

"""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()
UpperCAmelCase													=logging.get_logger(__name__)



def   _A					(	_a	:						List[Any]     ,   _a	:						str=False				):



      """simple docstring"""






      A				  =		[]

      # 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				  =		[(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					(	_a	:						Union[str, Any]     ,   _a	:						str     ,   _a	:						str=False				):



      """simple docstring"""






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



def   _A					(	_a	:						str				):



      """simple docstring"""






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



def   _A					(	_a	:						Dict     ,   _a	:						Any     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		dct.pop(lowerCamelCase_				)
      A				  =		val



def   _A					(	):



      """simple docstring"""






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



@torch.no_grad()
def   _A					(	_a	:						str     ,   _a	:						int     ,   _a	:						Dict=False				):



      """simple docstring"""






      A				  =		BitConfig(
          global_padding="""same"""     ,   layer_type="""bottleneck"""     ,   depths=(3, 4, 9)     ,   out_features=["""stage3"""]     ,   embedding_dynamic_padding=lowerCamelCase_     ,   )
      A				  =		ViTHybridConfig(backbone_config=lowerCamelCase_     ,   image_size=3_8_4     ,   num_labels=1_0_0_0				)
      A				  =		False

      # load original model from timm
      A				  =		timm.create_model(lowerCamelCase_     ,   pretrained=lowerCamelCase_				)
      timm_model.eval()

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

      A				  =		"""huggingface/label-files"""
      A				  =		"""imagenet-1k-id2label.json"""
      A				  =		json.load(open(hf_hub_download(lowerCamelCase_     ,   lowerCamelCase_     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
      A				  =		{int(lowerCamelCase_				): v for k, v in idalabel.items()}
      A				  =		idalabel
      A				  =		{v: k for k, v in idalabel.items()}

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            A				  =		ViTHybridModel(lowerCamelCase_				).eval()
      else:
            A				  =		ViTHybridForImageClassification(lowerCamelCase_				).eval()
      model.load_state_dict(lowerCamelCase_				)

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

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

      A				  =		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()     ,   )

      A				  =		prepare_img()
      A				  =		transform(lowerCamelCase_				).unsqueeze(0				)
      A				  =		processor(lowerCamelCase_     ,   return_tensors="""pt"""				).pixel_values

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

      # verify logits
      with torch.no_grad():
            A				  =		model(lowerCamelCase_				)
            A				  =		outputs.logits

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



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			num - 1
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0

		while s % 2 == 0:
				SCREAMING_SNAKE_CASE :				List[str]		=			s // 2
				t += 1

		for _ in range(5	):
				SCREAMING_SNAKE_CASE :				List[Any]		=			random.randrange(2			,			num - 1	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			pow(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				if v != 1:
						SCREAMING_SNAKE_CASE :				Tuple		=			0
						while v != (num - 1):
								if i == t - 1:
										return False
								else:
										SCREAMING_SNAKE_CASE :				Any		=			i + 1
										SCREAMING_SNAKE_CASE :				str		=			(v**2) % num
		return True



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if num < 2:
				return False

		SCREAMING_SNAKE_CASE :				List[str]		=			[
		    2,
		    3,
		    5,
		    7,
		    11,
		    13,
		    17,
		    19,
		    23,
		    29,
		    31,
		    37,
		    41,
		    43,
		    47,
		    53,
		    59,
		    61,
		    67,
		    71,
		    73,
		    79,
		    83,
		    89,
		    97,
		    1_01,
		    1_03,
		    1_07,
		    1_09,
		    1_13,
		    1_27,
		    1_31,
		    1_37,
		    1_39,
		    1_49,
		    1_51,
		    1_57,
		    1_63,
		    1_67,
		    1_73,
		    1_79,
		    1_81,
		    1_91,
		    1_93,
		    1_97,
		    1_99,
		    2_11,
		    2_23,
		    2_27,
		    2_29,
		    2_33,
		    2_39,
		    2_41,
		    2_51,
		    2_57,
		    2_63,
		    2_69,
		    2_71,
		    2_77,
		    2_81,
		    2_83,
		    2_93,
		    3_07,
		    3_11,
		    3_13,
		    3_17,
		    3_31,
		    3_37,
		    3_47,
		    3_49,
		    3_53,
		    3_59,
		    3_67,
		    3_73,
		    3_79,
		    3_83,
		    3_89,
		    3_97,
		    4_01,
		    4_09,
		    4_19,
		    4_21,
		    4_31,
		    4_33,
		    4_39,
		    4_43,
		    4_49,
		    4_57,
		    4_61,
		    4_63,
		    4_67,
		    4_79,
		    4_87,
		    4_91,
		    4_99,
		    5_03,
		    5_09,
		    5_21,
		    5_23,
		    5_41,
		    5_47,
		    5_57,
		    5_63,
		    5_69,
		    5_71,
		    5_77,
		    5_87,
		    5_93,
		    5_99,
		    6_01,
		    6_07,
		    6_13,
		    6_17,
		    6_19,
		    6_31,
		    6_41,
		    6_43,
		    6_47,
		    6_53,
		    6_59,
		    6_61,
		    6_73,
		    6_77,
		    6_83,
		    6_91,
		    7_01,
		    7_09,
		    7_19,
		    7_27,
		    7_33,
		    7_39,
		    7_43,
		    7_51,
		    7_57,
		    7_61,
		    7_69,
		    7_73,
		    7_87,
		    7_97,
		    8_09,
		    8_11,
		    8_21,
		    8_23,
		    8_27,
		    8_29,
		    8_39,
		    8_53,
		    8_57,
		    8_59,
		    8_63,
		    8_77,
		    8_81,
		    8_83,
		    8_87,
		    9_07,
		    9_11,
		    9_19,
		    9_29,
		    9_37,
		    9_41,
		    9_47,
		    9_53,
		    9_67,
		    9_71,
		    9_77,
		    9_83,
		    9_91,
		    9_97,
		]

		if num in low_primes:
				return True

		for prime in low_primes:
				if (num % prime) == 0:
						return False

		return rabin_miller(lowerCamelCase_	)



def 		__A		(  lowerCamelCase_ = 10_24	):


		"""simple docstring"""



		while True:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			random.randrange(2 ** (keysize - 1)			,			2 ** (keysize)	)
				if is_prime_low_num(lowerCamelCase_	):
						return num


if __name__ == "__main__":
							__UpperCAmelCase            =       generate_large_prime()
							print(("""Prime number:""", num))
							print(("""is_prime_low_num:""", is_prime_low_num(num)))



