
import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase       :				List[str]       =			logging.get_logger(__name__)






def        a_		(       __lowercase   :	List[Any]       ,					__lowercase   :	Optional[int]=False ) ->  Optional[Any]:
			_snake_case		=			[]
			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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
						rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''') )
						rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
						rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
						rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''') )
						rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''') )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''') )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''') )

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

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

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						_snake_case		=			[(pair[0], pair[1][4:]) if pair[1].startswith('deit' ) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ('norm.weight', 'deit.layernorm.weight'),
						        ('norm.bias', 'deit.layernorm.bias'),
						        ('head.weight', 'cls_classifier.weight'),
						        ('head.bias', 'cls_classifier.bias'),
						        ('head_dist.weight', 'distillation_classifier.weight'),
						        ('head_dist.bias', 'distillation_classifier.bias'),
						    ] )

			return rename_keys






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






def        a_		(       __lowercase   :	int       ,					__lowercase   :	Tuple       ,					__lowercase   :	List[Any] ) ->  Optional[Any]:
			_snake_case		=			dct.pop(__lowercase )
			_snake_case		=			val






def        a_		(       ) ->  Union[str, Any]:
			_snake_case		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case		=			Image.open(requests.get(__lowercase       ,					stream=__lowercase ).raw )
			return im






@torch.no_grad()
def        a_		(       __lowercase   :	List[Any]       ,					__lowercase   :	Dict ) ->  List[str]:
			_snake_case		=			DeiTConfig()
			# all deit models have fine-tuned heads
			_snake_case		=			False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			_snake_case		=			1_000
			_snake_case		=			'huggingface/label-files'
			_snake_case		=			'imagenet-1k-id2label.json'
			_snake_case		=			json.load(open(hf_hub_download(__lowercase       ,					__lowercase       ,					repo_type='dataset' )       ,					'r' ) )
			_snake_case		=			{int(__lowercase ): v for k, v in idalabel.items()}
			_snake_case		=			idalabel
			_snake_case		=			{v: k for k, v in idalabel.items()}
			_snake_case		=			int(deit_name[-6:-4] )
			_snake_case		=			int(deit_name[-3:] )
			# size of the architecture
			if deit_name[9:].startswith('tiny' ):
						_snake_case		=			192
						_snake_case		=			768
						_snake_case		=			12
						_snake_case		=			3
			elif deit_name[9:].startswith('small' ):
						_snake_case		=			384
						_snake_case		=			1_536
						_snake_case		=			12
						_snake_case		=			6
			if deit_name[9:].startswith('base' ):
						pass
			elif deit_name[4:].startswith('large' ):
						_snake_case		=			1_024
						_snake_case		=			4_096
						_snake_case		=			24
						_snake_case		=			16

			# load original model from timm
			_snake_case		=			timm.create_model(__lowercase       ,					pretrained=__lowercase )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			_snake_case		=			timm_model.state_dict()
			_snake_case		=			create_rename_keys(__lowercase       ,					__lowercase )
			for src, dest in rename_keys:
						rename_key(__lowercase       ,					__lowercase       ,					__lowercase )
			read_in_q_k_v(__lowercase       ,					__lowercase       ,					__lowercase )

			# load HuggingFace model
			_snake_case		=			DeiTForImageClassificationWithTeacher(__lowercase ).eval()
			model.load_state_dict(__lowercase )

			# Check outputs on an image, prepared by DeiTImageProcessor
			_snake_case		=			int(
			    (256 / 224) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			_snake_case		=			DeiTImageProcessor(size=__lowercase       ,					crop_size=config.image_size )
			_snake_case		=			image_processor(images=prepare_img()       ,					return_tensors='pt' )
			_snake_case		=			encoding['pixel_values']
			_snake_case		=			model(__lowercase )

			_snake_case		=			timm_model(__lowercase )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(__lowercase       ,					outputs.logits       ,					atol=1E-3 )

			Path(__lowercase ).mkdir(exist_ok=__lowercase )
			print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(__lowercase )
			print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(__lowercase )


if __name__ == "__main__":
			_lowerCamelCase       :				Tuple       =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--deit_name''',
			    default='''vit_deit_base_distilled_patch16_224''',
			    type=str,
			    help='''Name of the DeiT timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_lowerCamelCase       :				int       =			parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)

from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
							line += "]"
							return line

				s += "\n".join(single_line(lowercase	) for row_vector in self.array	)
				return s



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				if not (isinstance(lowercase			,  (list, tuple)	) and len(lowercase	) == 2):
							return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
							return False
				else:
							return True



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

				# Add
				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c] + another[r, c]
				return result



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			Matrix(self.row			,  another.column	)
							for r in range(self.row	):
										for c in range(another.column	):
													for i in range(self.column	):
																result[r, c] += self[r, i] * another[i, c]
							return result
				else:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				assert self.row == self.column == u.row == v.row  # u, v should be column vector
				assert u.column == v.column == 1  # u, v should be column vector

				# Calculate
				_snake_case		=			v.transpose()
				_snake_case		=			(v_t * self * u)[0, 0] + 1
				if numerator_factor == 0:
							return None  # It's not invertable
				return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






			def        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			4, -2, 5
						print(f'''u is {u}''' )
						print(f'''v is {v}''' )
						print(f'''uv^T is {u * v.transpose()}''' )
						# Sherman Morrison
						print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()