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()
_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)





def        lowercase(     UpperCamelCase_				, UpperCamelCase_=False    )						->     List[str]:







 '''simple docstring'''
 UpperCamelCase          =	[]
 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"
  UpperCamelCase          =	[(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        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_=False    )						->     str:







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





def        lowercase(     UpperCamelCase_    )						->     List[str]:







 '''simple docstring'''
 UpperCamelCase          =	['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(lowercase__				, lowercase__    )





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     Tuple:







 '''simple docstring'''
 UpperCamelCase          =	dct.pop(lowercase__    )
 UpperCamelCase          =	val





def        lowercase(     )						->     str:







 '''simple docstring'''
 UpperCamelCase          =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase          =	Image.open(requests.get(lowercase__				, stream=lowercase__    ).raw    )
 return im





@torch.no_grad()
def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     List[str]:







 '''simple docstring'''
 UpperCamelCase          =	ViTConfig()
 UpperCamelCase          =	False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  UpperCamelCase          =	True
  UpperCamelCase          =	int(vit_name[-12:-10]    )
  UpperCamelCase          =	int(vit_name[-9:-6]    )
 else:
  UpperCamelCase          =	1000
  UpperCamelCase          =	'''huggingface/label-files'''
  UpperCamelCase          =	'''imagenet-1k-id2label.json'''
  UpperCamelCase          =	json.load(open(hf_hub_download(lowercase__				, lowercase__				, repo_type="""dataset"""    )				, """r"""    )    )
  UpperCamelCase          =	{int(lowercase__    ): v for k, v in idalabel.items()}
  UpperCamelCase          =	idalabel
  UpperCamelCase          =	{v: k for k, v in idalabel.items()}
  UpperCamelCase          =	int(vit_name[-6:-4]    )
  UpperCamelCase          =	int(vit_name[-3:]    )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("""tiny"""    ):
   UpperCamelCase          =	192
   UpperCamelCase          =	768
   UpperCamelCase          =	12
   UpperCamelCase          =	3
  elif vit_name[9:].startswith("""small"""    ):
   UpperCamelCase          =	384
   UpperCamelCase          =	1536
   UpperCamelCase          =	12
   UpperCamelCase          =	6
  else:
   pass
 else:
  if vit_name[4:].startswith("""small"""    ):
   UpperCamelCase          =	768
   UpperCamelCase          =	2304
   UpperCamelCase          =	8
   UpperCamelCase          =	8
  elif vit_name[4:].startswith("""base"""    ):
   pass
  elif vit_name[4:].startswith("""large"""    ):
   UpperCamelCase          =	1024
   UpperCamelCase          =	4096
   UpperCamelCase          =	24
   UpperCamelCase          =	16
  elif vit_name[4:].startswith("""huge"""    ):
   UpperCamelCase          =	1280
   UpperCamelCase          =	5120
   UpperCamelCase          =	32
   UpperCamelCase          =	16

    # load original model from timm
 UpperCamelCase          =	timm.create_model(lowercase__				, pretrained=lowercase__    )
 timm_model.eval()

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  UpperCamelCase          =	ViTModel(lowercase__    ).eval()
 else:
  UpperCamelCase          =	ViTForImageClassification(lowercase__    ).eval()
 model.load_state_dict(lowercase__    )

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  UpperCamelCase          =	DeiTImageProcessor(size=config.image_size    )
 else:
  UpperCamelCase          =	ViTImageProcessor(size=config.image_size    )
 UpperCamelCase          =	image_processor(images=prepare_img()				, return_tensors="""pt"""    )
 UpperCamelCase          =	encoding['''pixel_values''']
 UpperCamelCase          =	model(lowercase__    )

 if base_model:
  UpperCamelCase          =	timm_model.forward_features(lowercase__    )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(lowercase__				, outputs.pooler_output				, atol=1E-3    )
 else:
  UpperCamelCase          =	timm_model(lowercase__    )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(lowercase__				, outputs.logits				, atol=1E-3    )

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE      						=	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."""
     )

     _SCREAMING_SNAKE_CASE      						=	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


_UpperCamelCase 			=		logging.get_logger(__name__)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      def constraint_to_multiple_of(lowercase__   ,	lowercase__   ,	lowercase__=0   ,	lowercase__=None						):
            __lowerCAmelCase					:						int					=					round(val / multiple						) * multiple

            if max_val is not None and x > max_val:
                  __lowerCAmelCase					:						Optional[int]					=					math.floor(val / multiple						) * multiple

            if x < min_val:
                  __lowerCAmelCase					:						Any					=					math.ceil(val / multiple						) * multiple

            return x

      __lowerCAmelCase					:						Dict					=					(output_size, output_size) if isinstance(lowercase__   ,	lowercase__						) else output_size

      __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					get_image_size(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						int					=					output_size

      # determine new height and width
      __lowerCAmelCase					:						Optional[Any]					=					output_height / input_height
      __lowerCAmelCase					:						List[Any]					=					output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width						) < abs(1 - scale_height						):
                  # fit width
                  __lowerCAmelCase					:						str					=					scale_width
            else:
                  # fit height
                  __lowerCAmelCase					:						str					=					scale_height

      __lowerCAmelCase					:						Any					=					constraint_to_multiple_of(scale_height * input_height   ,	multiple=lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					constraint_to_multiple_of(scale_width * input_width   ,	multiple=lowercase__						)

      return (new_height, new_width)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  ["""pixel_values"""]
 def __init__(							self      ,							A_ = True      ,							A_ = None      ,							A_ = PILImageResampling.BILINEAR      ,							A_ = False      ,							A_ = 1      ,							A_ = True      ,							A_ = 1 / 255      ,							A_ = True      ,							A_ = None      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       super().__init__(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					size if size is not None else {'''height''': 384, '''width''': 384}
       __lowerCAmelCase					:						Dict					=					get_size_dict(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					do_resize
       __lowerCAmelCase					:						int					=					size
       __lowerCAmelCase					:						Dict					=					keep_aspect_ratio
       __lowerCAmelCase					:						List[Any]					=					ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample
       __lowerCAmelCase					:						Dict					=					do_rescale
       __lowerCAmelCase					:						Any					=					rescale_factor
       __lowerCAmelCase					:						List[Any]					=					do_normalize
       __lowerCAmelCase					:						Optional[int]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __lowerCAmelCase					:						Optional[int]					=					image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = False      ,							A_ = 1      ,							A_ = PILImageResampling.BICUBIC      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					get_size_dict(A_					)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
       __lowerCAmelCase					:						Union[str, Any]					=					get_resize_output_image_size(
           A_      ,							output_size=(size['''height'''], size['''width'''])      ,							keep_aspect_ratio=A_      ,							multiple=A_      ,							)
       return resize(A_      ,							size=A_      ,							resample=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->Dict:



       '''simple docstring'''





       return rescale(A_      ,							scale=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       return normalize(A_      ,							mean=A_      ,							std=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = ChannelDimension.FIRST      ,							**A_      ,							)					->PIL.Image.Image:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					do_resize if do_resize is not None else self.do_resize
       __lowerCAmelCase					:						Optional[int]					=					size if size is not None else self.size
       __lowerCAmelCase					:						Union[str, Any]					=					get_size_dict(A_					)
       __lowerCAmelCase					:						List[Any]					=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       __lowerCAmelCase					:						Optional[int]					=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample if resample is not None else self.resample
       __lowerCAmelCase					:						Optional[int]					=					do_rescale if do_rescale is not None else self.do_rescale
       __lowerCAmelCase					:						Union[str, Any]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowerCAmelCase					:						Tuple					=					do_normalize if do_normalize is not None else self.do_normalize
       __lowerCAmelCase					:						str					=					image_mean if image_mean is not None else self.image_mean
       __lowerCAmelCase					:						Optional[Any]					=					image_std if image_std is not None else self.image_std

       __lowerCAmelCase					:						Optional[Any]					=					make_list_of_images(A_					)

       if not valid_images(A_					):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

       if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

       # All transformations expect numpy arrays.
       __lowerCAmelCase					:						Any					=					[to_numpy_array(A_					) for image in images]

       if do_resize:
             __lowerCAmelCase					:						Optional[Any]					=					[self.resize(image=A_      ,							size=A_      ,							resample=A_					) for image in images]

       if do_rescale:
             __lowerCAmelCase					:						Tuple					=					[self.rescale(image=A_      ,							scale=A_					) for image in images]

       if do_normalize:
             __lowerCAmelCase					:						str					=					[self.normalize(image=A_      ,							mean=A_      ,							std=A_					) for image in images]

       __lowerCAmelCase					:						Union[str, Any]					=					[to_channel_dimension_format(A_      ,							A_					) for image in images]

       __lowerCAmelCase					:						Dict					=					{'''pixel_values''': images}
       return BatchFeature(data=A_      ,							tensor_type=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(A_					) != len(A_					):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''					)

             if is_torch_tensor(A_					):
                   __lowerCAmelCase					:						Optional[int]					=					target_sizes.numpy()

             __lowerCAmelCase					:						List[str]					=					[]

             for idx in range(len(A_					)					):
                   __lowerCAmelCase					:						Any					=					torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0					)      ,							size=target_sizes[idx]      ,							mode='''bilinear'''      ,							align_corners=A_					)
                   __lowerCAmelCase					:						str					=					resized_logits[0].argmax(dim=0					)
                   semantic_segmentation.append(A_					)
       else:
             __lowerCAmelCase					:						Any					=					logits.argmax(dim=1					)
             __lowerCAmelCase					:						List[Any]					=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

       return semantic_segmentation


