



"""simple docstring"""
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


SCREAMING_SNAKE_CASE_				= """sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE_				= """patrickvonplaten/t5-tiny-random"""




@require_torch
class 			snake_case_						(						unittest.TestCase				):






							@cached_property
							def snake_case_  (   self      ):
									return AutoConfig.from_pretrained(a_      )






							def snake_case_  (   self      ):
									a_						,				*a_     :      Any 						=     create_student_by_copying_alternating_layers(a_			,     tempfile.mkdtemp()			,     e=1			,     d=1      )
									self.assertEqual(student.config.num_hidden_layers			,     1      )






							def snake_case_  (   self      ):
									a_						,				*a_     :      Tuple 						=     create_student_by_copying_alternating_layers(a_			,     tempfile.mkdtemp()			,     e=1			,     d=a_      )






							def snake_case_  (   self      ):
									a_						,				*a_     :      Any 						=     create_student_by_copying_alternating_layers(a_			,     tempfile.mkdtemp()			,     e=1			,     d=a_      )
									self.assertEqual(student.config.encoder_layers			,     1      )
									self.assertEqual(student.config.decoder_layers			,     self.teacher_config.encoder_layers      )






							def snake_case_  (   self      ):
									a_						,				*a_     :      List[Any] 						=     create_student_by_copying_alternating_layers(a_			,     tempfile.mkdtemp()			,     e=1			,     d=1      )
									self.assertEqual(student.config.encoder_layers			,     1      )
									self.assertEqual(student.config.decoder_layers			,     1      )






							def snake_case_  (   self      ):
									with self.assertRaises(a_      ):
											create_student_by_copying_alternating_layers(a_			,     tempfile.mkdtemp()			,     e=a_			,     d=a_      )




"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

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