



"""simple docstring"""






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 					snake_case__       (		__lowerCamelCase    :   List[Any]=None							,		__lowerCamelCase    :   List[str]=None     ):
			"""simple docstring"""
			return field(default_factory=lambda: default							,		metadata=__lowerCamelCase     )

@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





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






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""





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






def 					snake_case__       (		__lowerCamelCase    :   Any     ):
			"""simple docstring"""





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

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  str,  lowerCamelCase							:  str					)-> Optional[int]:
								lowerCamelCase__		:		int		     =args
								lowerCamelCase__		:		Optional[Any]		     =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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




					def 		snake_case	(	self							:  List[str]					)-> str:
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =plt.subplots()
								lowerCamelCase__		:		Optional[int]		     ='''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCamelCase__		:		Optional[Any]		     =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()					):
											lowerCamelCase__		:		Optional[int]		     =sorted(set(self.result_dict[model_name]['''bsz''']					)					)
											lowerCamelCase__		:		Tuple		     =sorted(set(self.result_dict[model_name]['''seq_len''']					)					)
											lowerCamelCase__		:		Optional[int]		     =self.result_dict[model_name]['''result''']

											((lowerCamelCase__)     ,       (lowerCamelCase__))		:		Tuple		     =(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											lowerCamelCase__		:		Optional[int]		     =(
											    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:
																	lowerCamelCase__		:		Optional[Any]		     =np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],  dtype=lowerCamelCase_,  )
														else:
																	lowerCamelCase__		:		str		     =np.asarray(
																	    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],  dtype=np.floataa,  )

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

														lowerCamelCase__		:		Any		     =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.'''

								lowerCamelCase__		:		Tuple		     =title_str[:-4]
								lowerCamelCase__		:		Tuple		     ='''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 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =HfArgumentParser(__lowerCamelCase     )
			lowerCamelCase__		:		Dict		     =parser.parse_args_into_dataclasses()[0]
			lowerCamelCase__		:		List[str]		     =Plot(args=__lowerCamelCase     )
			plot.plot()


if __name__ == "__main__":
		main()









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





@slow
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

								lowerCamelCase__		:		Dict		     =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




