
import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
snake_case :		str							      =  logging.getLogger(__name__)


snake_case :		str							      =  "Hello world! cécé herlolip"


snake_case :		Optional[Any]							      =  namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  List[str]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     BertAbsConfig(
	    temp_dir="."      ,   finetune_bert=_snake_case      ,   large=_snake_case      ,   share_emb=_snake_case      ,   use_bert_emb=_snake_case      ,   encoder="bert"      ,   max_pos=512      ,   enc_layers=6      ,   enc_hidden_size=512      ,   enc_heads=8      ,   enc_ff_size=512      ,   enc_dropout=0.2      ,   dec_layers=6      ,   dec_hidden_size=768      ,   dec_heads=8      ,   dec_ff_size=2048      ,   dec_dropout=0.2      ,   )
	__magic_name__		:		Tuple	   =     torch.load(_snake_case      ,   lambda _snake_case      ,   _snake_case   : storage   )
	__magic_name__		:		Any	   =     AbsSummarizer(_snake_case      ,   torch.device("cpu"   )      ,   _snake_case   )
	original.eval()

	__magic_name__		:		Optional[int]	   =     BertAbsSummarizer(_snake_case      ,   torch.device("cpu"   )   )
	new_model.eval()

	# -------------------
	# Convert the weights
	# -------------------

	logging.info("convert the model"   )
	new_model.bert.load_state_dict(original.bert.state_dict()   )
	new_model.decoder.load_state_dict(original.decoder.state_dict()   )
	new_model.generator.load_state_dict(original.generator.state_dict()   )

	# ----------------------------------
	# Make sure the outpus are identical
	# ----------------------------------

	logging.info("Make sure that the models' outputs are identical"   )
	__magic_name__		:		Any	   =     BertTokenizer.from_pretrained("bert-base-uncased"   )

	# prepare the model inputs
	__magic_name__		:		Tuple	   =     tokenizer.encode("This is sample éàalj'-."   )
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case   ))   )
	__magic_name__		:		int	   =     torch.tensor(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		int	   =     tokenizer.encode("This is sample 3 éàalj'-."   )
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_snake_case   ))   )
	__magic_name__		:		Dict	   =     torch.tensor(_snake_case   ).unsqueeze(0   )

	# failsafe to make sure the weights reset does not affect the
	# loaded weights.
	assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight   )   ) == 0

	# forward pass
	__magic_name__		:		Dict	   =     encoder_input_ids
	__magic_name__		:		List[Any]	   =     decoder_input_ids
	__magic_name__		:		List[str]	   =     None
	__magic_name__		:		Any	   =     None
	__magic_name__		:		List[Any]	   =     None
	__magic_name__		:		Optional[int]	   =     None
	__magic_name__		:		str	   =     None

	# The original model does not apply the geneator layer immediatly but rather in
	# the beam search (where it combines softmax + linear layer). Since we already
	# apply the softmax in our generation process we only apply the linear layer here.
	# We make sure that the outputs of the full stack are identical
	__magic_name__		:		List[Any]	   =     original(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )[0]
	__magic_name__		:		Optional[int]	   =     original.generator(_snake_case   )

	__magic_name__		:		str	   =     new_model(
	    _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )[0]
	__magic_name__		:		Any	   =     new_model.generator(_snake_case   )

	__magic_name__		:		Tuple	   =     torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_snake_case   )   )
	__magic_name__		:		Optional[Any]	   =     torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_snake_case   )   )

	__magic_name__		:		Union[str, Any]	   =     torch.allclose(_snake_case      ,   _snake_case      ,   atol=1E-3   )
	if are_identical:
		logging.info("all weights are equal up to 1e-3"   )
	else:
		raise ValueError("the weights are different. The new model is likely different from the original one."   )

	# The model has been saved with torch.save(model) and this is bound to the exact
	# directory structure. We save the state_dict instead.
	logging.info("saving the model's state dictionary"   )
	torch.save(
	    new_model.state_dict()      ,   "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"   )


if __name__ == "__main__":
				snake_case :		Optional[Any]							      =  argparse.ArgumentParser()
				parser.add_argument(
				    "--bertabs_checkpoint_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path the official PyTorch dump.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to the output PyTorch model.",
				)
				snake_case :		str							      =  parser.parse_args()

				convert_bertabs_checkpoints(
				    args.bertabs_checkpoint_path,
				    args.pytorch_dump_folder_path,
				)


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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		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"
		__magic_name__		:		int	   =     [(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_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _snake_case      ,   _snake_case   )

	__magic_name__		:		List[str]	   =     "huggingface/label-files"
	__magic_name__		:		int	   =     "imagenet-1k-id2label.json"
	__magic_name__		:		Optional[int]	   =     json.load(open(hf_hub_download(_snake_case      ,   _snake_case      ,   repo_type="dataset"   )      ,   "r"   )   )
	__magic_name__		:		int	   =     {int(_snake_case   ): v for k, v in idalabel.items()}
	__magic_name__		:		List[str]	   =     idalabel
	__magic_name__		:		List[str]	   =     {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

	__magic_name__		:		List[str]	   =     {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

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

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

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

	if pytorch_dump_folder_path is not None:
		Path(_snake_case   ).mkdir(exist_ok=_snake_case   )
		print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''   )
		model.save_pretrained(_snake_case   )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''   )
		processor.save_pretrained(_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__":
				snake_case :		Any							      =  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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
