shape
								__UpperCamelCase			:     Optional[Any]         =      ReformerModel(a		)

								# 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(**a		)
															model(**a		)







	@slow
	def      _lowerCamelCase       (      self	:int		)	->				str:
								# fmt: off
								__UpperCamelCase			:     str         =      {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "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
								__UpperCamelCase			:     List[Any]         =      [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

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

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)