

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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case							:	str											=       logging.get_logger(__name__)


def  _UpperCAmelCase							(		a__   ,     a__=False):

			'''simple docstring'''



			a_		:		Dict      				=      []
			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"""),
						        ("""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_		:		List[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"""),
						    ])

			return rename_keys


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__=False):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						if base_model:
									a_		:		Tuple      				=      """"""
						else:
									a_		:		Any      				=      """vit."""
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_		:		Dict      				=      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_		:		List[Any]      				=      in_proj_bias[: config.hidden_size]
						a_		:		Optional[int]      				=      in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_		:		Any      				=      in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_		:		Union[str, Any]      				=      in_proj_weight[
						    -config.hidden_size :, :
						]
						a_		:		Dict      				=      in_proj_bias[-config.hidden_size :]


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Tuple      				=      ["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(A__   ,     A__)


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		List[str]      				=      dct.pop(A__)
			a_		:		Tuple      				=      val


def  _UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_		:		str      				=      Image.open(requests.get(A__   ,     stream=A__).raw)
			return im


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

			'''simple docstring'''



			a_		:		int      				=      ViTConfig()
			a_		:		int      				=      False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						a_		:		int      				=      True
						a_		:		List[str]      				=      int(vit_name[-1_2:-1_0])
						a_		:		List[Any]      				=      int(vit_name[-9:-6])
			else:
						a_		:		Dict      				=      1_0_0_0
						a_		:		Dict      				=      """huggingface/label-files"""
						a_		:		Optional[int]      				=      """imagenet-1k-id2label.json"""
						a_		:		Optional[int]      				=      json.load(open(hf_hub_download(A__   ,     A__   ,     repo_type="""dataset""")   ,     """r"""))
						a_		:		Optional[int]      				=      {int(A__): v for k, v in idalabel.items()}
						a_		:		Optional[int]      				=      idalabel
						a_		:		List[str]      				=      {v: k for k, v in idalabel.items()}
						a_		:		Optional[int]      				=      int(vit_name[-6:-4])
						a_		:		List[Any]      				=      int(vit_name[-3:])
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("""tiny"""):
									a_		:		List[Any]      				=      1_9_2
									a_		:		int      				=      7_6_8
									a_		:		Optional[Any]      				=      1_2
									a_		:		List[Any]      				=      3
						elif vit_name[9:].startswith("""small"""):
									a_		:		str      				=      3_8_4
									a_		:		Any      				=      1_5_3_6
									a_		:		Optional[int]      				=      1_2
									a_		:		Optional[int]      				=      6
						else:
									pass
			else:
						if vit_name[4:].startswith("""small"""):
									a_		:		Any      				=      7_6_8
									a_		:		Dict      				=      2_3_0_4
									a_		:		Any      				=      8
									a_		:		int      				=      8
						elif vit_name[4:].startswith("""base"""):
									pass
						elif vit_name[4:].startswith("""large"""):
									a_		:		Union[str, Any]      				=      1_0_2_4
									a_		:		Union[str, Any]      				=      4_0_9_6
									a_		:		int      				=      2_4
									a_		:		Optional[Any]      				=      1_6
						elif vit_name[4:].startswith("""huge"""):
									a_		:		Optional[int]      				=      1_2_8_0
									a_		:		str      				=      5_1_2_0
									a_		:		Any      				=      3_2
									a_		:		Union[str, Any]      				=      1_6

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

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						a_		:		List[Any]      				=      ViTModel(A__).eval()
			else:
						a_		:		Any      				=      ViTForImageClassification(A__).eval()
			model.load_state_dict(A__)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						a_		:		Optional[Any]      				=      DeiTImageProcessor(size=config.image_size)
			else:
						a_		:		List[Any]      				=      ViTImageProcessor(size=config.image_size)
			a_		:		Any      				=      image_processor(images=prepare_img()   ,     return_tensors="""pt""")
			a_		:		int      				=      encoding["""pixel_values"""]
			a_		:		Union[str, Any]      				=      model(A__)

			if base_model:
						a_		:		List[str]      				=      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_		:		List[str]      				=      timm_model(A__)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(A__   ,     outputs.logits   ,     atol=1e-3)

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


if __name__ == "__main__":
	__snake_case							:	List[str]											=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--vit_name""",
	    default="""vit_base_patch16_224""",
	    type=str,
	    help="""Name of the 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."""
	)

	__snake_case							:	Tuple											=       parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

