




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  a_		(							__snake_case=None     ,		__snake_case=None					)	->			int:

			'''simple docstring'''


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




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







def  a_		(							__snake_case					)	->			Tuple:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			List[str]:

			'''simple docstring'''


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




class     A					:







							def __init__(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	Dict				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= args
										UpperCamelCase_     				= defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

										with open(self.args.csv_file   ,     newline=''				) as csv_file:
													UpperCamelCase_     				= csv.DictReader(__UpperCAmelCase				)
													for row in reader:
																UpperCamelCase_     				= 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
																			UpperCamelCase_     				= int(row['result']				)
																elif can_convert_to_float(row['result']				):
																			# value is not None
																			UpperCamelCase_     				= float(row['result']				)







							def        lowercase__							(    self   :	Optional[Any]				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= plt.subplots()
										UpperCamelCase_     				= 'Time usage' if self.args.is_time else 'Memory usage'
										UpperCamelCase_     				= 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()				):
													UpperCamelCase_     				= sorted(set(self.result_dict[model_name]['bsz']				)				)
													UpperCamelCase_     				= sorted(set(self.result_dict[model_name]['seq_len']				)				)
													UpperCamelCase_     				= self.result_dict[model_name]['result']

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

													UpperCamelCase_     				= (
													    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:
																			UpperCamelCase_     				= np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,     dtype=__UpperCAmelCase   ,     )
																else:
																			UpperCamelCase_     				= np.asarray(
																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,     dtype=np.floataa   ,     )

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

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

													title_str += f''' {label_model_name} vs.'''

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

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

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







def  a_		(							)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= HfArgumentParser(__snake_case					)
			UpperCamelCase_     				= parser.parse_args_into_dataclasses()[0]
			UpperCamelCase_     				= Plot(args=__snake_case					)
			plot.plot()


if __name__ == "__main__":
				main()






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor




class     A					(				unittest.TestCase       ):







							def __init__(    self   :	Any   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	List[Any]=7   ,     __UpperCAmelCase   :	Union[str, Any]=3   ,     __UpperCAmelCase   :	Optional[Any]=18   ,     __UpperCAmelCase   :	List[str]=30   ,     __UpperCAmelCase   :	int=400   ,     __UpperCAmelCase   :	List[Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     __UpperCAmelCase   :	List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     __UpperCAmelCase   :	Dict=True   ,     )		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= size if size is not None else {'shortest_edge': 20}
										UpperCamelCase_     				= crop_size if crop_size is not None else {'height': 18, 'width': 18}
										UpperCamelCase_     				= parent
										UpperCamelCase_     				= batch_size
										UpperCamelCase_     				= num_channels
										UpperCamelCase_     				= image_size
										UpperCamelCase_     				= min_resolution
										UpperCamelCase_     				= max_resolution
										UpperCamelCase_     				= do_resize
										UpperCamelCase_     				= size
										UpperCamelCase_     				= do_center_crop
										UpperCamelCase_     				= crop_size
										UpperCamelCase_     				= do_normalize
										UpperCamelCase_     				= image_mean
										UpperCamelCase_     				= image_std
										UpperCamelCase_     				= do_convert_rgb







							def        lowercase__							(    self   :	Tuple				)		->					List[Any]:

										"""simple docstring"""



										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	str=False   ,     __UpperCAmelCase   :	Tuple=False   ,     __UpperCAmelCase   :	Optional[int]=False				)		->					Any:

										"""simple docstring"""



										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
													UpperCamelCase_     				= []
													for i in range(self.batch_size				):
																image_inputs.append(
																    np.random.randint(
																        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta				)				)
										else:
													UpperCamelCase_     				= []
													for i in range(self.batch_size				):
																UpperCamelCase_				,   UpperCamelCase_     				= np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution				)   ,     2				)
																image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta				)				)

										if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													UpperCamelCase_     				= [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,     0   ,     -1				)				) for x in image_inputs]

										if torchify:
													UpperCamelCase_     				= [torch.from_numpy(__UpperCAmelCase				) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		Any			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self				)







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

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Tuple				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 20}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 18, 'width': 18}				)

										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42   ,     crop_size=84				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 42}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 84, 'width': 84}				)







							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[int]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	int				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







							def        lowercase__							(    self   :	Optional[Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     numpify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     np.ndarray				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







							def        lowercase__							(    self   :	Optional[Any]				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase   ,     torchify=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     torch.Tensor				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	str				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self   ,     num_channels=4				)
										UpperCamelCase_     				= 3







							@property
							def        lowercase__							(    self   :	Dict				)		->					str:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Optional[int]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	int				)		->					Any:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	Any				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )
