
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   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Dict=False							)	->     str:
							lowerCamelCase_					=       []

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[Any]							)	->     int:
							lowerCamelCase_					=       ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase__      ,					UpperCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   int							)	->     List[str]:
							lowerCamelCase_					=       dct.pop(UpperCAmelCase__							)
							lowerCamelCase_					=       val



def   __lowerCAmelCase    (				)	->     Optional[Any]:
							lowerCamelCase_					=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_					=       Image.open(requests.get(UpperCAmelCase__      ,					stream=UpperCAmelCase__							).raw							)
							return im



@torch.no_grad()
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Any=False							)	->     Tuple:
							lowerCamelCase_					=       BitConfig(
							    global_padding="""same"""      ,					layer_type="""bottleneck"""      ,					depths=(3, 4, 9)      ,					out_features=["""stage3"""]      ,					embedding_dynamic_padding=UpperCAmelCase__      ,					)
							lowerCamelCase_					=       ViTHybridConfig(backbone_config=UpperCAmelCase__      ,					image_size=3_8_4      ,					num_labels=1_0_0_0							)
							lowerCamelCase_					=       False

							# load original model from timm
							lowerCamelCase_					=       timm.create_model(UpperCAmelCase__      ,					pretrained=UpperCAmelCase__							)
							timm_model.eval()

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

							lowerCamelCase_					=       """huggingface/label-files"""
							lowerCamelCase_					=       """imagenet-1k-id2label.json"""
							lowerCamelCase_					=       json.load(open(hf_hub_download(UpperCAmelCase__      ,					UpperCAmelCase__      ,					repo_type="""dataset"""							)      ,					"""r"""							)							)
							lowerCamelCase_					=       {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
							lowerCamelCase_					=       idalabel
							lowerCamelCase_					=       {v: k for k, v in idalabel.items()}

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														lowerCamelCase_					=       ViTHybridModel(UpperCAmelCase__							).eval()
							else:
														lowerCamelCase_					=       ViTHybridForImageClassification(UpperCAmelCase__							).eval()
							model.load_state_dict(UpperCAmelCase__							)

							# create image processor
							lowerCamelCase_					=       create_transform(**resolve_data_config({}      ,					model=UpperCAmelCase__							)							)
							lowerCamelCase_					=       transform.transforms

							lowerCamelCase_					=       {
							    """bilinear""": PILImageResampling.BILINEAR,
							    """bicubic""": PILImageResampling.BICUBIC,
							    """nearest""": PILImageResampling.NEAREST,
							}

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

							lowerCamelCase_					=       prepare_img()
							lowerCamelCase_					=       transform(UpperCAmelCase__							).unsqueeze(0							)
							lowerCamelCase_					=       processor(UpperCAmelCase__      ,					return_tensors="""pt"""							).pixel_values

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

							# verify logits
							with torch.no_grad():
														lowerCamelCase_					=       model(UpperCAmelCase__							)
														lowerCamelCase_					=       outputs.logits

							print("""Predicted class:"""      ,					logits.argmax(-1							).item()							)
							if base_model:
														lowerCamelCase_					=       timm_model.forward_features(UpperCAmelCase__							)
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(UpperCAmelCase__      ,					outputs.pooler_output      ,					atol=1e-3							)
							else:
														lowerCamelCase_					=       timm_model(UpperCAmelCase__							)
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(UpperCAmelCase__      ,					outputs.logits      ,					atol=1e-3							)
							print("""Looks ok!"""							)

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

							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)







import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


if __name__ == "__main__":
		fire.Fire(save_randomly_initialized_version)





