



"""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()
snake_case_    					=	logging.get_logger()







@dataclass
class 				A_      :



						"""simple docstring"""
						__UpperCamelCase		    =							42
						__UpperCamelCase		    =							field(default_factory=SCREAMING_SNAKE_CASE_    )
						__UpperCamelCase		    =							field(default_factory=SCREAMING_SNAKE_CASE_    )






						def      UpperCAmelCase__					( self			:str       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Tensor       ,  lowercase_			:Tensor )    ->      Any:
												UpperCAmelCase					=			len(list(m.modules() ) ) == 1 or isinstance(_A       ,  nn.Convad ) or isinstance(_A       ,  nn.BatchNormad )
												if has_not_submodules:
																		self.traced.append(_A )






						def __call__( self			:Dict       ,  lowercase_			:Tensor )    ->      Optional[Any]:
												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook ) )
												self.module(_A )
												[x.remove() for x in self.handles]
												return self






						@property
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[str]:
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda lowercase_ : len(list(x.state_dict().keys() ) ) > 0       ,  self.traced ) )







@dataclass
class 				A_      :



						"""simple docstring"""
						__UpperCamelCase		    =							42
						__UpperCamelCase		    =							42
						__UpperCamelCase		    =							0
						__UpperCamelCase		    =							field(default_factory=SCREAMING_SNAKE_CASE_    )
						__UpperCamelCase		    =							field(default_factory=SCREAMING_SNAKE_CASE_    )






						def __call__( self			:List[str]       ,  lowercase_			:Tensor )    ->      List[Any]:
												UpperCAmelCase					=			Tracker(self.dest )(_A ).parametrized
												UpperCAmelCase					=			Tracker(self.src )(_A ).parametrized

												UpperCAmelCase					=			list(filter(lambda lowercase_ : type(_A ) not in self.src_skip       ,  _A ) )
												UpperCAmelCase					=			list(filter(lambda lowercase_ : type(_A ) not in self.dest_skip       ,  _A ) )

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

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


def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_ = True						):
						print(F"""Converting {name}..."""						)
						with torch.no_grad():
												UpperCAmelCase					=			timm.create_model(UpperCAmelCase_		,     pretrained=UpperCAmelCase_						).eval()
												UpperCAmelCase					=			ResNetForImageClassification(UpperCAmelCase_						).eval()
												UpperCAmelCase					=			ModuleTransfer(src=UpperCAmelCase_		,     dest=UpperCAmelCase_						)
												UpperCAmelCase					=			torch.randn((1, 3, 224, 224)						)
												module_transfer(UpperCAmelCase_						)

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

						UpperCAmelCase					=			F"""resnet{'-'.join(name.split('resnet'						)						)}"""
						print(UpperCAmelCase_						)

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

												# we can use the convnext one
												UpperCAmelCase					=			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=UpperCAmelCase_		,     )

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = None		,     lowercase_ = True						):
						UpperCAmelCase					=			'imagenet-1k-id2label.json'
						UpperCAmelCase					=			1000
						UpperCAmelCase					=			(1, num_labels)

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

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

						UpperCAmelCase					=			partial(UpperCAmelCase_		,     num_labels=UpperCAmelCase_		,     idalabel=UpperCAmelCase_		,     labelaid=UpperCAmelCase_						)

						UpperCAmelCase					=			{
						    '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, 1024, 2048]		,     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, 1024, 2048]		,     layer_type='bottleneck'						),
						    'resnet101': ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]		,     hidden_sizes=[256, 512, 1024, 2048]		,     layer_type='bottleneck'						),
						    'resnet152': ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]		,     hidden_sizes=[256, 512, 1024, 2048]		,     layer_type='bottleneck'						),
						}

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


if __name__ == "__main__":
				snake_case_    					=	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.""",
				)

				snake_case_    					=	parser.parse_args()
				snake_case_    					=	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)










"""simple docstring"""


import math

import flax.linen as nn
import jax.numpy as jnp

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_ = 1		,     lowercase_ = 1		,     lowercase_ = 1.0e4		,     lowercase_ = False		,     lowercase_ = 1.0		,     ):
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
						UpperCAmelCase					=			float(embedding_dim // 2						)
						UpperCAmelCase					=			math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						UpperCAmelCase					=			min_timescale * jnp.exp(jnp.arange(lowercase_		,     dtype=jnp.floataa						) * -log_timescale_increment						)
						UpperCAmelCase					=			jnp.expand_dims(lowercase_		,     1						) * jnp.expand_dims(lowercase_		,     0						)

						# scale embeddings
						UpperCAmelCase					=			scale * emb

						if flip_sin_to_cos:
												UpperCAmelCase					=			jnp.concatenate([jnp.cos(lowercase_						), jnp.sin(lowercase_						)]		,     axis=1						)
						else:
												UpperCAmelCase					=			jnp.concatenate([jnp.sin(lowercase_						), jnp.cos(lowercase_						)]		,     axis=1						)
						UpperCAmelCase					=			jnp.reshape(lowercase_		,     [jnp.shape(lowercase_						)[0], embedding_dim]						)
						return signal







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							jnp.floataa






						@nn.compact
						def __call__( self			:Union[str, Any]       ,  lowercase_			:Tuple )    ->      str:
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_1' )(lowercase_ )
												UpperCAmelCase					=			nn.silu(lowercase_ )
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_2' )(lowercase_ )
												return temb







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							False
						__UpperCamelCase		    =							1






						@nn.compact
						def __call__( self			:Any       ,  lowercase_			:int )    ->      Union[str, Any]:
												return get_sinusoidal_embeddings(
												    lowercase_       ,  embedding_dim=self.dim       ,  flip_sin_to_cos=self.flip_sin_to_cos       ,  freq_shift=self.freq_shift )





