from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase						=		logging.get_logger(__name__)


if is_vision_available():
	import PIL





class UpperCAmelCase						(					A_  ):


				'''simple docstring'''






				lowerCamelCase_    =					['''pixel_values''']
				def __init__(						self		,	lowercase = True		,	lowercase = None		,	lowercase = PILImageResampling.BICUBIC		,	lowercase = True		,	lowercase = None		,	lowercase = True		,	lowercase = 1 / 2_5_5		,	lowercase = True		,	lowercase = None		,	lowercase = None		,	lowercase = True		,	**lowercase		,	):



						"""simple docstring"""





						super().__init__(**lowercase					)
						A_		:				Union[str, Any]     =      size if size is not None else {'shortest_edge': 2_2_4}
						A_		:				List[Any]     =      get_size_dict(lowercase		,	default_to_square=lowercase					)
						A_		:				Tuple     =      crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:				Optional[int]     =      get_size_dict(lowercase		,	default_to_square=lowercase		,	param_name='crop_size'					)

						A_		:				List[str]     =      do_resize
						A_		:				List[str]     =      size
						A_		:				Dict     =      resample
						A_		:				List[Any]     =      do_center_crop
						A_		:				Tuple     =      crop_size
						A_		:				Optional[int]     =      do_rescale
						A_		:				List[str]     =      rescale_factor
						A_		:				List[str]     =      do_normalize
						A_		:				Dict     =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						A_		:				List[Any]     =      image_std if image_std is not None else OPENAI_CLIP_STD
						A_		:				Optional[int]     =      do_convert_rgb
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase = PILImageResampling.BICUBIC		,	lowercase = None		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				str     =      get_size_dict(lowercase		,	default_to_square=lowercase					)
						if "shortest_edge" not in size:
								raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
						A_		:				List[Any]     =      get_resize_output_image_size(lowercase		,	size=size['shortest_edge']		,	default_to_square=lowercase					)
						return resize(lowercase		,	size=lowercase		,	resample=lowercase		,	data_format=lowercase		,	**lowercase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase = None		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				Dict     =      get_size_dict(lowercase					)
						if "height" not in size or "width" not in size:
								raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
						return center_crop(lowercase		,	size=(size['height'], size['width'])		,	data_format=lowercase		,	**lowercase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase = None		,	**lowercase		,	):



						"""simple docstring"""





						return rescale(lowercase		,	scale=lowercase		,	data_format=lowercase		,	**lowercase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	**lowercase		,	):



						"""simple docstring"""





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







				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = ChannelDimension.FIRST		,	**lowercase		,	):



						"""simple docstring"""





						A_		:				Any     =      do_resize if do_resize is not None else self.do_resize
						A_		:				str     =      size if size is not None else self.size
						A_		:				Optional[int]     =      get_size_dict(lowercase		,	param_name='size'		,	default_to_square=lowercase					)
						A_		:				List[str]     =      resample if resample is not None else self.resample
						A_		:				Union[str, Any]     =      do_center_crop if do_center_crop is not None else self.do_center_crop
						A_		:				Optional[int]     =      crop_size if crop_size is not None else self.crop_size
						A_		:				Tuple     =      get_size_dict(lowercase		,	param_name='crop_size'		,	default_to_square=lowercase					)
						A_		:				Dict     =      do_rescale if do_rescale is not None else self.do_rescale
						A_		:				Dict     =      rescale_factor if rescale_factor is not None else self.rescale_factor
						A_		:				str     =      do_normalize if do_normalize is not None else self.do_normalize
						A_		:				List[Any]     =      image_mean if image_mean is not None else self.image_mean
						A_		:				Tuple     =      image_std if image_std is not None else self.image_std
						A_		:				List[str]     =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						A_		:				Union[str, Any]     =      make_list_of_images(lowercase					)

						if not valid_images(lowercase					):
								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:
								raise ValueError('Size must be specified if do_resize is True.'					)

						if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop 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.'					)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
								A_		:				Optional[Any]     =      [convert_to_rgb(lowercase					) for image in images]

						# All transformations expect numpy arrays.
						A_		:				str     =      [to_numpy_array(lowercase					) for image in images]

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

						if do_center_crop:
								A_		:				Optional[Any]     =      [self.center_crop(image=lowercase		,	size=lowercase					) for image in images]

						if do_rescale:
								A_		:				str     =      [self.rescale(image=lowercase		,	scale=lowercase					) for image in images]

						if do_normalize:
								A_		:				Optional[Any]     =      [self.normalize(image=lowercase		,	mean=lowercase		,	std=lowercase					) for image in images]

						A_		:				Dict     =      [to_channel_dimension_format(lowercase		,	lowercase					) for image in images]

						A_		:				List[str]     =      {'pixel_values': images}
						return BatchFeature(data=lowercase		,	tensor_type=lowercase					)










import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def        _lowerCamelCase(	lowercase__=None   , lowercase__=None	)						->	Dict:

		'''simple docstring'''







		return field(default_factory=lambda: default   , metadata=lowercase__	)





@dataclass
class       A    :
							UpperCamelCase_   :							str       =field(
							    metadata={'''help''': '''The csv file to plot.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Disable logarithmic scale when plotting'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={
							        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
							    }					,				)
							UpperCamelCase_   :							Optional[str]       =field(
							    default=A_					,				metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}					,				)
							UpperCamelCase_   :							Optional[List[str]]       =list_field(
							    default=A_					,				metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		try:
				int(lowercase__	)
				return True
		except ValueError:
				return False







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		try:
				float(lowercase__	)
				return True
		except ValueError:
				return False





class       A    :


							def __init__(self      ,				lowerCAmelCase					):
									__lowercase=			args
									__lowercase=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

									with open(self.args.csv_file      ,				newline=''					) as csv_file:
											__lowercase=			csv.DictReader(lowerCAmelCase					)
											for row in reader:
													__lowercase=			row['model']
													self.result_dict[model_name]["bsz"].append(int(row['batch_size']					)					)
													self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']					)					)
													if can_convert_to_int(row['result']					):
															# value is not None
															__lowercase=			int(row['result']					)
													elif can_convert_to_float(row['result']					):
															# value is not None
															__lowercase=			float(row['result']					)







							def 			_A       (self					):
									__lowercase,							__lowercase=			plt.subplots()
									__lowercase=			'Time usage' if self.args.is_time else 'Memory usage'
									__lowercase=			title_str + ' for training' if self.args.is_train else title_str + ' for inference'

									if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('log'					)
											ax.set_yscale('log'					)

									for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()					)

									for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
											__lowercase=			sorted(set(self.result_dict[model_name]['bsz']					)					)
											__lowercase=			sorted(set(self.result_dict[model_name]['seq_len']					)					)
											__lowercase=			self.result_dict[model_name]['result']

											((__lowercase),							(__lowercase))=			(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowercase=			(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
													if self.args.plot_along_batch:
															__lowercase=			np.asarray(
															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,				dtype=lowerCAmelCase      ,				)
													else:
															__lowercase=			np.asarray(
															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,				dtype=np.floataa      ,				)

													((__lowercase),							(__lowercase))=			(
													    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
													)

													__lowercase=			np.asarray(lowerCAmelCase      ,				lowerCAmelCase					)[: len(lowerCAmelCase					)]
													plt.scatter(
													    lowerCAmelCase      ,				lowerCAmelCase      ,				label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'					)
													plt.plot(lowerCAmelCase      ,				lowerCAmelCase      ,				'--'					)

											title_str += f' {label_model_name} vs.'

									__lowercase=			title_str[:-4]
									__lowercase=			'Time in s' if self.args.is_time else 'Memory in MB'

									# plot
									plt.title(lowerCAmelCase					)
									plt.xlabel(lowerCAmelCase					)
									plt.ylabel(lowerCAmelCase					)
									plt.legend()

									if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file					)
									else:
											plt.show()







def        _lowerCamelCase(	)						->	str:

		'''simple docstring'''







		__lowercase=			HfArgumentParser(lowercase__	)
		__lowercase=			parser.parse_args_into_dataclasses()[0]
		__lowercase=			Plot(args=lowercase__	)
		plot.plot()


if __name__ == "__main__":
	main()






