


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase					:			List[Any]				     =					{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			List[str]				     =					["""GPT2TokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			str				     =					[
	    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPT2DoubleHeadsModel""",
	    """GPT2ForQuestionAnswering""",
	    """GPT2ForSequenceClassification""",
	    """GPT2ForTokenClassification""",
	    """GPT2LMHeadModel""",
	    """GPT2Model""",
	    """GPT2PreTrainedModel""",
	    """load_tf_weights_in_gpt2""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			List[Any]				     =					[
	    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGPT2DoubleHeadsModel""",
	    """TFGPT2ForSequenceClassification""",
	    """TFGPT2LMHeadModel""",
	    """TFGPT2MainLayer""",
	    """TFGPT2Model""",
	    """TFGPT2PreTrainedModel""",
	]

try:
	if not is_keras_nlp_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			Union[str, Any]				     =					["""TFGPT2Tokenizer"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			List[str]				     =					["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
	from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
	from .tokenization_gpta import GPTaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_fast import GPTaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpta import (
		    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTaDoubleHeadsModel,
		    GPTaForQuestionAnswering,
		    GPTaForSequenceClassification,
		    GPTaForTokenClassification,
		    GPTaLMHeadModel,
		    GPTaModel,
		    GPTaPreTrainedModel,
		    load_tf_weights_in_gpta,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gpta import (
		    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGPTaDoubleHeadsModel,
		    TFGPTaForSequenceClassification,
		    TFGPTaLMHeadModel,
		    TFGPTaMainLayer,
		    TFGPTaModel,
		    TFGPTaPreTrainedModel,
		)

	try:
		if not is_keras_nlp_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_tf import TFGPTaTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
	import sys

	_UpperCAmelCase					:			Optional[int]				     =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class    a__					(			__A				,    unittest.TestCase   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						SpeechaTextaTokenizer
				__UpperCamelCase     :		str						=						False




				def        _snake_case	(self		):
										super().setUp()

										__lowerCAmelCase											=					'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''		)
										__lowerCAmelCase											=					['''he re</w> 123''', '''here a 1456''']
										__lowerCAmelCase											=					dict(zip(__lowercase							,      range(len(__lowercase		)		)		)		)

										__lowerCAmelCase											=					{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__lowerCAmelCase											=					tempfile.mkdtemp()
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''merges_file''']		)

										with open(self.vocab_file							,      '''w'''							,      encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)

										with open(self.merges_file							,      '''w'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]							,      '''<s>'''		)
										self.assertEqual(vocab_keys[1]							,      '''<pad>'''		)
										self.assertEqual(vocab_keys[-1]							,      '''vocab'''		)
										self.assertEqual(len(__lowercase		)							,      14		)




				def        _snake_case	(self		):
										self.assertEqual(self.get_tokenizer().vocab_size							,      14		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										# make sure @@ is correctly concatenated
										__lowerCAmelCase											=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__lowerCAmelCase											=					tokenizer.decode(__lowercase		)

										self.assertTrue(output_string == '''herecouple words ofthe'''		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(__lowercase		)
																os.remove(os.path.join(__lowercase							,      '''merges.txt'''		)		)

																# load tokenizer without merges file should not throw an error
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(__lowercase		)
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										self.assertIsNotNone(__lowercase		)




				def        _snake_case	(self		):
										if not self.test_slow_tokenizer:
																return

										__lowerCAmelCase											=					inspect.signature(self.tokenizer_class.__init__		)
										__lowerCAmelCase											=					self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(__lowercase							,      tokenizer.init_kwargs		)

