



"""simple docstring"""


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 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""OwlViTImageProcessor"""
			UpperCamelCase__			      =	("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(		self     :    str				,   __UpperCamelCase     :    Any=None				,   __UpperCamelCase     :    Tuple=None				,   **__UpperCamelCase     :    Tuple   )->Tuple:
							_UpperCAmelCase					=    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				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    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     :    Tuple				,   __UpperCamelCase     :    Any=None				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    Optional[int]="max_length"				,   __UpperCamelCase     :    List[Any]="np"				,   **__UpperCamelCase     :    Union[str, Any]   )->Optional[Any]:

							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   )):
															_UpperCAmelCase					=    [self.tokenizer(__UpperCamelCase				,   padding=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )]

											elif isinstance(__UpperCamelCase				,   __UpperCamelCase   ) and isinstance(text[0]				,   __UpperCamelCase   ):
															_UpperCAmelCase					=    []

															# Maximum number of queries across batch
															_UpperCAmelCase					=    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:
																							_UpperCAmelCase					=    t + [''' '''] * (max_num_queries - len(__UpperCamelCase   ))

																			_UpperCAmelCase					=    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":
															_UpperCAmelCase					=    np.concatenate([encoding['''input_ids'''] for encoding in encodings]				,   axis=0   )
															_UpperCAmelCase					=    np.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,   axis=0   )

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

															_UpperCAmelCase					=    jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]				,   axis=0   )
															_UpperCAmelCase					=    jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,   axis=0   )

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

															_UpperCAmelCase					=    torch.cat([encoding['''input_ids'''] for encoding in encodings]				,   dim=0   )
															_UpperCAmelCase					=    torch.cat([encoding['''attention_mask'''] for encoding in encodings]				,   dim=0   )

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

															_UpperCAmelCase					=    tf.stack([encoding['''input_ids'''] for encoding in encodings]				,   axis=0   )
															_UpperCAmelCase					=    tf.stack([encoding['''attention_mask'''] for encoding in encodings]				,   axis=0   )

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

											_UpperCAmelCase					=    BatchEncoding()
											_UpperCAmelCase					=    input_ids
											_UpperCAmelCase					=    attention_mask

							if query_images is not None:
											_UpperCAmelCase					=    BatchEncoding()
											_UpperCAmelCase					=    self.image_processor(
											    __UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   ).pixel_values
											_UpperCAmelCase					=    query_pixel_values

							if images is not None:
											_UpperCAmelCase					=    self.image_processor(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

							if text is not None and images is not None:
											_UpperCAmelCase					=    image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											_UpperCAmelCase					=    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  lowercase__						(		self     :    List[str]				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    Optional[Any]   )->Optional[int]:
							return self.image_processor.post_process(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   *__UpperCamelCase     :    List[str]				,   **__UpperCamelCase     :    Optional[int]   )->Optional[Any]:
							return self.image_processor.post_process_object_detection(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   *__UpperCamelCase     :    List[Any]				,   **__UpperCamelCase     :    Dict   )->str:
							return self.image_processor.post_process_image_guided_detection(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    str				,   *__UpperCamelCase     :    int				,   **__UpperCamelCase     :    Optional[int]   )->List[Any]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   *__UpperCamelCase     :    int				,   **__UpperCamelCase     :    Optional[int]   )->str:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			@property
			def  lowercase__						(		self     :    Any   )->Optional[int]:
							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  lowercase__						(		self     :    Optional[Any]   )->Any:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
															_UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Cant do half precision'''   )
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							# set architectures equal to `None`
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Can\'t do half precision'''   )
			def  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    str   )->Dict:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[int]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   save_to_csv=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )				,   train_memory_csv_file=os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )				,   inference_memory_csv_file=os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )				,   train_time_csv_file=os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )				,   env_info_csv_file=os.path.join(__UpperCamelCase				,   '''env.csv'''   )				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											benchmark.run()
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''env.csv'''   )   ).exists()   )



			def  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
											self.assertTrue(hasattr(__UpperCamelCase				,   '''sequential'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''cumulative'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''current'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''total'''   )   )

							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   log_filename=os.path.join(__UpperCamelCase				,   '''log.txt'''   )				,   log_print=__UpperCamelCase				,   trace_memory_line_by_line=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											_UpperCAmelCase					=    benchmark.run()
											_check_summary_is_not_empty(result.inference_summary   )
											_check_summary_is_not_empty(result.train_summary   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''log.txt'''   )   ).exists()   )



