





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()
UpperCAmelCase_							=				logging.get_logger(__name__)





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

							"""simple docstring"""



							lowercase_	:   int  				=     []
							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"
														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"""),
														    ]				)

							return rename_keys





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=False				)				-> str:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(lowercase				,       lowercase				)





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     dct.pop(lowercase				)
							lowercase_	:   Tuple  				=     val





def  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



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





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				)				-> Any:

							"""simple docstring"""



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

    # 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_	:   str  				=     timm_model.state_dict()
							if base_model:
														remove_classification_head_(lowercase				)
							lowercase_	:   Tuple  				=     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":
														lowercase_	:   int  				=     ViTModel(lowercase				).eval()
							else:
														lowercase_	:   int  				=     ViTForImageClassification(lowercase				).eval()
							model.load_state_dict(lowercase				)

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														lowercase_	:   str  				=     DeiTImageProcessor(size=config.image_size				)
							else:
														lowercase_	:   Union[str, Any]  				=     ViTImageProcessor(size=config.image_size				)
							lowercase_	:   Any  				=     image_processor(images=prepare_img()				,       return_tensors="""pt"""				)
							lowercase_	:   Optional[Any]  				=     encoding["""pixel_values"""]
							lowercase_	:   Dict  				=     model(lowercase				)

							if base_model:
														lowercase_	:   Union[str, Any]  				=     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_	:   int  				=     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__":
							UpperCAmelCase_							=				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."""
							)

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

"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_            =    [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE_            =    [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       len(lowerCAmelCase			)

    for i in range(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for j in range(i + 1			,					lowerCAmelCase			):
            if arr[i] < arr[j]:
                UpperCAmelCase          =       arr[j]
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    for i, outer in enumerate(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for inner in arr[i + 1 :]:
            if outer < inner:
                UpperCAmelCase          =       inner
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       []
    UpperCAmelCase          =       [-1] * arr_size

    for index in reversed(range(lowerCAmelCase			)			):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            UpperCAmelCase          =       stack[-1]
        stack.append(arr[index]			)
    return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   SCREAMING_SNAKE_CASE_            =    (
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )



