





'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     int(_lowerCamelCase )
			assert noofclusters < len(_lowerCamelCase )

			# Find out the dimensionality
			__snake_case					:      Tuple							      =     len(vectors[0] )

			# Will help select random centroids from among the available vectors
			__snake_case					:      Dict							      =     list(range(len(_lowerCamelCase ) ) )
			shuffle(_lowerCamelCase )

			# GRAPH OF COMPUTATION
			# We initialize a new graph and set it as the default during each run
			# of this algorithm. This ensures that as this function is called
			# multiple times, the default graph doesn't keep getting crowded with
			# unused ops and Variables from previous function calls.

			__snake_case					:      Dict							      =     tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

						__snake_case					:      List[Any]							      =     tf.Session()

						##CONSTRUCTING THE ELEMENTS OF COMPUTATION

						##First lets ensure we have a Variable vector for each centroid,
						##initialized to one of the vectors from the available data points
						__snake_case					:      List[Any]							      =     [
						    tf.Variable(vectors[vector_indices[i]] ) for i in range(_lowerCamelCase )
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						__snake_case					:      Dict							      =     tf.placeholder("""float64"""    ,	[dim] )
						__snake_case					:      Optional[int]							      =     []
						for centroid in centroids:
									cent_assigns.append(tf.assign(_lowerCamelCase    ,	_lowerCamelCase ) )

						##Variables for cluster assignments of individual vectors(initialized
						##to 0 at first)
						__snake_case					:      Dict							      =     [tf.Variable(0 ) for i in range(len(_lowerCamelCase ) )]
						##These nodes will assign an assignment Variable the appropriate
						##value
						__snake_case					:      Optional[Any]							      =     tf.placeholder("""int32""" )
						__snake_case					:      Any							      =     []
						for assignment in assignments:
									cluster_assigns.append(tf.assign(_lowerCamelCase    ,	_lowerCamelCase ) )

						##Now lets construct the node that will compute the mean
						# The placeholder for the input
						__snake_case					:      Union[str, Any]							      =     tf.placeholder("""float"""    ,	[None, dim] )
						# The Node/op takes the input and computes a mean along the 0th
						# dimension, i.e. the list of input vectors
						__snake_case					:      Optional[int]							      =     tf.reduce_mean(_lowerCamelCase    ,	0 )

						##Node for computing Euclidean distances
						# Placeholders for input
						__snake_case					:      int							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      Optional[int]							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      int							      =     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,	_lowerCamelCase )    ,	2 ) ) )

						##This node will figure out which cluster to assign a vector to,
						##based on Euclidean distances of the vector from the centroids.
						# Placeholder for input
						__snake_case					:      Optional[Any]							      =     tf.placeholder("""float"""    ,	[noofclusters] )
						__snake_case					:      Optional[int]							      =     tf.argmin(_lowerCamelCase    ,	0 )

						##INITIALIZING STATE VARIABLES

						##This will help initialization of all Variables defined with respect
						##to the graph. The Variable-initializer should be defined after
						##all the Variables have been constructed, so that each of them
						##will be included in the initialization.
						__snake_case					:      str							      =     tf.initialize_all_variables()

						# Initialize all variables
						sess.run(_lowerCamelCase )

						##CLUSTERING ITERATIONS

						# Now perform the Expectation-Maximization steps of K-Means clustering
						# iterations. To keep things simple, we will only do a set number of
						# iterations, instead of using a Stopping Criterion.
						__snake_case					:      int							      =     100
						for _ in range(_lowerCamelCase ):
									##EXPECTATION STEP
									##Based on the centroid locations till last iteration, compute
									##the _expected_ centroid assignments.
									# Iterate over each vector
									for vector_n in range(len(_lowerCamelCase ) ):
												__snake_case					:      Optional[int]							      =     vectors[vector_n]
												# Compute Euclidean distance between this vector and each
												# centroid. Remember that this list cannot be named
												#'centroid_distances', since that is the input to the
												# cluster assignment node.
												__snake_case					:      Union[str, Any]							      =     [
												    sess.run(_lowerCamelCase    ,	feed_dict={va: vect, va: sess.run(_lowerCamelCase )} )
												    for centroid in centroids
												]
												# Now use the cluster assignment node, with the distances
												# as the input
												__snake_case					:      Dict							      =     sess.run(
												    _lowerCamelCase    ,	feed_dict={centroid_distances: distances} )
												# Now assign the value to the appropriate state variable
												sess.run(
												    cluster_assigns[vector_n]    ,	feed_dict={assignment_value: assignment} )

									##MAXIMIZATION STEP
									# Based on the expected state computed from the Expectation Step,
									# compute the locations of the centroids so as to maximize the
									# overall objective of minimizing within-cluster Sum-of-Squares
									for cluster_n in range(_lowerCamelCase ):
												# Collect all the vectors assigned to this cluster
												__snake_case					:      str							      =     [
												    vectors[i]
												    for i in range(len(_lowerCamelCase ) )
												    if sess.run(assignments[i] ) == cluster_n
												]
												# Compute new centroid location
												__snake_case					:      str							      =     sess.run(
												    _lowerCamelCase    ,	feed_dict={mean_input: array(_lowerCamelCase )} )
												# Assign value to appropriate variable
												sess.run(
												    cent_assigns[cluster_n]    ,	feed_dict={centroid_value: new_location} )

        # Return centroids and assignments
						__snake_case					:      Tuple							      =     sess.run(_lowerCamelCase )
						__snake_case					:      str							      =     sess.run(_lowerCamelCase )
						return centroids, assignments






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()
A				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

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


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

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






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

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






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		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."

						__snake_case				=		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
												__snake_case				=		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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000
						__snake_case				=		(1, num_labels)

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

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

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

						__snake_case				=		{
						    "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(_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__":
	A				:       Optional[Any]      =  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.',
	)

	A				:       Union[str, Any]      =  parser.parse_args()
	A				:       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)





