




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 							__snake_case	(lowercase_		):
				lowerCAmelCase__      						=  ['''image_processor''', '''tokenizer''']
				lowerCAmelCase__      						=  '''OwlViTImageProcessor'''
				lowerCAmelCase__      						=  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')



				def __init__(   self   :		Optional[Any]		,						_UpperCAmelCase   :		Dict=None		,						_UpperCAmelCase   :		List[str]=None		,						**_UpperCAmelCase   :		List[Any]   )	->     Any:





								'''simple docstring'''




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

								_lowerCAmelCase     :     Tuple              =   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__(_UpperCAmelCase		,						_UpperCAmelCase   )



				def __call__(   self   :		str		,						_UpperCAmelCase   :		str=None		,						_UpperCAmelCase   :		Dict=None		,						_UpperCAmelCase   :		Optional[Any]=None		,						_UpperCAmelCase   :		List[Any]="max_length"		,						_UpperCAmelCase   :		Union[str, Any]="np"		,						**_UpperCAmelCase   :		str   )	->     Optional[Any]:





								'''simple docstring'''




								if text is None and query_images is None and images is None:
												raise ValueError(
												    """You have to specify at least one text or query image or image. All three cannot be none."""   )

								if text is not None:
												if isinstance(_UpperCAmelCase		,						_UpperCAmelCase   ) or (isinstance(_UpperCAmelCase		,						_UpperCAmelCase   ) and not isinstance(text[0]		,						_UpperCAmelCase   )):
																_lowerCAmelCase     :     Dict              =   [self.tokenizer(_UpperCAmelCase		,						padding=_UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase   )]

												elif isinstance(_UpperCAmelCase		,						_UpperCAmelCase   ) and isinstance(text[0]		,						_UpperCAmelCase   ):
																_lowerCAmelCase     :     List[str]              =   []

																# Maximum number of queries across batch
																_lowerCAmelCase     :     int              =   max([len(_UpperCAmelCase   ) for t in text]   )

																# Pad all batch samples to max number of text queries
																for t in text:
																				if len(_UpperCAmelCase   ) != max_num_queries:
																								_lowerCAmelCase     :     Tuple              =   t + [""" """] * (max_num_queries - len(_UpperCAmelCase   ))

																				_lowerCAmelCase     :     Optional[int]              =   self.tokenizer(_UpperCAmelCase		,						padding=_UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase   )
																				encodings.append(_UpperCAmelCase   )
												else:
																raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""   )

												if return_tensors == "np":
																_lowerCAmelCase     :     Tuple              =   np.concatenate([encoding["""input_ids"""] for encoding in encodings]		,						axis=0   )
																_lowerCAmelCase     :     Optional[int]              =   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,						axis=0   )

												elif return_tensors == "jax" and is_flax_available():
																import jax.numpy as jnp

																_lowerCAmelCase     :     int              =   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]		,						axis=0   )
																_lowerCAmelCase     :     Optional[Any]              =   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,						axis=0   )

												elif return_tensors == "pt" and is_torch_available():
																import torch

																_lowerCAmelCase     :     Dict              =   torch.cat([encoding["""input_ids"""] for encoding in encodings]		,						dim=0   )
																_lowerCAmelCase     :     Union[str, Any]              =   torch.cat([encoding["""attention_mask"""] for encoding in encodings]		,						dim=0   )

												elif return_tensors == "tf" and is_tf_available():
																import tensorflow as tf

																_lowerCAmelCase     :     str              =   tf.stack([encoding["""input_ids"""] for encoding in encodings]		,						axis=0   )
																_lowerCAmelCase     :     int              =   tf.stack([encoding["""attention_mask"""] for encoding in encodings]		,						axis=0   )

												else:
																raise ValueError("""Target return tensor type could not be returned"""   )

												_lowerCAmelCase     :     str              =   BatchEncoding()
												_lowerCAmelCase     :     List[Any]              =   input_ids
												_lowerCAmelCase     :     Optional[Any]              =   attention_mask

								if query_images is not None:
												_lowerCAmelCase     :     Tuple              =   BatchEncoding()
												_lowerCAmelCase     :     List[Any]              =   self.image_processor(
												    _UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase   ).pixel_values
												_lowerCAmelCase     :     List[Any]              =   query_pixel_values

								if images is not None:
												_lowerCAmelCase     :     List[str]              =   self.image_processor(_UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase   )

								if text is not None and images is not None:
												_lowerCAmelCase     :     Optional[Any]              =   image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_lowerCAmelCase     :     Optional[int]              =   image_features.pixel_values
												return encoding
								elif text is not None or query_images is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_UpperCAmelCase   )		,						tensor_type=_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		int		,						*_UpperCAmelCase   :		Dict		,						**_UpperCAmelCase   :		Any   )	->     Tuple:





								'''simple docstring'''




								return self.image_processor.post_process(*_UpperCAmelCase		,						**_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Union[str, Any]		,						*_UpperCAmelCase   :		str		,						**_UpperCAmelCase   :		int   )	->     Union[str, Any]:





								'''simple docstring'''




								return self.image_processor.post_process_object_detection(*_UpperCAmelCase		,						**_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						*_UpperCAmelCase   :		str		,						**_UpperCAmelCase   :		Optional[int]   )	->     Optional[int]:





								'''simple docstring'''




								return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase		,						**_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		Any		,						*_UpperCAmelCase   :		List[str]		,						**_UpperCAmelCase   :		List[str]   )	->     List[str]:





								'''simple docstring'''




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



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						*_UpperCAmelCase   :		Any		,						**_UpperCAmelCase   :		List[Any]   )	->     Optional[Any]:





								'''simple docstring'''




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



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[int]   )	->     str:





								'''simple docstring'''




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



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[Any]   )	->     Any:





								'''simple docstring'''




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




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


_UpperCAmelCase						:  Dict									=			"""sshleifer/bart-tiny-random"""
_UpperCAmelCase						:  Optional[Any]									=			"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 					lowercase							(			unittest.TestCase     ):




				@cached_property
				def        a				(   self			):
											return AutoConfig.from_pretrained(snake_case			)




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




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




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




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




				def        a				(   self			):
											with self.assertRaises(snake_case			):
																		create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=snake_case ,  d=snake_case			)




