
"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowercase				 =					logging.get_logger(__name__)






def    _snake_case					(		snake_case__ :  Dict=None						,   snake_case__ :  Optional[int]=None  ):
	return field(default_factory=lambda: default						,   metadata=snake_case__  )







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     list_field(
						    default=[]		,       metadata={
						        '''help''': (
						            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
						            ''' of all available models'''
						        )
						    }		,       )

						_lowerCamelCase:     List[int]   =     list_field(
						    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}     )

						_lowerCamelCase:     List[int]   =     list_field(
						    default=[8, 32, 128, 512]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Benchmark training of model'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Verbose memory tracing'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
						    }		,       )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Trace memory line by line'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Save result to a CSV file'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Save all print statements in a log file'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Whether to print environment information'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': (
						            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
						            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
						            ''' for debugging / testing and on TPU.'''
						        )
						    }		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'inference_time_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'inference_memory_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'train_time_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'train_memory_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'env_info_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'log_{round(time()     )}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
						_lowerCamelCase:     int   =     field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': (
						            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
						            ''' model weights.'''
						        )
						    }		,       )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> List[str]:
							warnings.warn(
							    F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
							    ' are deprecated in general and it is advised to use external Benchmarking libraries '
							    ' to benchmark Transformer models.'	,A_	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							return json.dumps(dataclasses.asdict(self   )	,indent=2   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[str]:
							if len(self.models   ) <= 0:
								raise ValueError(
								    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
								    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'   )
							return self.models




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[Any]:
							if not self.multi_process:
								return False
							elif self.is_tpu:
								logger.info('Multiprocessing is currently not possible on TPU.'   )
								return False
							else:
								return True






'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf




@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				List[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Optional[int]		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				int		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				List[str]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop original_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_torch
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			[torch.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				int		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(
							    lowerCamelCase_      ,						torch.tensor(lowerCamelCase_						)      ,						torch.tensor(lowerCamelCase_						)						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Tuple		=			[[1, 0], [0, 1]]
							with self.assertRaises(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)







@require_vision
@require_tf
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				str		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop reshaped_input_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_tf
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[tf.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						return_tensors="""tf"""      ,						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				List[str]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			[[1, 0], [0, 1]]
							with self.assertRaises(tf.errors.InvalidArgumentError						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
									    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)







@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				int		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				str		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Tuple      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			np.random.randint(0      ,						2      ,						size=(1, 3, 5, 5)						).astype(np.floataa						)
							SCREAMING_SNAKE_CASE :				str		=			[tf.convert_to_tensor(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[torch.tensor(lowerCamelCase_						)]

							SCREAMING_SNAKE_CASE :				Tuple		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							SCREAMING_SNAKE_CASE :				Any		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""pt"""						)

							self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()						)						)



					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				int		=			image_processor(lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Tuple		=			processor(images=lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()

							SCREAMING_SNAKE_CASE :				Dict		=			image_processor(lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()

							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)



