



'''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   _A       (   _lowerCAmelCase=None  ,   _lowerCAmelCase=None					):



							"""simple docstring"""



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





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Dict  ,       _lowerCAmelCase  :  str):







											'''simple docstring'''

											__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 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__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   _A       (   ):



							"""simple docstring"""



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


if __name__ == "__main__":
				main()








'''simple docstring'''



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	 =       logging.get_logger()





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Tensor  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=len(list(m.modules())) == 1 or isinstance(_lowerCAmelCase  ,       nn.Convad) or isinstance(_lowerCAmelCase  ,       nn.BatchNormad)
											if has_not_submodules:
																		self.traced.append(_lowerCAmelCase)

				def __call__(							self  :  Dict  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook))
											self.module(_lowerCAmelCase)
											[x.remove() for x in self.handles]
											return self

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											return list(filter(lambda _lowerCAmelCase: len(list(x.state_dict().keys())) > 0  ,       self.traced))





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    0
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def __call__(							self  :  Any  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=Tracker(self.dest)(_lowerCAmelCase).parametrized
											__lowercase       	=Tracker(self.src)(_lowerCAmelCase).parametrized

											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.src_skip  ,       _lowerCAmelCase))
											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.dest_skip  ,       _lowerCAmelCase))

											if len(_lowerCAmelCase) != len(_lowerCAmelCase):
																		raise Exception(
																		    f"""Numbers of operations are different. Source module has {len(_lowerCAmelCase)} operations while"""
																		    f""" destination module has {len(_lowerCAmelCase)}.""")

											for dest_m, src_m in zip(_lowerCAmelCase  ,       _lowerCAmelCase):
																		dest_m.load_state_dict(src_m.state_dict())
																		if self.verbose == 1:
																									print(f"""Transfered from={src_m} to={dest_m}""")



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							print(f"""Converting {name}..."""					)
							with torch.no_grad():
														__lowercase       	=timm.create_model(_lowerCAmelCase  ,   pretrained=_lowerCAmelCase					).eval()
														__lowercase       	=ResNetForImageClassification(_lowerCAmelCase					).eval()
														__lowercase       	=ModuleTransfer(src=_lowerCAmelCase  ,   dest=_lowerCAmelCase					)
														__lowercase       	=torch.randn((1, 3, 224, 224)					)
														module_transfer(_lowerCAmelCase					)

							assert torch.allclose(from_model(_lowerCAmelCase					)  ,   our_model(_lowerCAmelCase					).logits					), "The model logits don't match the original one."

							__lowercase       	=f"""resnet{'-'.join(name.split('resnet'					)					)}"""
							print(_lowerCAmelCase					)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name  ,   commit_message='Add model'  ,   use_temp_dir=_lowerCAmelCase  ,   )

														# we can use the convnext one
														__lowercase       	=AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name  ,   commit_message='Add image processor'  ,   use_temp_dir=_lowerCAmelCase  ,   )

														print(f"""Pushed {checkpoint_name}"""					)



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							__lowercase       	='imagenet-1k-id2label.json'
							__lowercase       	=1_000
							__lowercase       	=(1, num_labels)

							__lowercase       	='huggingface/label-files'
							__lowercase       	=num_labels
							__lowercase       	=json.load(open(hf_hub_download(_lowerCAmelCase  ,   _lowerCAmelCase  ,   repo_type='dataset'					)  ,   'r'					)					)
							__lowercase       	={int(_lowerCAmelCase					): v for k, v in idalabel.items()}

							__lowercase       	=idalabel
							__lowercase       	={v: k for k, v in idalabel.items()}

							__lowercase       	=partial(_lowerCAmelCase  ,   num_labels=_lowerCAmelCase  ,   idalabel=_lowerCAmelCase  ,   labelaid=_lowerCAmelCase					)

							__lowercase       	={
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							}

							if model_name:
														convert_weight_and_push(_lowerCAmelCase  ,   names_to_config[model_name]  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase	 =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default=None,
		    type=str,
		    help=(
		        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
		        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=Path,
		    required=True,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument(
		    """--push_to_hub""",
		    default=True,
		    type=bool,
		    required=False,
		    help="""If True, push model and image processor to the hub.""",
		)

		lowerCamelCase	 =       parser.parse_args()
		lowerCamelCase	 =       args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



