'''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


__lowercase        =    '''sshleifer/bart-tiny-random'''
__lowercase        =    '''patrickvonplaten/t5-tiny-random'''






@require_torch
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			@cached_property
			def 				a_			(  self):




					"""simple docstring"""


					return AutoConfig.from_pretrained(__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	*lowerCAmelCase     =    create_student_by_copying_alternating_layers(__lowerCAmelCase			,       tempfile.mkdtemp()			,       e=1			,       d=1)
					self.assertEqual(student.config.num_hidden_layers			,       1)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	*lowerCAmelCase     =    create_student_by_copying_alternating_layers(__lowerCAmelCase			,       tempfile.mkdtemp()			,       e=1			,       d=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	*lowerCAmelCase     =    create_student_by_copying_alternating_layers(__lowerCAmelCase			,       tempfile.mkdtemp()			,       e=1			,       d=__lowerCAmelCase)
					self.assertEqual(student.config.encoder_layers			,       1)
					self.assertEqual(student.config.decoder_layers			,       self.teacher_config.encoder_layers)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	*lowerCAmelCase     =    create_student_by_copying_alternating_layers(__lowerCAmelCase			,       tempfile.mkdtemp()			,       e=1			,       d=1)
					self.assertEqual(student.config.encoder_layers			,       1)
					self.assertEqual(student.config.decoder_layers			,       1)







			def 				a_			(  self):




					"""simple docstring"""


					with self.assertRaises(__lowerCAmelCase):
							create_student_by_copying_alternating_layers(__lowerCAmelCase			,       tempfile.mkdtemp()			,       e=__lowerCAmelCase			,       d=__lowerCAmelCase)





'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




