


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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




		lowercase__            =      []
		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"
				lowercase__            =      [(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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      dct.pop(SCREAMING_SNAKE_CASE     )
		lowercase__            =      val
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=True     ):


		"""simple docstring"""




		lowercase__            =      ViTConfig()
		# patch_size
		if model_name[-1] == "8":
				lowercase__            =      8
		# set labels if required
		if not base_model:
				lowercase__            =      10_00
				lowercase__            =      '''huggingface/label-files'''
				lowercase__            =      '''imagenet-1k-id2label.json'''
				lowercase__            =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				repo_type='''dataset'''     )						,				'''r'''     )     )
				lowercase__            =      {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
				lowercase__            =      idalabel
				lowercase__            =      {v: k for k, v in idalabel.items()}
		# size of the architecture
		if model_name in ["dino_vits8", "dino_vits16"]:
				lowercase__            =      3_84
				lowercase__            =      15_36
				lowercase__            =      12
				lowercase__            =      6

		# load original model from torch hub
		lowercase__            =      torch.hub.load('''facebookresearch/dino:main'''						,				SCREAMING_SNAKE_CASE     )
		original_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowercase__            =      original_model.state_dict()
		if base_model:
				remove_classification_head_(SCREAMING_SNAKE_CASE     )
		lowercase__            =      create_rename_keys(SCREAMING_SNAKE_CASE						,				base_model=SCREAMING_SNAKE_CASE     )
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
		read_in_q_k_v(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		# load HuggingFace model
		if base_model:
				lowercase__            =      ViTModel(SCREAMING_SNAKE_CASE						,				add_pooling_layer=SCREAMING_SNAKE_CASE     ).eval()
		else:
				lowercase__            =      ViTForImageClassification(SCREAMING_SNAKE_CASE     ).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE     )

		# Check outputs on an image, prepared by ViTImageProcessor
		lowercase__            =      ViTImageProcessor()
		lowercase__            =      image_processor(images=prepare_img()						,				return_tensors='''pt'''     )
		lowercase__            =      encoding['''pixel_values''']
		lowercase__            =      model(SCREAMING_SNAKE_CASE     )

		if base_model:
				lowercase__            =      original_model(SCREAMING_SNAKE_CASE     )
				assert torch.allclose(SCREAMING_SNAKE_CASE						,				outputs.last_hidden_state[:, 0, :]						,				atol=1E-1     )
		else:
				lowercase__            =      original_model(SCREAMING_SNAKE_CASE     )
				assert logits.shape == outputs.logits.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE						,				outputs.logits						,				atol=1E-3     )

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


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)








from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

