



"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A:     Optional[Any]										=	logging.get_logger(__name__)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Tuple=False		):
	UpperCAmelCase			:      Any			=			[]
	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"
		UpperCAmelCase			:      Optional[int]			=			[(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    _snake_case		(		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Optional[Any]=False		):


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

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Tuple		):
	UpperCAmelCase			:      Optional[int]			=			dct.pop(__snake_case		)
	UpperCAmelCase			:      Dict			=			val

def    _snake_case		(		):
	UpperCAmelCase			:      Tuple			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase			:      Optional[int]			=			Image.open(requests.get(__snake_case    ,	stream=__snake_case		).raw		)
	return im

@torch.no_grad()
def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	List[Any]		):
	UpperCAmelCase			:      Optional[Any]			=			DeiTConfig()
	# all deit models have fine-tuned heads
	UpperCAmelCase			:      Tuple			=			False
	# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
	UpperCAmelCase			:      Optional[int]			=			1000
	UpperCAmelCase			:      Tuple			=			"huggingface/label-files"
	UpperCAmelCase			:      Union[str, Any]			=			"imagenet-1k-id2label.json"
	UpperCAmelCase			:      Dict			=			json.load(open(hf_hub_download(__snake_case    ,	__snake_case    ,	repo_type="""dataset"""		)    ,	"""r"""		)		)
	UpperCAmelCase			:      int			=			{int(__snake_case		): v for k, v in idalabel.items()}
	UpperCAmelCase			:      List[str]			=			idalabel
	UpperCAmelCase			:      Union[str, Any]			=			{v: k for k, v in idalabel.items()}
	UpperCAmelCase			:      int			=			int(deit_name[-6:-4]		)
	UpperCAmelCase			:      str			=			int(deit_name[-3:]		)
	# size of the architecture
	if deit_name[9:].startswith("""tiny"""		):
		UpperCAmelCase			:      Union[str, Any]			=			192
		UpperCAmelCase			:      Optional[Any]			=			768
		UpperCAmelCase			:      Tuple			=			12
		UpperCAmelCase			:      int			=			3
	elif deit_name[9:].startswith("""small"""		):
		UpperCAmelCase			:      Any			=			384
		UpperCAmelCase			:      Union[str, Any]			=			1536
		UpperCAmelCase			:      List[Any]			=			12
		UpperCAmelCase			:      Optional[Any]			=			6
	if deit_name[9:].startswith("""base"""		):
		pass
	elif deit_name[4:].startswith("""large"""		):
		UpperCAmelCase			:      Tuple			=			1024
		UpperCAmelCase			:      int			=			4096
		UpperCAmelCase			:      List[Any]			=			24
		UpperCAmelCase			:      Any			=			16

	# load original model from timm
	UpperCAmelCase			:      Optional[Any]			=			timm.create_model(__snake_case    ,	pretrained=__snake_case		)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	UpperCAmelCase			:      str			=			timm_model.state_dict()
	UpperCAmelCase			:      Any			=			create_rename_keys(__snake_case    ,	__snake_case		)
	for src, dest in rename_keys:
		rename_key(__snake_case    ,	__snake_case    ,	__snake_case		)
	read_in_q_k_v(__snake_case    ,	__snake_case    ,	__snake_case		)

	# load HuggingFace model
	UpperCAmelCase			:      List[Any]			=			DeiTForImageClassificationWithTeacher(__snake_case		).eval()
	model.load_state_dict(__snake_case		)

	# Check outputs on an image, prepared by DeiTImageProcessor
	UpperCAmelCase			:      List[str]			=			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
	UpperCAmelCase			:      str			=			DeiTImageProcessor(size=__snake_case    ,	crop_size=config.image_size		)
	UpperCAmelCase			:      List[Any]			=			image_processor(images=prepare_img()    ,	return_tensors="""pt"""		)
	UpperCAmelCase			:      str			=			encoding["pixel_values"]
	UpperCAmelCase			:      List[str]			=			model(__snake_case		)

	UpperCAmelCase			:      Optional[Any]			=			timm_model(__snake_case		)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(__snake_case    ,	outputs.logits    ,	atol=1e-3		)

	Path(__snake_case		).mkdir(exist_ok=__snake_case		)
	print(F"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
	model.save_pretrained(__snake_case		)
	print(F"Saving image processor to {pytorch_dump_folder_path}"		)
	image_processor.save_pretrained(__snake_case		)


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

						A:     Optional[int]										=	parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			get_file_from_repo(
         _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if speaker_embeddings_path is None:
      logger.warning(
          F"`{os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  """v2"""		)   ,  exist_ok=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]   ,  _SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}"		)   ,  voice_preset[key]   ,  allow_pickle=_SCREAMING_SNAKE_CASE   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			tmp_dict

     with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  """w"""		) as fp:
      json.dump(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    super().save_pretrained(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.speaker_embeddings[voice_preset]

    UpperCAmelCase			:      List[Any]			=			{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

     UpperCAmelCase			:      List[str]			=			get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""   ,  """/"""		)   ,  voice_preset_paths[key]   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if path is None:
      raise ValueError(
          F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'   ,  '/'		)   ,  voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''simple docstring'''




    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

     if not isinstance(voice_preset[key]   ,  np.ndarray		):
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

     if len(voice_preset[key].shape		) != self.preset_shape[key]:
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if (
         isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

     else:
      if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) and not voice_preset.endswith(""".npz"""		):
       UpperCAmelCase			:      Tuple			=			voice_preset + """.npz"""

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			self.tokenizer(
        _SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_attention_mask=_SCREAMING_SNAKE_CASE   ,  return_token_type_ids=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    if voice_preset is not None:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
