

'''simple docstring'''





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					['image_processor', 'tokenizer']
		SCREAMING_SNAKE_CASE_           =					'AutoImageProcessor'
		SCREAMING_SNAKE_CASE_           =					'AutoTokenizer'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			**SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''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.'   ,			SCREAMING_SNAKE_CASE_   ,			)
												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__(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      self.image_processor
							lowerCamelCase_  				=      False
		def __call__(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							if self._in_target_context_manager:
												return self.current_processor(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      kwargs.pop('images'   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      kwargs.pop('text'   ,			SCREAMING_SNAKE_CASE_		)
							if len(SCREAMING_SNAKE_CASE_		) > 0:
												lowerCamelCase_  				=      args[0]
												lowerCamelCase_  				=      args[1:]

							if images is None and text is None:
												raise ValueError('You need to specify either an `images` or `text` input to process.'		)

							if images is not None:
												lowerCamelCase_  				=      self.image_processor(SCREAMING_SNAKE_CASE_   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
							if text is not None:
												lowerCamelCase_  				=      self.tokenizer(SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												lowerCamelCase_  				=      encodings['input_ids']
												return inputs
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
		@contextmanager
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your images inputs, or in a separate call.'		)
							lowerCamelCase_  				=      True
							lowerCamelCase_  				=      self.tokenizer
							yield
							lowerCamelCase_  				=      self.image_processor
							lowerCamelCase_  				=      False
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=None		)  ->     Tuple:






							'''simple docstring'''



							if added_vocab is None:
												lowerCamelCase_  				=      self.tokenizer.get_added_vocab()

							lowerCamelCase_  				=      {}

							while tokens:
												lowerCamelCase_  				=      re.search(r'<s_(.*?)>'   ,			SCREAMING_SNAKE_CASE_   ,			re.IGNORECASE		)
												if start_token is None:
																	break
												lowerCamelCase_  				=      start_token.group(1		)
												lowerCamelCase_  				=      re.search(rf'''</s_{key}>'''   ,			SCREAMING_SNAKE_CASE_   ,			re.IGNORECASE		)
												lowerCamelCase_  				=      start_token.group()
												if end_token is None:
																	lowerCamelCase_  				=      tokens.replace(SCREAMING_SNAKE_CASE_   ,			''		)
												else:
																	lowerCamelCase_  				=      end_token.group()
																	lowerCamelCase_  				=      re.escape(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      re.escape(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''   ,			SCREAMING_SNAKE_CASE_   ,			re.IGNORECASE		)
																	if content is not None:
																						lowerCamelCase_  				=      content.group(1		).strip()
																						if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											lowerCamelCase_  				=      self.tokenajson(SCREAMING_SNAKE_CASE_   ,			is_inner_value=SCREAMING_SNAKE_CASE_   ,			added_vocab=SCREAMING_SNAKE_CASE_		)
																											if value:
																																if len(SCREAMING_SNAKE_CASE_		) == 1:
																																					lowerCamelCase_  				=      value[0]
																																lowerCamelCase_  				=      value
																						else:  # leaf nodes
																											lowerCamelCase_  				=      []
																											for leaf in content.split(r'<sep/>'		):
																																lowerCamelCase_  				=      leaf.strip()
																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																					lowerCamelCase_  				=      leaf[1:-2]  # for categorical special tokens
																																output[key].append(SCREAMING_SNAKE_CASE_		)
																											if len(output[key]		) == 1:
																																lowerCamelCase_  				=      output[key][0]

																	lowerCamelCase_  				=      tokens[tokens.find(SCREAMING_SNAKE_CASE_		) + len(SCREAMING_SNAKE_CASE_		) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																						return [output] + self.tokenajson(tokens[6:]   ,			is_inner_value=SCREAMING_SNAKE_CASE_   ,			added_vocab=SCREAMING_SNAKE_CASE_		)

							if len(SCREAMING_SNAKE_CASE_		):
												return [output] if is_inner_value else output
							else:
												return [] if is_inner_value else {"text_sequence": tokens}
		@property
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



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






		@property
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



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










"""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


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






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return AutoConfig.from_pretrained(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[str]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.num_hidden_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[Any]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					self.teacher_config.encoder_layers )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

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