



"""simple docstring"""
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()
A:   Optional[Any]								=		logging.get_logger(__name__)

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Union[str, Any]=False						):
 UpperCAmelCase	:					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"
  UpperCAmelCase	:					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       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Dict=False						):


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

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

def       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Union[str, Any]						):
 UpperCAmelCase	:					int	=	dct.pop(_snake_case						)
 UpperCAmelCase	:					List[Any]	=	val

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

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		int=False						):
 UpperCAmelCase	:					List[str]	=	BitConfig(
     global_padding="""same"""			,		layer_type="""bottleneck"""			,		depths=(3, 4, 9)			,		out_features=["""stage3"""]			,		embedding_dynamic_padding=_snake_case			,		)
 UpperCAmelCase	:					List[str]	=	ViTHybridConfig(backbone_config=_snake_case			,		image_size=384			,		num_labels=1000						)
 UpperCAmelCase	:					str	=	False

 # load original model from timm
 UpperCAmelCase	:					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
 UpperCAmelCase	:					List[Any]	=	timm_model.state_dict()
 if base_model:
  remove_classification_head_(_snake_case						)
 UpperCAmelCase	:					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						)

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

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

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

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

 UpperCAmelCase	:					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()			,		)

 UpperCAmelCase	:					List[Any]	=	prepare_img()
 UpperCAmelCase	:					Any	=	transform(_snake_case						).unsqueeze(0						)
 UpperCAmelCase	:					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():
  UpperCAmelCase	:					Optional[int]	=	model(_snake_case						)
  UpperCAmelCase	:					List[str]	=	outputs.logits

 print("""Predicted class:"""			,		logits.argmax(-1						).item()						)
 if base_model:
  UpperCAmelCase	:					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:
  UpperCAmelCase	:					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__":
      A:   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."
      )

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


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class       _snake_case					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     hashlib.mda(image.tobytes()   )
	return m.hexdigest()[:10]






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.array(_snake_case   )
	__magic_name__		:		Optional[int]	   =     npimg.shape
	return {"hash": hashimage(_snake_case   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

				__magic_name__		:		str	   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Dict	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
				        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
				        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
				        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
				        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
				        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
				        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
				        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
				        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
				        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
				        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
				        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
				        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
				        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
				        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
				        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
				        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
				        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
				        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
				        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
				        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
				        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
				        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
				    ]						,   )
				# fmt: on







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

				__magic_name__		:		Tuple	   =     image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"						,   pred_iou_thresh=1						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Any	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				    ]						,   )
