
'''simple docstring'''
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							Tuple:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						[]

		# fmt: off
		# stem:
		rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')			)
		rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')			)

		rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')			)
		rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')			)

		# backbone
		rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')			)
		rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')			)
		rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')			)

		for stage_idx in range(len(config.backbone_config.depths			)			):
				for layer_idx in range(config.backbone_config.depths[stage_idx]			):
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')			)
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')			)

				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')			)
				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')			)
				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')			)

		# transformer encoder
		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')			)

		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 "vit" from all keys that start with "vit"
				snake_case  :       Any     			=						[(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'''),
				    ]			)
		# fmt: on

		return rename_keys




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							Union[str, Any]:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


		snake_case  :       str     			=						['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


		snake_case  :       int     			=						dct.pop(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Dict     			=						val




def    _UpperCamelCase						(       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case  :       List[Any]     			=						Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,       stream=SCREAMING_SNAKE_CASE__			).raw			)
		return im




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						BitConfig(
		    global_padding='''same''' ,       layer_type='''bottleneck''' ,       depths=(3, 4, 9) ,       out_features=['''stage3'''] ,       embedding_dynamic_padding=SCREAMING_SNAKE_CASE__ ,       )
		snake_case  :       List[Any]     			=						ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE__ ,       image_size=384 ,       num_labels=1000			)
		snake_case  :       Union[str, Any]     			=						False

		# load original model from timm
		snake_case  :       Dict     			=						timm.create_model(SCREAMING_SNAKE_CASE__ ,       pretrained=SCREAMING_SNAKE_CASE__			)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		snake_case  :       List[str]     			=						timm_model.state_dict()
		if base_model:
				remove_classification_head_(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Dict     			=						create_rename_keys(SCREAMING_SNAKE_CASE__ ,       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__			)

		snake_case  :       Dict     			=						'''huggingface/label-files'''
		snake_case  :       Optional[int]     			=						'''imagenet-1k-id2label.json'''
		snake_case  :       Union[str, Any]     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
		snake_case  :       Optional[Any]     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
		snake_case  :       Dict     			=						idalabel
		snake_case  :       Tuple     			=						{v: k for k, v in idalabel.items()}

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				snake_case  :       List[Any]     			=						ViTHybridModel(SCREAMING_SNAKE_CASE__			).eval()
		else:
				snake_case  :       List[Any]     			=						ViTHybridForImageClassification(SCREAMING_SNAKE_CASE__			).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)

		# create image processor
		snake_case  :       int     			=						create_transform(**resolve_data_config({} ,       model=SCREAMING_SNAKE_CASE__			)			)
		snake_case  :       Any     			=						transform.transforms

		snake_case  :       Dict     			=						{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		snake_case  :       Optional[int]     			=						ViTHybridImageProcessor(
		    do_resize=SCREAMING_SNAKE_CASE__ ,       size={'''shortest_edge''': timm_transforms[0].size} ,       resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,       do_center_crop=SCREAMING_SNAKE_CASE__ ,       crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,       do_normalize=SCREAMING_SNAKE_CASE__ ,       image_mean=timm_transforms[-1].mean.tolist() ,       image_std=timm_transforms[-1].std.tolist() ,       )

		snake_case  :       int     			=						prepare_img()
		snake_case  :       Tuple     			=						transform(SCREAMING_SNAKE_CASE__			).unsqueeze(0			)
		snake_case  :       Union[str, Any]     			=						processor(SCREAMING_SNAKE_CASE__ ,       return_tensors='''pt'''			).pixel_values

		# verify pixel values
		assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		# verify logits
		with torch.no_grad():
				snake_case  :       int     			=						model(SCREAMING_SNAKE_CASE__			)
				snake_case  :       List[str]     			=						outputs.logits

		print('''Predicted class:''' ,       logits.argmax(-1			).item()			)
		if base_model:
				snake_case  :       Optional[int]     			=						timm_model.forward_features(SCREAMING_SNAKE_CASE__			)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       outputs.pooler_output ,       atol=1E-3			)
		else:
				snake_case  :       List[str]     			=						timm_model(SCREAMING_SNAKE_CASE__			)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE__ ,       outputs.logits ,       atol=1E-3			)
		print('''Looks ok!'''			)

		if pytorch_dump_folder_path is not None:
				Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
				print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'			)
				model.save_pretrained(SCREAMING_SNAKE_CASE__			)
				print(F'Saving processor to {pytorch_dump_folder_path}'			)
				processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

		if push_to_hub:
				print(F'Pushing model and processor to the hub {vit_name}'			)
				model.push_to_hub(F'ybelkada/{vit_name}'			)
				processor.push_to_hub(F'ybelkada/{vit_name}'			)


if __name__ == "__main__":
						lowercase__						       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--vit_name",
						    default="vit_base_r50_s16_384",
						    type=str,
						    help="Name of the hybrid ViT 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."
						)
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
						)

						lowercase__						       =      parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''
from __future__ import annotations

from PIL import Image

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							list[list[int]]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						[]
		for i in range(len(SCREAMING_SNAKE_CASE__			)			):
				snake_case  :       str     			=						[]
				for j in range(len(cells[i]			)			):
						# Get the number of live neighbours
						snake_case  :       Any     			=						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(SCREAMING_SNAKE_CASE__			) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(SCREAMING_SNAKE_CASE__			) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(SCREAMING_SNAKE_CASE__			) - 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.
						snake_case  :       List[Any]     			=						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(SCREAMING_SNAKE_CASE__			)
		return next_generation




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							list[Image.Image]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						[]
		for _ in range(SCREAMING_SNAKE_CASE__			):
				# Create output image
				snake_case  :       List[Any]     			=						Image.new('''RGB''' ,       (len(cells[0]			), len(SCREAMING_SNAKE_CASE__			))			)
				snake_case  :       Optional[Any]     			=						img.load()

				# Save cells to image
				for x in range(len(SCREAMING_SNAKE_CASE__			)			):
						for y in range(len(cells[0]			)			):
								snake_case  :       str     			=						255 - cells[y][x] * 255
								snake_case  :       List[str]     			=						(colour, colour, colour)

        # Save image
				images.append(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Dict     			=						new_generation(SCREAMING_SNAKE_CASE__			)
		return images


if __name__ == "__main__":
						lowercase__						       =      generate_images(GLIDER, 1_6)
						images[0].save("out.gif", save_all=True, append_images=images[1:])


