




'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE							:   int      		= logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:   Any      		= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class  _snake_case		(	lowercase_						,						lowercase_							):
			lowerCAmelCase_     :      Tuple						       =    "swin"

			lowerCAmelCase_     :      Dict						       =    {
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}




			def __init__(					self				,							a__=224				,							a__=4				,							a__=3				,							a__=96				,							a__=[2, 2, 6, 2]				,							a__=[3, 6, 12, 24]				,							a__=7				,							a__=4.0				,							a__=True				,							a__=0.0				,							a__=0.0				,							a__=0.1				,							a__="gelu"				,							a__=False				,							a__=0.0_2				,							a__=1e-5				,							a__=32				,							a__=None				,							a__=None				,							**a__				,							)				->				Optional[int]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_						=      image_size
										snake_case_						=      patch_size
										snake_case_						=      num_channels
										snake_case_						=      embed_dim
										snake_case_						=      depths
										snake_case_						=      len(a__							)
										snake_case_						=      num_heads
										snake_case_						=      window_size
										snake_case_						=      mlp_ratio
										snake_case_						=      qkv_bias
										snake_case_						=      hidden_dropout_prob
										snake_case_						=      attention_probs_dropout_prob
										snake_case_						=      drop_path_rate
										snake_case_						=      hidden_act
										snake_case_						=      use_absolute_embeddings
										snake_case_						=      layer_norm_eps
										snake_case_						=      initializer_range
										snake_case_						=      encoder_stride
										# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case_						=      int(embed_dim * 2 ** (len(a__							) - 1)							)
										snake_case_						=      ["stem"] + [F'stage{idx}' for idx in range(1				,							len(a__							) + 1							)]
										snake_case_					,      snake_case_						=      get_aligned_output_features_output_indices(
										    out_features=a__				,							out_indices=a__				,							stage_names=self.stage_names							)




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      str						       =    version.parse("1.11"							)




			@property
			def    lowerCAmelCase__				(					self							)				->				Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]							)




			@property
			def    lowerCAmelCase__				(					self							)				->				float:

										'''simple docstring'''




										return 1e-4








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()






