






'''simple docstring'''

import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
A:   List[str]						=					"stas/tiny-wmt19-en-ru"






class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	FSMTTokenizer
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowercase_   : Optional[Any]   	=      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							lowercase_   : Any   	=      dict(zip(_lowercase	,   range(len(_lowercase							)							)							)							)
							lowercase_   : Dict   	=      ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							lowercase_   : Dict   	=      ['en', 'ru']
							lowercase_   : List[Any]   	=      {
							    'langs': self.langs,
							    'src_vocab_size': 10,
							    'tgt_vocab_size': 20,
							}

							lowercase_   : Tuple   	=      os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['src_vocab_file']							)
							lowercase_   : Tuple   	=      os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['tgt_vocab_file']							)
							lowercase_   : Any   	=      os.path.join(self.tmpdirname	,   'tokenizer_config.json'							)
							lowercase_   : Union[str, Any]   	=      os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['merges_file']							)
							with open(self.src_vocab_file	,   'w'							) as fp:
													fp.write(json.dumps(_lowercase							)							)
							with open(self.tgt_vocab_file	,   'w'							) as fp:
													fp.write(json.dumps(_lowercase							)							)
							with open(self.merges_file	,   'w'							) as fp:
													fp.write('\n'.join(_lowercase							)							)
							with open(_lowercase	,   'w'							) as fp:
													fp.write(json.dumps(_lowercase							)							)
	@cached_property
	def 							lowerCamelCase__		(				self							)    ->	str:
							return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'							)
	@cached_property
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'							)
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : str   	=      FSMTTokenizer.from_pretrained(_lowercase							)
							self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]	,   ['en', 'ru']							)
							self.assertEqual(tokenizer.src_vocab_size	,   21							)
							self.assertEqual(tokenizer.tgt_vocab_size	,   21							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							lowercase_   : Optional[int]   	=      FSMTTokenizer(self.langs	,   self.src_vocab_file	,   self.tgt_vocab_file	,   self.merges_file							)

							lowercase_   : List[str]   	=      'lower'
							lowercase_   : int   	=      ['low', 'er</w>']
							lowercase_   : str   	=      tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokens + ['<unk>']
							lowercase_   : Tuple   	=      [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)	,   _lowercase							)
	@slow
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : Optional[Any]   	=      self.tokenizer_ru_en

							lowercase_   : Optional[int]   	=      tokenizer.encode('sequence builders'	,   add_special_tokens=_lowercase							)
							lowercase_   : List[str]   	=      tokenizer.encode('multi-sequence build'	,   add_special_tokens=_lowercase							)

							lowercase_   : Any   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : Any   	=      tokenizer.build_inputs_with_special_tokens(_lowercase	,   _lowercase							)

							assert encoded_sentence == text + [2]
							assert encoded_pair == text + [2] + text_a + [2]
	@slow
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							lowercase_   : Any   	=      self.tokenizer_en_ru
							lowercase_   : Any   	=      self.tokenizer_ru_en

							lowercase_   : Tuple   	=      [
							    [
							        'Here\'s a little song I wrote. Don\'t worry, be happy.',
							        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
							    ],
							    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
							]

							# if data needs to be recreated or added, run:
							# import torch
							# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
							# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

							for src_text, tgt_input_ids in targets:
													lowercase_   : Union[str, Any]   	=      tokenizer_enc.encode(_lowercase	,   return_tensors=_lowercase							)
													self.assertListEqual(_lowercase	,   _lowercase							)

													# and decode backward, using the reversed languages model
													lowercase_   : Optional[Any]   	=      tokenizer_dec.decode(_lowercase	,   skip_special_tokens=_lowercase							)
													self.assertEqual(_lowercase	,   _lowercase							)
	@slow
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							lowercase_   : Any   	=      FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'	,   do_lower_case=_lowercase							)
							lowercase_   : Optional[Any]   	=      tokenizer.tokenize('USA is United States of America'							)
							lowercase_   : List[str]   	=      ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
							self.assertListEqual(_lowercase	,   _lowercase							)
	@unittest.skip('FSMTConfig.__init__  requires non-optional args'							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							pass







	@unittest.skip('FSMTConfig.__init__  requires non-optional args'							)
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							pass








'''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
