




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()
UpperCamelCase								=	logging.get_logger(__name__)



def 				_A  (		lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   int=False     ):
     """simple docstring"""

     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 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   List[str]=False     ):
     """simple docstring"""

     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 				_A  (		lowerCAmelCase_	:   Optional[int]     ):
     """simple docstring"""

     lowerCAmelCase__            =					["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(lowerCAmelCase_  ,							lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   str     ):
     """simple docstring"""

     lowerCAmelCase__            =					dct.pop(lowerCAmelCase_     )
     lowerCAmelCase__            =					val



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					"http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase__            =					Image.open(requests.get(lowerCAmelCase_  ,							stream=lowerCAmelCase_     ).raw     )
     return im



@torch.no_grad()
def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   int=False     ):
     """simple docstring"""

     lowerCAmelCase__            =					BitConfig(
         global_padding="same"  ,							layer_type="bottleneck"  ,							depths=(3, 4, 9)  ,							out_features=["stage3"]  ,							embedding_dynamic_padding=lowerCAmelCase_  ,							)
     lowerCAmelCase__            =					ViTHybridConfig(backbone_config=lowerCAmelCase_  ,							image_size=384  ,							num_labels=1000     )
     lowerCAmelCase__            =					False

     # load original model from timm
     lowerCAmelCase__            =					timm.create_model(lowerCAmelCase_  ,							pretrained=lowerCAmelCase_     )
     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_(lowerCAmelCase_     )
     lowerCAmelCase__            =					create_rename_keys(lowerCAmelCase_  ,							lowerCAmelCase_     )
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )
     read_in_q_k_v(lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

     lowerCAmelCase__            =					"huggingface/label-files"
     lowerCAmelCase__            =					"imagenet-1k-id2label.json"
     lowerCAmelCase__            =					json.load(open(hf_hub_download(lowerCAmelCase_  ,							lowerCAmelCase_  ,							repo_type="dataset"     )  ,							"r"     )     )
     lowerCAmelCase__            =					{int(lowerCAmelCase_     ): 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(lowerCAmelCase_     ).eval()
     else:
          lowerCAmelCase__            =					ViTHybridForImageClassification(lowerCAmelCase_     ).eval()
     model.load_state_dict(lowerCAmelCase_     )

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

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

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

     lowerCAmelCase__            =					prepare_img()
     lowerCAmelCase__            =					transform(lowerCAmelCase_     ).unsqueeze(0     )
     lowerCAmelCase__            =					processor(lowerCAmelCase_  ,							return_tensors="pt"     ).pixel_values

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

     # verify logits
     with torch.no_grad():
          lowerCAmelCase__            =					model(lowerCAmelCase_     )
          lowerCAmelCase__            =					outputs.logits

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

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

     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__":
     UpperCamelCase								=	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.'
     )

     UpperCamelCase								=	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Any=7     ,					__UpperCAmelCase					:    Union[str, Any]=3     ,					__UpperCAmelCase					:    Any=1_8     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    List[Any]=4_0_0     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    int=None     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Any=True     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   size if size is not None else {"""shortest_edge""": 2_0}
						SCREAMING_SNAKE_CASE__										=   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   min_resolution
						SCREAMING_SNAKE_CASE__										=   max_resolution
						SCREAMING_SNAKE_CASE__										=   do_resize
						SCREAMING_SNAKE_CASE__										=   size
						SCREAMING_SNAKE_CASE__										=   do_center_crop
						SCREAMING_SNAKE_CASE__										=   crop_size
						SCREAMING_SNAKE_CASE__										=   do_flip_channel_order





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}





@require_torch
@require_vision
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				MobileViTImageProcessor if is_vision_available() else None

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   MobileViTImageProcessingTester(self							)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_resize"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""size"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_flip_channel_order"""							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 2_0}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 1_8, """width""": 1_8}							)

						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2     ,					crop_size=8_4							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 4_2}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 8_4, """width""": 8_4}							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					Image.Image							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					numpify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					np.ndarray							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					torchify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					torch.Tensor							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)


