






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  List[str]            =						get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCAmelCase							:  int            =						get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowerCAmelCase							:  List[Any]            =						'''pt''' if is_torch_available() else '''tf'''



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




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					CamembertTokenizer
			_UpperCAmelCase       :							Tuple      							=					CamembertTokenizerFast
			_UpperCAmelCase       :							List[Any]      							=					True
			_UpperCAmelCase       :							List[Any]      							=					True



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_:			Tuple  		=	CamembertTokenizer(__lowerCamelCase)
									tokenizer.save_pretrained(self.tmpdirname)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"""<pad>"""
									SCREAMING_SNAKE_CASE_:			int  		=	1

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Dict  		=	list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]       ,						"<s>NOTUSED")
									self.assertEqual(vocab_keys[1]       ,						"<pad>")
									self.assertEqual(vocab_keys[-1]       ,						"<mask>")
									self.assertEqual(len(__lowerCamelCase)       ,						1004)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									self.assertEqual(self.get_tokenizer().vocab_size       ,						1005)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	CamembertTokenizer(__lowerCamelCase)
									tokenizer.save_pretrained(self.tmpdirname)
									SCREAMING_SNAKE_CASE_:			Dict  		=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

									SCREAMING_SNAKE_CASE_:			int  		=	"""I was born in 92000, and this is falsé."""

									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.encode(__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	rust_tokenizer.encode(__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)

									SCREAMING_SNAKE_CASE_:			Any  		=	tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Any  		=	rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)

									# <unk> tokens are not the same for `rust` than for `slow`.
									# Because spm gives back raw token instead of `unk` in EncodeAsPieces
									# tokens = tokenizer.tokenize(sequence)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.convert_ids_to_tokens(__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Any  		=	rust_tokenizer.tokenize(__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									if not self.test_rust_tokenizer:
															return

									SCREAMING_SNAKE_CASE_:			Dict  		=	self.get_tokenizer()
									SCREAMING_SNAKE_CASE_:			str  		=	self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_:			Any  		=	"""I was born in 92000, and this is falsé."""

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.tokenize(__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Dict  		=	rust_tokenizer.tokenize(__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)

									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.encode(__lowerCamelCase)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	rust_tokenizer.encode(__lowerCamelCase)
									self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase)




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# fmt: off
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# camembert is a french model. So we also use french texts.
									SCREAMING_SNAKE_CASE_:			int  		=	[
									    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
									    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
									    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
									    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
									    """telles que la traduction et la synthèse de texte.""",
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=__lowerCamelCase       ,						model_name="camembert-base"       ,						revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"       ,						sequences=__lowerCamelCase       ,						)









# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase							:  List[str]            =						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def       A_				(   _UpperCAmelCase=None   ):
						if subparsers is not None:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	subparsers.add_parser("tpu-config"    ,     description=_description   )
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	argparse.ArgumentParser("Accelerate tpu-config command"    ,     description=_description   )
						# Core arguments
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.add_argument_group(
						    "Config Arguments"    ,     "Arguments that can be configured through `accelerate config`."   )
						config_args.add_argument(
						    "--config_file"    ,     type=_UpperCAmelCase    ,     default=_UpperCAmelCase    ,     help="Path to the config file to use for accelerate."    ,     )
						config_args.add_argument(
						    "--tpu_name"    ,     default=_UpperCAmelCase    ,     help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."    ,     )
						config_args.add_argument(
						    "--tpu_zone"    ,     default=_UpperCAmelCase    ,     help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."    ,     )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.add_argument_group("TPU Arguments"    ,     "Arguments for options ran inside the TPU."   )
						pod_args.add_argument(
						    "--use_alpha"    ,     action="store_true"    ,     help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."    ,     )
						pod_args.add_argument(
						    "--command_file"    ,     default=_UpperCAmelCase    ,     help="The path to the file containing the commands to run on the pod on startup."    ,     )
						pod_args.add_argument(
						    "--command"    ,     action="append"    ,     nargs="+"    ,     help="A command to run on the pod. Can be passed multiple times."    ,     )
						pod_args.add_argument(
						    "--install_accelerate"    ,     action="store_true"    ,     help="Whether to install accelerate on the pod. Defaults to False."    ,     )
						pod_args.add_argument(
						    "--accelerate_version"    ,     default="latest"    ,     help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."    ,     )
						pod_args.add_argument(
						    "--debug"    ,     action="store_true"    ,     help="If set, will print the command that would be run instead of running it."   )

						if subparsers is not None:
												parser.set_defaults(func=_UpperCAmelCase   )
						return parser







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_config_from_file(args.config_file   )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		SCREAMING_SNAKE_CASE_:			Any  		=	defaults.command_file
												if not args.command and defaults.commands is not None:
																		SCREAMING_SNAKE_CASE_:			int  		=	defaults.commands
												if not args.tpu_name:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	defaults.tpu_name
												if not args.tpu_zone:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	defaults.tpu_zone
						if args.accelerate_version == "dev":
												SCREAMING_SNAKE_CASE_:			Tuple  		=	"git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												SCREAMING_SNAKE_CASE_:			str  		=	"accelerate -U"
						elif isinstance(parse(args.accelerate_version   )    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f"accelerate=={args.accelerate_version}"

						if not args.command_file and not args.command:
												raise ValueError("You must specify either a command file or a command to run on the pod."   )

						if args.command_file:
												with open(args.command_file    ,     "r"   ) as f:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	[f.read().splitlines()]

    # To turn list of lists into list of strings
						if isinstance(args.command[0]    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[line for cmd in args.command for line in cmd]
						# Default to the shared folder and install accelerate
						SCREAMING_SNAKE_CASE_:			Dict  		=	["cd /usr/share"]
						if args.install_accelerate:
												new_cmd += [f"pip install {args.accelerate_version}"]
						new_cmd += args.command
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"; ".join(_UpperCAmelCase   )

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						SCREAMING_SNAKE_CASE_:			int  		=	["gcloud"]
						if args.use_alpha:
												cmd += ["alpha"]
						cmd += [
						    "compute",
						    "tpus",
						    "tpu-vm",
						    "ssh",
						    args.tpu_name,
						    "--zone",
						    args.tpu_zone,
						    "--command",
						    args.command,
						    "--worker",
						    "all",
						]
						if args.debug:
												print(f"Running {' '.join(_UpperCAmelCase   )}"   )
												return
						subprocess.run(_UpperCAmelCase   )
						print("Successfully setup pod."   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	tpu_command_parser()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	parser.parse_args()

						tpu_command_launcher(_UpperCAmelCase   )






