






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


a						:			List[Any]     	=					"sshleifer/bart-tiny-random"
a						:			Any     	=					"patrickvonplaten/t5-tiny-random"
@require_torch
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @cached_property
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[Any]:
        return AutoConfig.from_pretrained(__lowercase   )
      def       UpperCAmelCase ( self   :     str   )				-> List[str]:
        __UpperCAmelCase ,					*__UpperCAmelCase				:      Union[str, Any]	 =    create_student_by_copying_alternating_layers(__lowercase						, tempfile.mkdtemp()						, e=1						, d=1   )
        self.assertEqual(student.config.num_hidden_layers						, 1   )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> str:
        __UpperCAmelCase ,					*__UpperCAmelCase				:      int	 =    create_student_by_copying_alternating_layers(__lowercase						, tempfile.mkdtemp()						, e=1						, d=__lowercase   )
      def       UpperCAmelCase ( self   :     str   )				-> Union[str, Any]:
        __UpperCAmelCase ,					*__UpperCAmelCase				:      List[Any]	 =    create_student_by_copying_alternating_layers(__lowercase						, tempfile.mkdtemp()						, e=1						, d=__lowercase   )
        self.assertEqual(student.config.encoder_layers						, 1   )
        self.assertEqual(student.config.decoder_layers						, self.teacher_config.encoder_layers   )
      def       UpperCAmelCase ( self   :     Dict   )				-> str:
        __UpperCAmelCase ,					*__UpperCAmelCase				:      Dict	 =    create_student_by_copying_alternating_layers(__lowercase						, tempfile.mkdtemp()						, e=1						, d=1   )
        self.assertEqual(student.config.encoder_layers						, 1   )
        self.assertEqual(student.config.decoder_layers						, 1   )




      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> List[Any]:
        with self.assertRaises(__lowercase   ):
          create_student_by_copying_alternating_layers(__lowercase						, tempfile.mkdtemp()						, e=__lowercase						, d=__lowercase   )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
