



"""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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=False			) ->							Optional[int]:
       A         =					[]
       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"
              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'),
                  ]			)

       return rename_keys

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Union[str, Any]:
       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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Tuple:
       A         =					["""head.weight""", """head.bias"""]
       for k in ignore_keys:
              state_dict.pop(lowerCamelCase__				,      lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Tuple:
       A         =					dct.pop(lowerCamelCase__			)
       A         =					val

def   lowerCAmelCase__			(     ) ->							Tuple:
       A         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
       return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							List[str]:
       A         =					ViTConfig()
       A         =					False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              A         =					True
              A         =					int(vit_name[-12:-10]			)
              A         =					int(vit_name[-9:-6]			)
       else:
              A         =					1000
              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()}
              A         =					int(vit_name[-6:-4]			)
              A         =					int(vit_name[-3:]			)
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith('tiny'			):
                     A         =					192
                     A         =					768
                     A         =					12
                     A         =					3
              elif vit_name[9:].startswith('small'			):
                     A         =					384
                     A         =					1536
                     A         =					12
                     A         =					6
              else:
                     pass
       else:
              if vit_name[4:].startswith('small'			):
                     A         =					768
                     A         =					2304
                     A         =					8
                     A         =					8
              elif vit_name[4:].startswith('base'			):
                     pass
              elif vit_name[4:].startswith('large'			):
                     A         =					1024
                     A         =					4096
                     A         =					24
                     A         =					16
              elif vit_name[4:].startswith('huge'			):
                     A         =					1280
                     A         =					5120
                     A         =					32
                     A         =					16

    # 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__			)

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              A         =					ViTModel(lowerCamelCase__			).eval()
       else:
              A         =					ViTForImageClassification(lowerCamelCase__			).eval()
       model.load_state_dict(lowerCamelCase__			)

       # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
       if "deit" in vit_name:
              A         =					DeiTImageProcessor(size=config.image_size			)
       else:
              A         =					ViTImageProcessor(size=config.image_size			)
       A         =					image_processor(images=prepare_img()				,      return_tensors='pt'			)
       A         =					encoding["""pixel_values"""]
       A         =					model(lowerCamelCase__			)

       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			)

       Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
       print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
       model.save_pretrained(lowerCamelCase__			)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
       image_processor.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
       A    =			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.'
       )

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








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					state_dim
									A         =					act_dim
									A         =					hidden_size
									A         =					max_ep_len
									A         =					action_tanh
									A         =					vocab_size
									A         =					n_positions
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					scale_attn_weights
									A         =					use_cache
									A         =					scale_attn_by_inverse_layer_idx
									A         =					reorder_and_upcast_attn

									A         =					bos_token_id
									A         =					eos_token_id

									super().__init__(bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)



