






'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec








'''simple docstring'''

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A:   Dict						=					get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	ErnieMTokenizer
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	True
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	False
	def 							lowerCamelCase__		(				self							)    ->	Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_   : List[Any]   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)
							tokenizer.save_pretrained(self.tmpdirname							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      'this is a test'
							lowercase_   : Dict   	=      'this is a test'
							return input_text, output_text
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : str   	=      '<pad>'
							lowercase_   : Optional[Any]   	=      0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)	,   _lowercase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : List[str]   	=      list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]	,   '<pad>'							)
							self.assertEqual(vocab_keys[1]	,   '<unk>'							)
							self.assertEqual(vocab_keys[-1]	,   '▁eloquent'							)
							self.assertEqual(len(_lowercase							)	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							self.assertEqual(self.get_tokenizer().vocab_size	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							if not self.test_rust_tokenizer:
													return

							lowercase_   : Optional[int]   	=      self.get_tokenizer()
							lowercase_   : Optional[Any]   	=      self.get_rust_tokenizer()

							lowercase_   : Tuple   	=      'I was born in 92000, and this is falsé.'

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize(_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Union[str, Any]   	=      self.get_rust_tokenizer()
							lowercase_   : Optional[Any]   	=      tokenizer.encode(_lowercase							)
							lowercase_   : str   	=      rust_tokenizer.encode(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Optional[int]   	=      ErnieMTokenizer(_lowercase	,   do_lower_case=_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : Tuple   	=      tokenizer.tokenize('This is a test'							)
							self.assertListEqual(_lowercase	,   ['▁this', '▁is', '▁a', '▁test']							)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)	,   [48, 25, 21, 1289]							)

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
							# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']							)
							lowercase_   : int   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
							self.assertListEqual(_lowercase	,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

							lowercase_   : Optional[int]   	=      tokenizer.convert_ids_to_tokens(_lowercase							)
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,   )
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : List[Any]   	=      tokenizer.encode('sequence builders'							)
							lowercase_   : Optional[int]   	=      tokenizer.encode('multi-sequence build'							)

							lowercase_   : Union[str, Any]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : Optional[int]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase	,   _lowercase							)

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
							    tokenizer.sep_token_id
							] + text_a + [tokenizer.sep_token_id]







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							# fmt: off
							lowercase_   : Dict   	=      {'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]]}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_lowercase	,   model_name='susnato/ernie-m-base_pytorch'	,   sequences=[
							        'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
							        'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
							        'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
							        'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
							        'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
							        'conditioning on both left and right context in all layers.',
							        'The quick brown fox jumps over the lazy dog.',
							    ]	,   )
