
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A				='''▁'''

__A				=get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = BertGenerationTokenizer
	lowerCAmelCase__         = False
	lowerCAmelCase__         = True



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					super().setUp()

					lowerCamelCase_						=			BertGenerationTokenizer(lowercase						,  keep_accents=lowercase )
					tokenizer.save_pretrained(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			"<s>"
					lowerCamelCase_						=			1

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			list(self.get_tokenizer().get_vocab().keys() )

					self.assertEqual(vocab_keys[0]						,  "<unk>" )
					self.assertEqual(vocab_keys[1]						,  "<s>" )
					self.assertEqual(vocab_keys[-1]						,  "<pad>" )
					self.assertEqual(len(lowercase )						,  1002 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.assertEqual(self.get_tokenizer().vocab_size						,  1000 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			BertGenerationTokenizer(lowercase						,  keep_accents=lowercase )

					lowerCamelCase_						=			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]						,  )

					lowerCamelCase_						=			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",
					        ".",
					    ]						,  )
					lowerCamelCase_						=			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]						,  )

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			"Hello World!"
					lowerCamelCase_						=			[18536, 2260, 101]

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



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			(
					    "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"
					)
					lowerCamelCase_						=			[
					    871,
					    419,
					    358,
					    946,
					    991,
					    2521,
					    452,
					    358,
					    1357,
					    387,
					    7751,
					    3536,
					    112,
					    985,
					    456,
					    126,
					    865,
					    938,
					    5400,
					    5734,
					    458,
					    1368,
					    467,
					    786,
					    2462,
					    5246,
					    1159,
					    633,
					    865,
					    4519,
					    457,
					    582,
					    852,
					    2557,
					    427,
					    916,
					    508,
					    405,
					    34324,
					    497,
					    391,
					    408,
					    11342,
					    1244,
					    385,
					    100,
					    938,
					    985,
					    456,
					    574,
					    362,
					    12597,
					    3200,
					    3129,
					    1172,
					]

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



	@require_torch
	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					import torch

					from transformers import BertGenerationConfig, BertGenerationEncoder

					# Build sequence
					lowerCamelCase_						=			list(self.big_tokenizer.get_vocab().keys() )[:10]
					lowerCamelCase_						=			" ".join(lowercase )
					lowerCamelCase_						=			self.big_tokenizer.encode_plus(lowercase						,  return_tensors="pt"						,  return_token_type_ids=lowercase )
					lowerCamelCase_						=			self.big_tokenizer.batch_encode_plus(
					    [sequence + " " + sequence]						,  return_tensors="pt"						,  return_token_type_ids=lowercase )

					lowerCamelCase_						=			BertGenerationConfig()
					lowerCamelCase_						=			BertGenerationEncoder(lowercase )

					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					# fmt: off
					lowerCamelCase_						=			{"input_ids": [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowercase						,  model_name="google/bert_for_seq_generation_L-24_bbc_encoder"						,  revision="c817d1fd1be2ffa69431227a1fe320544943d4db"						,  )




import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				):
				# Get the sagemaker specific mp parameters from smp_options variable.
				lowerCamelCase_						=			os.getenv("SM_HP_MP_PARAMETERS"	, "{}"    )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				lowerCamelCase_						=			os.getenv("SM_FRAMEWORK_PARAMS"	, "{}"    )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if not mpi_options.get("sagemaker_mpi_enabled"	, lowerCamelCase__    ):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec("smdistributed"    ) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()





@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = field(
	    default=''			,       metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,       )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					super().__post_init__()
					warnings.warn(
					    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
					    "`TrainingArguments` instead."						,  lowercase						,  )



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       "torch.device":
					logger.info("PyTorch: setting up devices" )
					if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
									logger.warning(
									    "torch.distributed process group is initialized, but local_rank == -1. "
									    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
					if self.no_cuda:
									lowerCamelCase_						=			torch.device("cpu" )
									lowerCamelCase_						=			0
					elif is_sagemaker_model_parallel_available():
									lowerCamelCase_						=			smp.local_rank()
									lowerCamelCase_						=			torch.device("cuda"						,  lowercase )
									lowerCamelCase_						=			1
					elif is_sagemaker_dp_enabled():
									import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

									torch.distributed.init_process_group(backend="smddp"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1
					elif self.local_rank == -1:
									# if n_gpu is > 1 we'll use nn.DataParallel.
									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
									# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
									# trigger an error that a device index is missing. Index 0 takes into account the
									# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
									# will use the first GPU in that env, i.e. GPU#1
									lowerCamelCase_						=			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
									# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
									# the default value.
									lowerCamelCase_						=			torch.cuda.device_count()
					else:
									# Here, we'll use torch.distributed.
									# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
									if not torch.distributed.is_initialized():
													torch.distributed.init_process_group(backend="nccl"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1

					if device.type == "cuda":
									torch.cuda.set_device(lowercase )

					return device



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return not is_sagemaker_model_parallel_available()



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return False


