





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							OrderedDict()
   for key, value in state_dict.items():
      if key.startswith('''module.encoder'''					):
         lowerCamelCase__       :						Tuple        =							key.replace('''module.encoder'''      ,  '''glpn.encoder'''					)
      if key.startswith('''module.decoder'''					):
         lowerCamelCase__       :						Optional[Any]        =							key.replace('''module.decoder'''      ,  '''decoder.stages'''					)
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         lowerCamelCase__       :						List[str]        =							key[key.find('''patch_embed'''					) + len('''patch_embed'''					)]
         lowerCamelCase__       :						Dict        =							key.replace(f"patch_embed{idx}"      ,  f"patch_embeddings.{int(UpperCamelCase__					)-1}"					)
      if "norm" in key:
         lowerCamelCase__       :						Tuple        =							key.replace('''norm'''      ,  '''layer_norm'''					)
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         lowerCamelCase__       :						Optional[int]        =							key[key.find('''glpn.encoder.layer_norm'''					) + len('''glpn.encoder.layer_norm'''					)]
         lowerCamelCase__       :						int        =							key.replace(f"layer_norm{idx}"      ,  f"layer_norm.{int(UpperCamelCase__					)-1}"					)
      if "layer_norm1" in key:
         lowerCamelCase__       :						Any        =							key.replace('''layer_norm1'''      ,  '''layer_norm_1'''					)
      if "layer_norm2" in key:
         lowerCamelCase__       :						str        =							key.replace('''layer_norm2'''      ,  '''layer_norm_2'''					)
      if "block" in key:
         # replace for example block1 by block.0
         lowerCamelCase__       :						Dict        =							key[key.find('''block'''					) + len('''block'''					)]
         lowerCamelCase__       :						List[str]        =							key.replace(f"block{idx}"      ,  f"block.{int(UpperCamelCase__					)-1}"					)
      if "attn.q" in key:
         lowerCamelCase__       :						Optional[int]        =							key.replace('''attn.q'''      ,  '''attention.self.query'''					)
      if "attn.proj" in key:
         lowerCamelCase__       :						Any        =							key.replace('''attn.proj'''      ,  '''attention.output.dense'''					)
      if "attn" in key:
         lowerCamelCase__       :						Optional[Any]        =							key.replace('''attn'''      ,  '''attention.self'''					)
      if "fc1" in key:
         lowerCamelCase__       :						Optional[Any]        =							key.replace('''fc1'''      ,  '''dense1'''					)
      if "fc2" in key:
         lowerCamelCase__       :						Optional[int]        =							key.replace('''fc2'''      ,  '''dense2'''					)
      if "linear_pred" in key:
         lowerCamelCase__       :						str        =							key.replace('''linear_pred'''      ,  '''classifier'''					)
      if "linear_fuse" in key:
         lowerCamelCase__       :						Any        =							key.replace('''linear_fuse.conv'''      ,  '''linear_fuse'''					)
         lowerCamelCase__       :						List[str]        =							key.replace('''linear_fuse.bn'''      ,  '''batch_norm'''					)
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         lowerCamelCase__       :						str        =							key[key.find('''linear_c'''					) + len('''linear_c'''					)]
         lowerCamelCase__       :						int        =							key.replace(f"linear_c{idx}"      ,  f"linear_c.{int(UpperCamelCase__					)-1}"					)
      if "bot_conv" in key:
         lowerCamelCase__       :						Optional[int]        =							key.replace('''bot_conv'''      ,  '''0.convolution'''					)
      if "skip_conv1" in key:
         lowerCamelCase__       :						List[Any]        =							key.replace('''skip_conv1'''      ,  '''1.convolution'''					)
      if "skip_conv2" in key:
         lowerCamelCase__       :						Dict        =							key.replace('''skip_conv2'''      ,  '''2.convolution'''					)
      if "fusion1" in key:
         lowerCamelCase__       :						int        =							key.replace('''fusion1'''      ,  '''1.fusion'''					)
      if "fusion2" in key:
         lowerCamelCase__       :						int        =							key.replace('''fusion2'''      ,  '''2.fusion'''					)
      if "fusion3" in key:
         lowerCamelCase__       :						str        =							key.replace('''fusion3'''      ,  '''3.fusion'''					)
      if "fusion" in key and "conv" in key:
         lowerCamelCase__       :						Tuple        =							key.replace('''conv'''      ,  '''convolutional_layer'''					)
      if key.startswith('''module.last_layer_depth'''					):
         lowerCamelCase__       :						List[Any]        =							key.replace('''module.last_layer_depth'''      ,  '''head.head'''					)
      lowerCamelCase__       :						List[str]        =							value

   return new_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""







   for i in range(config.num_encoder_blocks					):
      for j in range(config.depths[i]					):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         lowerCamelCase__       :						Dict        =							state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"					)
         lowerCamelCase__       :						Dict        =							state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.bias"					)
         # next, add keys and values (in that order) to the state dict
         lowerCamelCase__       :						Optional[Any]        =							kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCamelCase__       :						Dict        =							kv_bias[: config.hidden_sizes[i]]
         lowerCamelCase__       :						Dict        =							kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCamelCase__       :						Union[str, Any]        =							kv_bias[config.hidden_sizes[i] :]



def 	_a						(   )    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCamelCase__       :						Tuple        =							Image.open(requests.get(UpperCamelCase__      ,  stream=UpperCamelCase__					).raw					)

   return image



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False      ,  UpperCAmelCase=None					)    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						str        =							GLPNConfig(hidden_sizes=[64, 128, 320, 512]      ,  decoder_hidden_size=64      ,  depths=[3, 8, 27, 3]					)

   # load image processor (only resize + rescale)
   lowerCamelCase__       :						int        =							GLPNImageProcessor()

   # prepare image
   lowerCamelCase__       :						List[str]        =							prepare_img()
   lowerCamelCase__       :						int        =							image_processor(images=UpperCamelCase__      ,  return_tensors='''pt'''					).pixel_values

   logger.info('''Converting model...'''					)

   # load original state dict
   lowerCamelCase__       :						Dict        =							torch.load(UpperCamelCase__      ,  map_location=torch.device('''cpu'''					)					)

   # rename keys
   lowerCamelCase__       :						Dict        =							rename_keys(UpperCamelCase__					)

   # key and value matrices need special treatment
   read_in_k_v(UpperCamelCase__      ,  UpperCamelCase__					)

   # create HuggingFace model and load state dict
   lowerCamelCase__       :						Optional[int]        =							GLPNForDepthEstimation(UpperCamelCase__					)
   model.load_state_dict(UpperCamelCase__					)
   model.eval()

   # forward pass
   lowerCamelCase__       :						str        =							model(UpperCamelCase__					)
   lowerCamelCase__       :						Optional[Any]        =							outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCamelCase__       :						List[str]        =							torch.tensor(
             [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]					)
      elif "kitti" in model_name:
         lowerCamelCase__       :						str        =							torch.tensor(
             [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					)
      else:
         raise ValueError(f"Unknown model name: {model_name}"					)

      lowerCamelCase__       :						str        =							torch.Size([1, 480, 640]					)

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]      ,  UpperCamelCase__      ,  atol=1E-4					)
      print('''Looks ok!'''					)

   # finally, push to hub if required
   if push_to_hub:
      logger.info('''Pushing model and image processor to the hub...'''					)
      model.push_to_hub(
          repo_path_or_name=Path(UpperCamelCase__      ,  UpperCamelCase__					)      ,  organization='''nielsr'''      ,  commit_message='''Add model'''      ,  use_temp_dir=UpperCamelCase__      ,  )
      image_processor.push_to_hub(
          repo_path_or_name=Path(UpperCamelCase__      ,  UpperCamelCase__					)      ,  organization='''nielsr'''      ,  commit_message='''Add image processor'''      ,  use_temp_dir=UpperCamelCase__      ,  )


if __name__ == "__main__":
       _A :   str  			=  argparse.ArgumentParser()

       parser.add_argument(
           '--checkpoint_path',
           default=None,
           type=str,
           help='Path to the original PyTorch checkpoint (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
       )
       parser.add_argument(
           '--model_name',
           default='glpn-kitti',
           type=str,
           help='Name of the model in case you\'re pushing to the hub.',
       )
       _A :   Optional[Any]  			=  parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						int        =							ly_weight['''attention''']
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Tuple        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						str        =							ly_weight['''attention''']

      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T					)					)

   for lyr_num, lyr in enumerate(model.decoders					):
      lowerCamelCase__       :						List[Any]        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T					)					)

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						Dict        =							ly_weight['''MultiHeadDotProductAttention_0''']
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							[
       '''from __gin__ import dynamic_registration''',
       '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
       '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
       '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
   ]

   lowerCamelCase__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						str        =							SpectrogramNotesEncoder(
       max_length=synth_model.sequence_length['''inputs''']      ,  vocab_size=synth_model.model.module.config.vocab_size      ,  d_model=synth_model.model.module.config.emb_dim      ,  dropout_rate=synth_model.model.module.config.dropout_rate      ,  num_layers=synth_model.model.module.config.num_encoder_layers      ,  num_heads=synth_model.model.module.config.num_heads      ,  d_kv=synth_model.model.module.config.head_dim      ,  d_ff=synth_model.model.module.config.mlp_dim      ,  feed_forward_proj='''gated-gelu'''      ,  )

   lowerCamelCase__       :						int        =							SpectrogramContEncoder(
       input_dims=synth_model.audio_codec.n_dims      ,  targets_context_length=synth_model.sequence_length['''targets_context''']      ,  d_model=synth_model.model.module.config.emb_dim      ,  dropout_rate=synth_model.model.module.config.dropout_rate      ,  num_layers=synth_model.model.module.config.num_encoder_layers      ,  num_heads=synth_model.model.module.config.num_heads      ,  d_kv=synth_model.model.module.config.head_dim      ,  d_ff=synth_model.model.module.config.mlp_dim      ,  feed_forward_proj='''gated-gelu'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							TaFilmDecoder(
       input_dims=synth_model.audio_codec.n_dims      ,  targets_length=synth_model.sequence_length['''targets_context''']      ,  max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time      ,  d_model=synth_model.model.module.config.emb_dim      ,  num_layers=synth_model.model.module.config.num_decoder_layers      ,  num_heads=synth_model.model.module.config.num_heads      ,  d_kv=synth_model.model.module.config.head_dim      ,  d_ff=synth_model.model.module.config.mlp_dim      ,  dropout_rate=synth_model.model.module.config.dropout_rate      ,  )

   lowerCamelCase__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							SpectrogramDiffusionPipeline(
       notes_encoder=UpperCAmelCase      ,  continuous_encoder=UpperCAmelCase      ,  decoder=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  melgan=UpperCAmelCase      ,  )
   if args.save:
      pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
       _A :   int  			=  argparse.ArgumentParser()

       parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
       parser.add_argument(
           '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
       )
       parser.add_argument(
           '--checkpoint_path',
           default=F'''{MODEL}/checkpoint_500000''',
           type=str,
           required=False,
           help='Path to the original jax model checkpoint.',
       )
       _A :   Tuple  			=  parser.parse_args()

       main(args)



