

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,
)


lowerCAmelCase__						=	{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """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:
	lowerCAmelCase__						=	[
	    """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:
	lowerCAmelCase__						=	["""TFGPT2Tokenizer"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	["""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

	lowerCAmelCase__						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import unittest

from cer import CER


lowerCAmelCase__						=	CER()

class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["White House"]
							A__            =     ["white house"]
							# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.18_1818_1818    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["were wolf"]
							A__            =     ["werewolf"]
							# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.111_1111    ) < 1e-6    )

							A__            =     ["werewolf"]
							A__            =     ["weae     wolf"]
							# S = 1, D = 1, I = 0, N = 8, CER = 0.25
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

							# consecutive whitespaces case 1
							A__            =     ["were wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

							# consecutive whitespaces case 2
							A__            =     ["were   wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["weaewolf"]
							# S = 1, D = 0, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereawolf"]
							# S = 0, D = 1, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereolf"]
							# S = 0, D = 0, I = 1, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["werewolf", "I am your father"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )

							A__            =     ["werewolf", "I am your father", "doge"]
							A__            =     ["werxwolf", "I       am your father", "doge"]
							# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0357_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["My hovercraft", "is full of eels"]
							A__            =     ["My hovercraft is full", " of eels"]
							# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
							# whitespace at the front of " of eels" will be strip during preporcessing
							# so need to insert 2 whitespaces
							A__            =     cer.compute(predictions=lowercase , references=lowercase , concatenate_texts=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.07_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     [" 能吞虾玻璃而 不霜身体啦"]
							# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.45_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃", "而不伤身体"]
							A__            =     ["我    能 吞 下 玻 璃", "而不伤身体"]
							# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.4545_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertFalse(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     [""]
							A__            =     ["Hypothesis"]
							with self.assertRaises(lowercase    ):
											cer.compute(predictions=lowercase , references=lowercase    )


if __name__ == "__main__":
	unittest.main()

