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

from ..utils import logging


__UpperCamelCase	: Optional[int]    		=  logging.get_logger(__name__)
def    A			(					_lowercase=None	,						_lowercase=None		):
						return field(default_factory=lambda: default	,						metadata=_lowercase		)





@dataclass
class       lowercase__		:
						UpperCamelCase_  =	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"""
						        )
						    }			,							)

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

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

						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Use FP16 to accelerate inference."""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Benchmark training of model"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Verbose memory tracing"""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}			,							)
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={
						        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
						    }			,							)
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Trace memory line by line"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Save result to a CSV file"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Save all print statements in a log file"""})
						UpperCamelCase_  =	field(default=UpperCamelCase_			,							metadata={"""help""": """Whether to print environment information"""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							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."""
						        )
						    }			,							)
						UpperCamelCase_  =	field(
						    default=f"inference_time_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving time results to csv."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"inference_memory_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving memory results to csv."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"train_time_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving time results to csv for training."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"train_memory_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"env_info_{round(time())}.csv"			,							metadata={"""help""": """CSV filename used if saving environment information."""}			,							)
						UpperCamelCase_  =	field(
						    default=f"log_{round(time())}.csv"			,							metadata={"""help""": """Log filename used if print statements are saved in log."""}			,							)
						UpperCamelCase_  =	field(default=3			,							metadata={"""help""": """Times an experiment will be run."""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={
						        """help""": (
						            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
						            """ model weights."""
						        )
						    }			,							)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												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.'''					,	UpperCamelCase__					,	)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												return json.dumps(dataclasses.asdict(self		)					,	indent=2		)


						@property
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												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  __A ( self			:       List[Any]		):





												'''simple docstring'''





												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






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

												if max_val is not None and x > max_val:
																		SCREAMING_SNAKE_CASE							:					Dict           =							math.floor(val / multiple		) * multiple

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


						def __init__( self			:       int					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Dict[str, int] = None					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BILINEAR					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Union[int, float] = 1 / 255					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												return rescale(UpperCamelCase__					,	scale=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												return normalize(UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

												if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

												if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																		if len(UpperCamelCase__		) != len(UpperCamelCase__		):
																								raise ValueError(
																								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

																		for idx in range(len(UpperCamelCase__		)		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							torch.nn.functional.interpolate(
																								    logits[idx].unsqueeze(dim=0		)					,	size=target_sizes[idx]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							resized_logits[0].argmax(dim=0		)
																								semantic_segmentation.append(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							logits.argmax(dim=1		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

												return semantic_segmentation





