
'''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()
snake_case_			:	int 	=       logging.get_logger(__name__)



def 			__snake_case  (     _UpperCAmelCase       :							int,   _UpperCAmelCase       :							Optional[int]=False):
   UpperCamelCase	  =	[]
   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"
      UpperCamelCase	  =	[(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 			__snake_case  (     _UpperCAmelCase       :							Union[str, Any],   _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							Dict=False):


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



def 			__snake_case  (     _UpperCAmelCase       :							Any):
   UpperCamelCase	  =	['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(_UpperCAmelCase,   _UpperCAmelCase)



def 			__snake_case  (     _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							Dict):
   UpperCamelCase	  =	dct.pop(_UpperCAmelCase)
   UpperCamelCase	  =	val



def 			__snake_case  (     ):
   UpperCamelCase	  =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCamelCase	  =	Image.open(requests.get(_UpperCAmelCase,   stream=_UpperCAmelCase).raw)
   return im



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

    # load original model from timm
   UpperCamelCase	  =	timm.create_model(_UpperCAmelCase,   pretrained=_UpperCAmelCase)
   timm_model.eval()

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

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

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

   if base_model:
      UpperCamelCase	  =	timm_model.forward_features(_UpperCAmelCase)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(_UpperCAmelCase,   outputs.pooler_output,   atol=1E-3)
   else:
      UpperCamelCase	  =	timm_model(_UpperCAmelCase)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_UpperCAmelCase,   outputs.logits,   atol=1E-3)

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


if __name__ == "__main__":
   snake_case_			:	str 	=       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.'
   )

   snake_case_			:	List[Any] 	=       parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
