

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							:	Dict											=       logging.get_logger(__name__)


def  _UpperCAmelCase							(		a__   ,     a__=False):

			'''simple docstring'''



			a_		:		List[str]      				=      []

			# 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"
						a_		:		Optional[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  _UpperCAmelCase							(		a__   ,     a__   ,     a__=False):

			'''simple docstring'''



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


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Dict      				=      ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(_a   ,     _a)


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      dct.pop(_a)
			a_		:		Union[str, Any]      				=      val


def  _UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Any      				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			a_		:		Tuple      				=      Image.open(requests.get(_a   ,     stream=_a).raw)
			return im


@torch.no_grad()
def  _UpperCAmelCase							(		a__   ,     a__   ,     a__=False):

			'''simple docstring'''



			a_		:		str      				=      BitConfig(
			    global_padding="""same"""   ,     layer_type="""bottleneck"""   ,     depths=(3, 4, 9)   ,     out_features=["""stage3"""]   ,     embedding_dynamic_padding=_a   ,     )
			a_		:		Tuple      				=      ViTHybridConfig(backbone_config=_a   ,     image_size=3_8_4   ,     num_labels=1_0_0_0)
			a_		:		int      				=      False

			# load original model from timm
			a_		:		str      				=      timm.create_model(_a   ,     pretrained=_a)
			timm_model.eval()

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

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						a_		:		Optional[Any]      				=      ViTHybridModel(_a).eval()
			else:
						a_		:		Any      				=      ViTHybridForImageClassification(_a).eval()
			model.load_state_dict(_a)

			# create image processor
			a_		:		Optional[Any]      				=      create_transform(**resolve_data_config({}   ,     model=_a))
			a_		:		List[Any]      				=      transform.transforms

			a_		:		Optional[Any]      				=      {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

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

			a_		:		Optional[int]      				=      prepare_img()
			a_		:		Optional[int]      				=      transform(_a).unsqueeze(0)
			a_		:		int      				=      processor(_a   ,     return_tensors="""pt""").pixel_values

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

			# verify logits
			with torch.no_grad():
						a_		:		List[str]      				=      model(_a)
						a_		:		Any      				=      outputs.logits

			print("""Predicted class:"""   ,     logits.argmax(-1).item())
			if base_model:
						a_		:		Optional[Any]      				=      timm_model.forward_features(_a)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(_a   ,     outputs.pooler_output   ,     atol=1e-3)
			else:
						a_		:		int      				=      timm_model(_a)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_a   ,     outputs.logits   ,     atol=1e-3)
			print("""Looks ok!""")

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

			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							:	int											=       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							:	Any											=       parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     str				=	np.array([len(lowercase_				) for t in data]				)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()

 def __getitem__(     self		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

 def __len__(     self		:       List[Any]				):
     return len(self.lengths				)

 def 						_snake_case					(     self		:       Tuple				):
     assert len(self.token_ids				) == len(self.lengths				)
     assert all(self.lengths[i] == len(self.token_ids[i]				) for i in range(len(self.lengths				)				)				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

     def divide_chunks(lowercase_		:       Tuple  ,     lowercase_		:       Optional[Any]				):
         return [l[i : i + n] for i in range(0  ,     len(lowercase_				)  ,     lowercase_				)]

     snake_case_ :     Tuple				=	[]
     snake_case_ :     Any				=	[]
     if self.params.mlm:
         snake_case_,	snake_case_ :     Union[str, Any]				=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
         snake_case_,	snake_case_ :     Dict				=	self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

     for seq_, len_ in zip(self.token_ids  ,     self.lengths				):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
             new_tok_ids.append(seq_				)
             new_lengths.append(len_				)
         else:
             snake_case_ :     Any				=	[]
             for sub_s in divide_chunks(seq_  ,     max_len - 2				):
                 if sub_s[0] != cls_id:
                     snake_case_ :     Dict				=	np.insert(lowercase_  ,     0  ,     lowercase_				)
                 if sub_s[-1] != sep_id:
                     snake_case_ :     Tuple				=	np.insert(lowercase_  ,     len(lowercase_				)  ,     lowercase_				)
                 assert len(lowercase_				) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(lowercase_				)

             new_tok_ids.extend(lowercase_				)
             new_lengths.extend([len(lowercase_				) for l in sub_seqs]				)

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     List[Any]				=	len(self				)
     snake_case_ :     List[str]				=	self.lengths > 11
     snake_case_ :     Dict				=	self.token_ids[indices]
     snake_case_ :     Dict				=	self.lengths[indices]
     snake_case_ :     str				=	len(self				)
     logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."				)

 def 						_snake_case					(     self		:       Tuple				):
     if "unk_token" not in self.params.special_tok_ids:
         return
     else:
         snake_case_ :     str				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     str				=	len(self				)
     snake_case_ :     int				=	np.array([np.count_nonzero(a == unk_token_id				) for a in self.token_ids]				)
     snake_case_ :     str				=	(unk_occs / self.lengths) < 0.5
     snake_case_ :     Optional[Any]				=	self.token_ids[indices]
     snake_case_ :     Optional[int]				=	self.lengths[indices]
     snake_case_ :     Dict				=	len(self				)
     logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."				)

 def 						_snake_case					(     self		:       Dict				):
     if not self.params.is_master:
         return
     logger.info(f"{len(self				)} sequences"				)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

     # Pad token ids
     if self.params.mlm:
         snake_case_ :     Tuple				=	self.params.special_tok_ids['''pad_token''']
     else:
         snake_case_ :     Dict				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     Any				=	[list(t.astype(lowercase_				)				) + [pad_idx] * (max_seq_len_ - len(lowercase_				)) for t in token_ids]
     assert len(tk_				) == len(lowercase_				)
     assert all(len(lowercase_				) == max_seq_len_ for t in tk_				)

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





