






from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]					)		->			Tuple:






	"""simple docstring"""



	return {key.lstrip("""-"""					): value for key, value in zip(unknown_args[::2]	,			unknown_args[1::2]					)}

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	ArgumentParser(
	    """HuggingFace Datasets CLI tool"""	,			usage="""datasets-cli <command> [<args>]"""	,			allow_abbrev=__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	parser.add_subparsers(help="""datasets-cli command helpers"""					)
	set_verbosity_info()

	# Register commands
	ConvertCommand.register_subcommand(__magic_name__					)
	EnvironmentCommand.register_subcommand(__magic_name__					)
	TestCommand.register_subcommand(__magic_name__					)
	RunBeamCommand.register_subcommand(__magic_name__					)
	DummyDataCommand.register_subcommand(__magic_name__					)

	# Parse args
	UpperCamelCase     ,      UpperCamelCase     :List[str]          =	parser.parse_known_args()
	if not hasattr(__magic_name__	,			"""func"""					):
		parser.print_help()
		exit(1					)
	UpperCamelCase     :Tuple          =	parse_unknown_args(__magic_name__					)

	# Run
	UpperCamelCase     :str          =	args.func(__magic_name__	,			**__magic_name__					)
	service.run()


if __name__ == "__main__":
	main()








import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_tensorflow_text_available():
	from transformers.models.bert import TFBertTokenizer


UpperCAmelCase_ :						Optional[Any]      =      ['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase_ :						List[str]      =      '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

	class     _SCREAMING_SNAKE_CASE       (			tf.keras.Model      ):





		def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any]	):
			super().__init__()
			UpperCamelCase     :Any          =	tokenizer
			UpperCamelCase     :List[str]          =	AutoConfig.from_pretrained(__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	TFAutoModel.from_config(__lowerCamelCase	)





		def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str	):
			UpperCamelCase     :str          =	self.tokenizer(__lowerCamelCase	)
			UpperCamelCase     :Any          =	self.bert(**__lowerCamelCase	)
			return out["pooler_output"]

@require_tf
@require_tensorflow_text
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					Dict	):
		super().setUp()

		UpperCamelCase     :int          =	[
		    BertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
		]  # repeat for when fast_bert_tokenizer=false
		UpperCamelCase     :Any          =	[TFBertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in TOKENIZER_CHECKPOINTS] + [
		    TFBertTokenizer.from_pretrained(__lowerCamelCase ,  use_fast_bert_tokenizer=__lowerCamelCase	)
		    for checkpoint in TOKENIZER_CHECKPOINTS
		]
		assert len(self.tokenizers	) == len(self.tf_tokenizers	)

		UpperCamelCase     :Any          =	[
		    """This is a straightforward English test sentence.""",
		    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
		    """Now we're going to add some Chinese: 一 二 三 一二三""",
		    """And some much more rare Chinese: 齉 堃 齉堃""",
		    """Je vais aussi écrire en français pour tester les accents""",
		    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
		]
		UpperCamelCase     :Union[str, Any]          =	list(zip(self.test_sentences ,  self.test_sentences[::-1]	)	)





	def 						_A (		self				:					Optional[int]	):
		for tokenizer, tf_tokenizer in zip(self.tokenizers ,  self.tf_tokenizers	):
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tokenizer(__lowerCamelCase ,  return_tensors="""tf""" ,  padding="""longest"""	)
				UpperCamelCase     :str          =	tf_tokenizer(__lowerCamelCase	)

				for key in python_outputs.keys():
					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape	)	)
					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,  tf.intaa	) == tf_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Dict	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :str          =	tf_tokenizer(self.paired_sentences	)
			UpperCamelCase     :Any          =	tf_tokenizer(
			    text=[sentence[0] for sentence in self.paired_sentences] ,  text_pair=[sentence[1] for sentence in self.paired_sentences] ,  )
			for key in merged_outputs.keys():
				self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,  tf.intaa	) == separated_outputs[key]	)	)





	@slow
	def 						_A (		self				:					List[str]	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[Any]          =	tf.function(__lowerCamelCase	)
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tf.constant(__lowerCamelCase	)
				UpperCamelCase     :List[str]          =	compiled_tokenizer(__lowerCamelCase	)
				UpperCamelCase     :Optional[Any]          =	tf_tokenizer(__lowerCamelCase	)

				for key in eager_outputs.keys():
					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[str]          =	ModelToSave(tokenizer=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	tf.convert_to_tensor(self.test_sentences	)
			UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)  # Build model with some sample inputs
			with TemporaryDirectory() as tempdir:
				UpperCamelCase     :List[str]          =	Path(__lowerCamelCase	) / """saved.model"""
				model.save(__lowerCamelCase	)
				UpperCamelCase     :List[Any]          =	tf.keras.models.load_model(__lowerCamelCase	)
			UpperCamelCase     :Dict          =	loaded_model(__lowerCamelCase	)
			# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
			self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output	)	) ,  1E-5	)
