"""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       =					logging.get_logger(__name__)



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase			,       __UpperCAmelCase=False					)    ->    Optional[int]:
   lowercase__:  Optional[int]   = []

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



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase					)    ->    Union[str, Any]:
   lowercase__:  int   = ["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(lowercase_			,       lowercase_					)



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase			,       __UpperCAmelCase			,       __UpperCAmelCase					)    ->    List[str]:
   lowercase__:  Tuple   = dct.pop(lowercase_					)
   lowercase__:  List[str]   = val



def SCREAMING_SNAKE_CASE__     (  )    ->    str:
   lowercase__:  List[str]   = """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowercase__:  Dict   = Image.open(requests.get(lowercase_			,       stream=lowercase_					).raw					)
   return im



@torch.no_grad()
def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase			,       __UpperCAmelCase			,       __UpperCAmelCase=False					)    ->    int:
   lowercase__:  str   = BitConfig(
       global_padding='''same'''			,       layer_type='''bottleneck'''			,       depths=(3, 4, 9)			,       out_features=['''stage3''']			,       embedding_dynamic_padding=lowercase_			,       )
   lowercase__:  Any   = ViTHybridConfig(backbone_config=lowercase_			,       image_size=3_8_4			,       num_labels=1_0_0_0					)
   lowercase__:  List[Any]   = False

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

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

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

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      lowercase__:  str   = ViTHybridModel(lowercase_					).eval()
   else:
      lowercase__:  Any   = ViTHybridForImageClassification(lowercase_					).eval()
   model.load_state_dict(lowercase_					)

   # create image processor
   lowercase__:  List[Any]   = create_transform(**resolve_data_config({}			,       model=lowercase_					)					)
   lowercase__:  Optional[Any]   = transform.transforms

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

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

   lowercase__:  Optional[int]   = prepare_img()
   lowercase__:  int   = transform(lowercase_					).unsqueeze(0					)
   lowercase__:  Optional[Any]   = processor(lowercase_			,       return_tensors='''pt'''					).pixel_values

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

   # verify logits
   with torch.no_grad():
      lowercase__:  Dict   = model(lowercase_					)
      lowercase__:  Tuple   = outputs.logits

   print('''Predicted class:'''			,       logits.argmax(-1					).item()					)
   if base_model:
      lowercase__:  int   = 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:
      lowercase__:  Dict   = timm_model(lowercase_					)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowercase_			,       outputs.logits			,       atol=1e-3					)
   print('''Looks ok!'''					)

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

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



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



@experimental
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Any       , lowercase_:					str       , lowercase_:					str       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Union[str, Any]   )	-> Union[str, Any]:
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	return _map_with_joblib(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[Any]       , lowercase_:					Tuple       , lowercase_:					Tuple       , lowercase_:					List[str]       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Tuple:
	A__				:       Union[str, Any]	  =						num_proc if num_proc <= len(lowercase_   ) else len(lowercase_   )
	A__				:       str	  =						[]  # We organize the splits ourselve (contiguous splits)
	for index in range(lowercase_   ):
		A__				:       Tuple	  =						len(lowercase_   ) // num_proc
		A__				:       Tuple	  =						len(lowercase_   ) % num_proc
		A__				:       Optional[Any]	  =						div * index + min(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

	if len(lowercase_   ) != sum(len(i[1]   ) for i in split_kwds   ):
		raise ValueError(
		    f"""Error dividing inputs iterable among processes. """
		    f"""Total number of objects {len(lowercase_   )}, """
		    f"""length: {sum(len(i[1]   ) for i in split_kwds   )}"""   )

	logger.info(
	    f"""Spawning {num_proc} processes for {len(lowercase_   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}"""   )
	A__  ,  A__				:       Optional[int]	  =						None, None
	if not disable_tqdm:
		A__  ,  A__				:       List[str]	  =						(RLock(),), tqdm.set_lock
	with Pool(lowercase_       , initargs=lowercase_       , initializer=lowercase_   ) as pool:
		A__				:       Optional[Any]	  =						pool.map(lowercase_       , lowercase_   )
	logger.info(f"""Finished {num_proc} processes"""   )
	A__				:       Tuple	  =						[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"""Unpacked {len(lowercase_   )} objects"""   )

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
	# and it requires monkey-patching joblib internal classes which is subject to change
	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name       , n_jobs=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		A__				:       Optional[Any]	  =						None



