

'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=False  )    ->    Optional[int]:

	'''simple docstring'''





	UpperCAmelCase_		:      Dict       	=					[]
	for i in range(config.num_hidden_layers  ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")  )
		rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")  )
		rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")  )
		rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")  )
		rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")  )
		rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")  )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")  )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")  )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")  )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")  )

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('cls_token', 'vit.embeddings.cls_token'),
	        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
	        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
	        ('pos_embed', 'vit.embeddings.position_embeddings'),
	    ]  )

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]  )

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		UpperCAmelCase_		:      List[str]       	=					[(pair[0], pair[1][4:]) if pair[1].startswith('vit'  ) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]  )

	return rename_keys







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=False  )    ->    Optional[int]:

	'''simple docstring'''





	for i in range(config.num_hidden_layers  ):
		if base_model:
			UpperCAmelCase_		:      Optional[int]       	=					''
		else:
			UpperCAmelCase_		:      Union[str, Any]       	=					'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		UpperCAmelCase_		:      Optional[Any]       	=					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""  )
		UpperCAmelCase_		:      str       	=					state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""  )
		# next, add query, keys and values (in that order) to the state dict
		UpperCAmelCase_		:      str       	=					in_proj_weight[
		    : config.hidden_size, :
		]
		UpperCAmelCase_		:      List[Any]       	=					in_proj_bias[: config.hidden_size]
		UpperCAmelCase_		:      Optional[Any]       	=					in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		UpperCAmelCase_		:      int       	=					in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		UpperCAmelCase_		:      Optional[Any]       	=					in_proj_weight[
		    -config.hidden_size :, :
		]
		UpperCAmelCase_		:      Optional[int]       	=					in_proj_bias[-config.hidden_size :]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:

	'''simple docstring'''





	UpperCAmelCase_		:      List[Any]       	=					['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:

	'''simple docstring'''





	UpperCAmelCase_		:      Optional[int]       	=					dct.pop(UpperCamelCase  )
	UpperCAmelCase_		:      List[str]       	=					val







def 						SCREAMING_SNAKE_CASE(  )    ->    List[str]:

	'''simple docstring'''





	UpperCAmelCase_		:      Any       	=					'http://images.cocodataset.org/val2017/000000039769.jpg'
	UpperCAmelCase_		:      Any       	=					Image.open(requests.get(UpperCamelCase   ,stream=UpperCamelCase  ).raw  )
	return im







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=True  )    ->    List[str]:

	'''simple docstring'''





	UpperCAmelCase_		:      int       	=					ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		UpperCAmelCase_		:      Optional[int]       	=					8
	# set labels if required
	if not base_model:
		UpperCAmelCase_		:      Union[str, Any]       	=					1_0_0_0
		UpperCAmelCase_		:      Union[str, Any]       	=					'huggingface/label-files'
		UpperCAmelCase_		:      Tuple       	=					'imagenet-1k-id2label.json'
		UpperCAmelCase_		:      Optional[Any]       	=					json.load(open(hf_hub_download(UpperCamelCase   ,UpperCamelCase   ,repo_type='dataset'  )   ,'r'  )  )
		UpperCAmelCase_		:      List[str]       	=					{int(UpperCamelCase  ): v for k, v in idalabel.items()}
		UpperCAmelCase_		:      Dict       	=					idalabel
		UpperCAmelCase_		:      int       	=					{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		UpperCAmelCase_		:      int       	=					3_8_4
		UpperCAmelCase_		:      str       	=					1_5_3_6
		UpperCAmelCase_		:      Tuple       	=					1_2
		UpperCAmelCase_		:      Optional[Any]       	=					6

	# load original model from torch hub
	UpperCAmelCase_		:      Union[str, Any]       	=					torch.hub.load('facebookresearch/dino:main'   ,UpperCamelCase  )
	original_model.eval()

	# load state_dict of original model, remove and rename some keys
	UpperCAmelCase_		:      List[Any]       	=					original_model.state_dict()
	if base_model:
		remove_classification_head_(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					create_rename_keys(UpperCamelCase   ,base_model=UpperCamelCase  )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
	read_in_q_k_v(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

	# load HuggingFace model
	if base_model:
		UpperCAmelCase_		:      List[str]       	=					ViTModel(UpperCamelCase   ,add_pooling_layer=UpperCamelCase  ).eval()
	else:
		UpperCAmelCase_		:      str       	=					ViTForImageClassification(UpperCamelCase  ).eval()
	model.load_state_dict(UpperCamelCase  )

	# Check outputs on an image, prepared by ViTImageProcessor
	UpperCAmelCase_		:      List[str]       	=					ViTImageProcessor()
	UpperCAmelCase_		:      str       	=					image_processor(images=prepare_img()   ,return_tensors='pt'  )
	UpperCAmelCase_		:      Optional[int]       	=					encoding['pixel_values']
	UpperCAmelCase_		:      Optional[Any]       	=					model(UpperCamelCase  )

	if base_model:
		UpperCAmelCase_		:      List[Any]       	=					original_model(UpperCamelCase  )
		assert torch.allclose(UpperCamelCase   ,outputs.last_hidden_state[:, 0, :]   ,atol=1e-1  )
	else:
		UpperCAmelCase_		:      Tuple       	=					original_model(UpperCamelCase  )
		assert logits.shape == outputs.logits.shape
		assert torch.allclose(UpperCamelCase   ,outputs.logits   ,atol=1e-3  )

	Path(UpperCamelCase  ).mkdir(exist_ok=UpperCamelCase  )
	print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
	model.save_pretrained(UpperCamelCase  )
	print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
	image_processor.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="dino_vitb16",
						    type=str,
						    help="Name of the model trained with DINO you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)
						parser.add_argument(
						    "--base_model",
						    action="store_true",
						    help="Whether to only convert the base model (no projection head weights).",
						)

						parser.set_defaults(base_model=True)
						lowerCAmelCase__										= parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[Any]       	=					input_array

								# Random initial weights are assigned where first argument is the
								# number of nodes in previous layer and second argument is the
								# number of nodes in the next layer.

								# Random initial weights are assigned.
								# self.input_array.shape[1] is used to represent number of nodes in input layer.
								# First hidden layer consists of 4 nodes.
								UpperCAmelCase_		:      Tuple       	=					numpy.random.rand(
								    self.input_array.shape[1]    ,			4)

								# Random initial values for the first hidden layer.
								# First hidden layer has 4 nodes.
								# Second hidden layer has 3 nodes.
								UpperCAmelCase_		:      List[str]       	=					numpy.random.rand(
								    4    ,			3)

								# Random initial values for the second hidden layer.
								# Second hidden layer has 3 nodes.
								# Output layer has 1 node.
								UpperCAmelCase_		:      Dict       	=					numpy.random.rand(3    ,			1)

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase_		:      Union[str, Any]       	=					numpy.zeros(output_array.shape)


							def 						_snake_case							(			self)							->				numpy.ndarray:
								UpperCAmelCase_		:      Any       	=					sigmoid(
								    numpy.dot(self.input_array    ,			self.input_layer_and_first_hidden_layer_weights))

								# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
								# connecting the first hidden set of nodes with the second hidden set of nodes.
								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								# layer_between_second_hidden_layer_and_output is the layer connecting
								# second hidden layer with the output node.
								UpperCAmelCase_		:      int       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,			self.second_hidden_layer_and_output_layer_weights    ,			))

								return self.layer_between_second_hidden_layer_and_output


							def 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Optional[int]       	=					numpy.dot(
								    self.layer_between_first_hidden_layer_and_second_hidden_layer.T    ,			2
								    * (self.output_array - self.predicted_output)
								    * sigmoid_derivative(self.predicted_output)    ,			)
								UpperCAmelCase_		:      Any       	=					numpy.dot(
								    self.layer_between_input_and_first_hidden_layer.T    ,			numpy.dot(
								        2
								        * (self.output_array - self.predicted_output)
								        * sigmoid_derivative(self.predicted_output)    ,			self.second_hidden_layer_and_output_layer_weights.T    ,			)
								    * sigmoid_derivative(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,			)
								UpperCAmelCase_		:      Tuple       	=					numpy.dot(
								    self.input_array.T    ,			numpy.dot(
								        numpy.dot(
								            2
								            * (self.output_array - self.predicted_output)
								            * sigmoid_derivative(self.predicted_output)    ,			self.second_hidden_layer_and_output_layer_weights.T    ,			)
								        * sigmoid_derivative(
								            self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,			self.first_hidden_layer_and_second_hidden_layer_weights.T    ,			)
								    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer)    ,			)

								self.input_layer_and_first_hidden_layer_weights += (
								    updated_input_layer_and_first_hidden_layer_weights
								)
								self.first_hidden_layer_and_second_hidden_layer_weights += (
								    updated_first_hidden_layer_and_second_hidden_layer_weights
								)
								self.second_hidden_layer_and_output_layer_weights += (
								    updated_second_hidden_layer_and_output_layer_weights
								)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(self.array    ,			self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      int       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,			self.second_hidden_layer_and_output_layer_weights    ,			))

								return int(self.layer_between_second_hidden_layer_and_output > 0.6)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )   ,dtype=numpy.floataa   ,)

	# True output values for the given input values.
	UpperCAmelCase_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase   ,output_array=UpperCamelCase  )

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=UpperCamelCase   ,iterations=1_0   ,give_loss=UpperCamelCase  )

	return neural_network.predict(numpy.array(([1, 1, 1])   ,dtype=numpy.floataa  )  )


if __name__ == "__main__":
						example()






