


"""simple docstring"""


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE      =      {'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      ['T5Tokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      ['T5TokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      [
		    'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'T5EncoderModel',
		    'T5ForConditionalGeneration',
		    'T5Model',
		    'T5PreTrainedModel',
		    'load_tf_weights_in_t5',
		    'T5ForQuestionAnswering',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      [
		    'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFT5EncoderModel',
		    'TFT5ForConditionalGeneration',
		    'TFT5Model',
		    'TFT5PreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      [
		    'FlaxT5EncoderModel',
		    'FlaxT5ForConditionalGeneration',
		    'FlaxT5Model',
		    'FlaxT5PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ta import TaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ta_fast import TaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ta import (
				    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TaEncoderModel,
				    TaForConditionalGeneration,
				    TaForQuestionAnswering,
				    TaModel,
				    TaPreTrainedModel,
				    load_tf_weights_in_ta,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_ta import (
				    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTaEncoderModel,
				    TFTaForConditionalGeneration,
				    TFTaModel,
				    TFTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_ta import (
				    FlaxTaEncoderModel,
				    FlaxTaForConditionalGeneration,
				    FlaxTaModel,
				    FlaxTaPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   1      )

											self.assertIn(
											    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
											    ' which is not implemented in the fast tokenizers.'    ,   str(w[0].message      )    ,   )