



"""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()
_UpperCAmelCase     =			logging.get_logger(__name__)





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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(lowercase							,			lowercase )





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Tuple     			=dct.pop(lowercase )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=val





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





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

    # load original model from timm
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=timm.create_model(lowercase							,			pretrained=lowercase )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   SCREAMING_SNAKE_CASE_: Dict     			=timm_model.state_dict()
   if base_model:
      remove_classification_head_(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=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":
      SCREAMING_SNAKE_CASE_: Optional[int]     			=ViTModel(lowercase ).eval()
   else:
      SCREAMING_SNAKE_CASE_: str     			=ViTForImageClassification(lowercase ).eval()
   model.load_state_dict(lowercase )

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

   if base_model:
      SCREAMING_SNAKE_CASE_: List[str]     			=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:
      SCREAMING_SNAKE_CASE_: List[str]     			=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"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint[f'''upsamples.{i}.1.bias''']

   for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
      for j in range(len(config.resblock_dilation_sizes ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

   if repo_id:
      print("""Pushing to the hub...""" )
      model.push_to_hub(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
   parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )



