


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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase					:       str			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a			,				__a=False      )	->   Dict:
      a__	:  Dict     =   []
      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'''deit.encoder.layer.{i}.layernorm_before.weight''')      )
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')      )
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')      )
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')      )
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')      )
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')      )
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')      )
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')      )
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')      )
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')      )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "deit.embeddings.cls_token"),
              ("dist_token", "deit.embeddings.distillation_token"),
              ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "deit.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 "deit" from all keys that start with "deit"
            a__	:  List[str]     =   [(pair[0], pair[1][4:]) if pair[1].startswith("deit"      ) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("norm.weight", "deit.layernorm.weight"),
                    ("norm.bias", "deit.layernorm.bias"),
                    ("head.weight", "cls_classifier.weight"),
                    ("head.bias", "cls_classifier.bias"),
                    ("head_dist.weight", "distillation_classifier.weight"),
                    ("head_dist.bias", "distillation_classifier.bias"),
                ]      )

      return rename_keys



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



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   int:
      a__	:  List[str]     =   dct.pop(__a      )
      a__	:  List[Any]     =   val



def UpperCamelCase_				(				)	->   Optional[Any]:
      a__	:  str     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  Dict     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )
      return im



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a      )	->   List[str]:
      a__	:  str     =   DeiTConfig()
      # all deit models have fine-tuned heads
      a__	:  Tuple     =   False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      a__	:  Optional[Any]     =   1_000
      a__	:  List[str]     =   "huggingface/label-files"
      a__	:  int     =   "imagenet-1k-id2label.json"
      a__	:  List[str]     =   json.load(open(hf_hub_download(__a			,				__a			,				repo_type="dataset"      )			,				"r"      )      )
      a__	:  Union[str, Any]     =   {int(__a      ): v for k, v in idalabel.items()}
      a__	:  Optional[Any]     =   idalabel
      a__	:  List[str]     =   {v: k for k, v in idalabel.items()}
      a__	:  Optional[Any]     =   int(deit_name[-6:-4]      )
      a__	:  Tuple     =   int(deit_name[-3:]      )
      # size of the architecture
      if deit_name[9:].startswith("tiny"      ):
            a__	:  Dict     =   192
            a__	:  Union[str, Any]     =   768
            a__	:  List[str]     =   12
            a__	:  Union[str, Any]     =   3
      elif deit_name[9:].startswith("small"      ):
            a__	:  int     =   384
            a__	:  int     =   1_536
            a__	:  Optional[int]     =   12
            a__	:  int     =   6
      if deit_name[9:].startswith("base"      ):
            pass
      elif deit_name[4:].startswith("large"      ):
            a__	:  List[str]     =   1_024
            a__	:  List[str]     =   4_096
            a__	:  int     =   24
            a__	:  Any     =   16

      # load original model from timm
      a__	:  List[Any]     =   timm.create_model(__a			,				pretrained=__a      )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      a__	:  Dict     =   timm_model.state_dict()
      a__	:  Optional[Any]     =   create_rename_keys(__a			,				__a      )
      for src, dest in rename_keys:
            rename_key(__a			,				__a			,				__a      )
      read_in_q_k_v(__a			,				__a			,				__a      )

      # load HuggingFace model
      a__	:  Optional[int]     =   DeiTForImageClassificationWithTeacher(__a      ).eval()
      model.load_state_dict(__a      )

      # Check outputs on an image, prepared by DeiTImageProcessor
      a__	:  int     =   int(
          (256 / 224) * config.image_size      )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      a__	:  Tuple     =   DeiTImageProcessor(size=__a			,				crop_size=config.image_size      )
      a__	:  Optional[int]     =   image_processor(images=prepare_img()			,				return_tensors="pt"      )
      a__	:  Optional[int]     =   encoding["pixel_values"]
      a__	:  Any     =   model(__a      )

      a__	:  List[str]     =   timm_model(__a      )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__a			,				outputs.logits			,				atol=1e-3      )

      Path(__a      ).mkdir(exist_ok=__a      )
      print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(__a      )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(__a      )


if __name__ == "__main__":
    UpperCamelCase					:       Tuple			    =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--deit_name""",
        default="""vit_deit_base_distilled_patch16_224""",
        type=str,
        help="""Name of the DeiT 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					:       Optional[int]			    =     parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''down_block_types''': down_block_types,
			    '''block_out_channels''': block_out_channels,
			    '''up_block_types''': up_block_types,
			    '''layers_per_block''': 1,
			    '''use_timestep_embedding''': True,
			    '''out_block_type''': '''OutConv1DBlock''',
			    '''norm_num_groups''': 8,
			    '''downsample_each_block''': False,
			    '''in_channels''': 14,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''sample_size''': 6_5536,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)
			SCREAMING_SNAKE_CASE     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)
			hf_value_function.load_state_dict(a	)

			torch.save(hf_value_function.state_dict()		,						F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""	)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""		,						'''w'''	) as f:
						json.dump(a		,						a	)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''in_channels''': 14,
			    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
			    '''up_block_types''': (),
			    '''out_block_type''': '''ValueFunction''',
			    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
			    '''block_out_channels''': (32, 64, 128, 256),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 6_5536,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''use_timestep_embedding''': True,
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''norm_num_groups''': 8,
			    '''act_fn''': '''mish''',
			}

			SCREAMING_SNAKE_CASE     						= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''	)
			SCREAMING_SNAKE_CASE     						= model
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)

			SCREAMING_SNAKE_CASE     						= dict(zip(state_dict.keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)

			hf_value_function.load_state_dict(a	)

			torch.save(hf_value_function.state_dict()		,						'''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''	)
			with open('''hub/hopper-medium-v2/value_function/config.json'''		,						'''w'''	) as f:
						json.dump(a		,						a	)


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()