





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:Optional[Any]				)	->				Optional[int]:
     '''simple docstring'''


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       torch.permute(_UpperCAmelCase  ,						(0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				):
          # linear layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          A_          =       flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:Any				)	->				Dict:
     '''simple docstring'''


     if "metadata" in layer:
          A_          =       layer.split('''metadata'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
     elif "kvstore" in layer:
          A_          =       layer.split('''kvstore'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

     else:
          A_          =       layer.split('''/'''				)
          A_          =       '''/'''.join(split_layer[:-1]				)
          A_          =       (split_layer[-1],)

     if "kvstore/path" in layer:
          A_          =       f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
     elif "kvstore/driver" in layer:
          A_          =       '''file'''
     else:
          A_          =       checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content

def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Any:
     '''simple docstring'''


     A_          =       rename_keys(_UpperCAmelCase				)
     A_          =       {}
     for k, v in current_block.items():
          A_          =       v
     A_          =       new_current_block
     torch.save(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = WEIGHTS_NAME				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       convert_file_size_to_int(_UpperCAmelCase				)
     A_          =       []
     A_          =       {}
     A_          =       0
     A_          =       0

     os.makedirs(_UpperCAmelCase  ,						exist_ok=_UpperCAmelCase				)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''  ,						'''rb'''				) as fp:
          A_          =       serialization.msgpack_restore(fp.read()				)['''optimizer''']['''target''']
          A_          =       flatten_dict(_UpperCAmelCase  ,						sep='''/'''				)

     A_          =       {}
     for layer in checkpoint_info.keys():
          A_   ,    A_   ,    A_          =       get_key_and_tensorstore_dict(
              _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          if curr_real_layer_name in all_layers:
               A_          =       content
          else:
               A_          =       {split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          A_          =       ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
          A_          =       torch.tensor(_UpperCAmelCase				)
          A_          =       raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

          # use the renaming pattern from the small conversion scripts
          A_   ,    A_          =       rename_base_flax_keys(tuple(key.split('''/'''				)				)  ,						_UpperCAmelCase				)
          A_          =       '''/'''.join(_UpperCAmelCase				)

          # If this weight is going to tip up over the maximal size, we split.
          if current_block_size + weight_size > max_shard_size:
               A_          =       os.path.join(
                   _UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{len(_UpperCAmelCase				)+1:05d}-of-???.bin'				)				)
               rename_and_save_block(_UpperCAmelCase  ,						_UpperCAmelCase				)
               sharded_state_dicts.append(current_block.keys()				)
               del current_block
               A_          =       {}
               A_          =       0

          A_          =       raw_weights.to(getattr(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     A_          =       os.path.join(_UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{len(_UpperCAmelCase				)+1:05d}-of-???.bin'				)				)
     rename_and_save_block(_UpperCAmelCase  ,						_UpperCAmelCase				)
     sharded_state_dicts.append(current_block.keys()				)

     # If we only have one shard, we return it
     if len(_UpperCAmelCase				) == 1:
          return {weights_name: sharded_state_dicts[0]}, None

     # Otherwise, let's build the index
     A_          =       {}
     A_          =       {}
     for idx, shard in enumerate(_UpperCAmelCase				):
          A_          =       weights_name.replace(
              '''.bin'''  ,						f'-{idx+1:05d}-of-{len(_UpperCAmelCase				):05d}.bin'				)  # len(sharded_state_dicts):05d}
          A_          =       os.path.join(_UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{idx+1:05d}-of-???.bin'				)				)
          os.rename(_UpperCAmelCase  ,						os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          A_          =       shard
          for key in shard:
               A_          =       shard_file

    # Add the metadata
     A_          =       {'''total_size''': total_size}
     A_          =       {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						'''w'''  ,						encoding='''utf-8'''				) as f:
          A_          =       json.dumps(_UpperCAmelCase  ,						indent=2  ,						sort_keys=_UpperCAmelCase				) + '''\n'''
          f.write(_UpperCAmelCase				)

     return metadata, index


if __name__ == "__main__":
      a__      :     Dict         =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--switch_t5x_checkpoint_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
          type=str,
          required=False,
          help='Path to a directory containing a folder per layer. Follows the original Google format.',
      )
      parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
      parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
          type=str,
          required=False,
          help='Path to the output pytorch model.',
      )
      a__      :     Tuple         =		parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )


def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     A_          =       SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''				)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''				)
     A_          =       SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted'''  ,						device_map='''auto'''				)

     A_          =       TaTokenizer.from_pretrained('''t5-small'''				)
     A_          =       '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     A_          =       tokenizer(_UpperCAmelCase  ,						return_tensors='''pt'''				).input_ids
     A_          =       model.generate(_UpperCAmelCase  ,						decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)










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__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


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

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''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)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       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
               A_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

     # load image processor (only resize + rescale)
     A_          =       GLPNImageProcessor()

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

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

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

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

     # create HuggingFace model and load state dict
     A_          =       GLPNForDepthEstimation(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               A_          =       torch.tensor(
                   [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]				)
          elif "kitti" in model_name:
               A_          =       torch.tensor(
                   [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				)
          else:
               raise ValueError(f'Unknown model name: {model_name}'				)

          A_          =       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__      :     Dict         =		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__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



