





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 :    int  							= logging.get_logger()


@dataclass
class 						lowerCamelCase       :







							"""simple docstring"""




							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   field(default_factory=__lowerCamelCase      )
							UpperCAmelCase_      						=   field(default_factory=__lowerCamelCase      )






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Tensor,			_UpperCAmelCase     :					Tensor						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						len(list(m.modules()						)						) == 1 or isinstance(_UpperCAmelCase,			nn.Convad						) or isinstance(_UpperCAmelCase,			nn.BatchNormad						)
														if has_not_submodules:
																					self.traced.append(_UpperCAmelCase						)






							def __call__( self     :					Tuple,			_UpperCAmelCase     :					Tensor						)				->			List[str]:





														"""simple docstring"""
														for m in self.module.modules():
																					self.handles.append(m.register_forward_hook(self._forward_hook						)						)
														self.module(_UpperCAmelCase						)
														[x.remove() for x in self.handles]
														return self






							@property
							def  A_     ( self     :					str						)				->			Optional[int]:





														"""simple docstring"""
														# check the len of the state_dict keys to see if we have learnable params
														return list(filter(lambda _UpperCAmelCase						: len(list(x.state_dict().keys()						)						) > 0,			self.traced						)						)


@dataclass
class 						lowerCamelCase       :







							"""simple docstring"""




							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   0
							UpperCAmelCase_      						=   field(default_factory=__lowerCamelCase      )
							UpperCAmelCase_      						=   field(default_factory=__lowerCamelCase      )






							def __call__( self     :					List[str],			_UpperCAmelCase     :					Tensor						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						Tracker(self.dest						)(_UpperCAmelCase						).parametrized
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						Tracker(self.src						)(_UpperCAmelCase						).parametrized

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.src_skip,			_UpperCAmelCase						)						)
														SCREAMING_SNAKE_CASE__       :      Any     =						list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.dest_skip,			_UpperCAmelCase						)						)

														if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
																					raise Exception(
																					    F'''Numbers of operations are different. Source module has {len(_UpperCAmelCase						)} operations while'''
																					    F''' destination module has {len(_UpperCAmelCase						)}.'''						)

														for dest_m, src_m in zip(_UpperCAmelCase,			_UpperCAmelCase						):
																					dest_m.load_state_dict(src_m.state_dict()						)
																					if self.verbose == 1:
																												print(F'''Transfered from={src_m} to={dest_m}'''						)




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  ResNetConfig			,				SCREAMING_SNAKE_CASE__      :  Path			,				SCREAMING_SNAKE_CASE__      :  bool = True       )	->    Optional[int]:

							'''simple docstring'''






							print(f'''Converting {name}...'''       )
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__       :      Any     =						timm.create_model(SCREAMING_SNAKE_CASE__			,				pretrained=SCREAMING_SNAKE_CASE__       ).eval()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ResNetForImageClassification(SCREAMING_SNAKE_CASE__       ).eval()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ModuleTransfer(src=SCREAMING_SNAKE_CASE__			,				dest=SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.randn((1, 3, 2_24, 2_24)       )
														module_transfer(SCREAMING_SNAKE_CASE__       )

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

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						f'''resnet{"-".join(name.split("resnet"       )       )}'''
							print(SCREAMING_SNAKE_CASE__       )

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

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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=SCREAMING_SNAKE_CASE__			,				)

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




def _a (  SCREAMING_SNAKE_CASE__      :  Path			,				SCREAMING_SNAKE_CASE__      :  str = None			,				SCREAMING_SNAKE_CASE__      :  bool = True       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"imagenet-1k-id2label.json"
							SCREAMING_SNAKE_CASE__       :      int     =						10_00
							SCREAMING_SNAKE_CASE__       :      List[str]     =						(1, num_labels)

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"huggingface/label-files"
							SCREAMING_SNAKE_CASE__       :      List[str]     =						num_labels
							SCREAMING_SNAKE_CASE__       :      Any     =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				repo_type="dataset"       )			,				"r"       )       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__       :      int     =						idalabel
							SCREAMING_SNAKE_CASE__       :      str     =						{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						partial(SCREAMING_SNAKE_CASE__			,				num_labels=SCREAMING_SNAKE_CASE__			,				idalabel=SCREAMING_SNAKE_CASE__			,				labelaid=SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						{
							    "resnet18": ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]			,				hidden_sizes=[64, 1_28, 2_56, 5_12]			,				layer_type="basic"       ),
							    "resnet26": ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]			,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,				layer_type="bottleneck"       ),
							    "resnet34": ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]			,				hidden_sizes=[64, 1_28, 2_56, 5_12]			,				layer_type="basic"       ),
							    "resnet50": ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]			,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,				layer_type="bottleneck"       ),
							    "resnet101": ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]			,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,				layer_type="bottleneck"       ),
							    "resnet152": ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]			,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,				layer_type="bottleneck"       ),
							}

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


if __name__ == "__main__":
		_lowerCamelCase :    List[str]  							= 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 :    Optional[int]  							= parser.parse_args()
		_lowerCamelCase :    Path  							= 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)













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

							# one length string
							SCREAMING_SNAKE_CASE__       :      str     =						0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						single_char_strings[ch]
																					SCREAMING_SNAKE_CASE__       :      Any     =						my_str / all_sum
																					my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )  # entropy formula.

    # print entropy
							print(f'''{round(-1 * my_fir_sum       ):.1f}'''       )

							# two len string
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sum(two_char_strings.values()       )
							SCREAMING_SNAKE_CASE__       :      int     =						0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						cha + cha
																					if sequence in two_char_strings:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						two_char_strings[sequence]
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						int(SCREAMING_SNAKE_CASE__       ) / all_sum
																												my_sec_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )

    # print second entropy
							print(f'''{round(-1 * my_sec_sum       ):.1f}'''       )

							# print the difference between them
							print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)       ):.1f}'''       )




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Counter()  # type: ignore
							SCREAMING_SNAKE_CASE__       :      Dict     =						Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0			,				len(SCREAMING_SNAKE_CASE__       ) - 1       ):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def _a (  )	->    str:

							'''simple docstring'''






							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()






