
'''simple docstring'''


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()
snake_case_ :  Any     =					logging.get_logger(__name__)



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

   return new_state_dict



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Tuple):
   # for each of the encoder blocks:
   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)
         UpperCamelCase       =  state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight')
         UpperCamelCase       =  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
         UpperCamelCase       =  kv_weight[
             : config.hidden_sizes[i], :
         ]
         UpperCamelCase       =  kv_bias[: config.hidden_sizes[i]]
         UpperCamelCase       =  kv_weight[
             config.hidden_sizes[i] :, :
         ]
         UpperCamelCase       =  kv_bias[config.hidden_sizes[i] :]



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

   return image



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Any=False,      _UpperCAmelCase					:       str=None):
   UpperCamelCase       =  GLPNConfig(hidden_sizes=[64, 128, 320, 512],      decoder_hidden_size=64,      depths=[3, 8, 27, 3])

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

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

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

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

   # rename keys
   UpperCamelCase       =  rename_keys(_UpperCAmelCase)

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

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

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

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         UpperCamelCase       =  torch.tensor(
             [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]])
      elif "kitti" in model_name:
         UpperCamelCase       =  torch.tensor(
             [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]])
      else:
         raise ValueError(f'Unknown model name: {model_name}')

      UpperCamelCase       =  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__":
   snake_case_ :  Union[str, Any]     =					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.',
   )
   snake_case_ :  int     =					parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
   from ..models.auto import AutoTokenizer


class 						lowercase__	:






   '''simple docstring'''







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      raise NotImplementedError()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      raise NotImplementedError()




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  tokenizer
      UpperCamelCase       =  skip_prompt
      UpperCamelCase       =  decode_kwargs

      # variables used in the streaming process
      UpperCamelCase       =  []
      UpperCamelCase       =  0
      UpperCamelCase       =  True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if len(value.shape   ) > 1 and value.shape[0] > 1:
         raise ValueError('''TextStreamer only supports batch size 1'''   )
      elif len(value.shape   ) > 1:
         UpperCamelCase       =  value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
         UpperCamelCase       =  False
         return

      # Add the new token to the cache and decodes the entire thing.
      self.token_cache.extend(value.tolist()   )
      UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )

      # After the symbol for a new line, we flush the cache.
      if text.endswith('''\n'''   ):
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      # If the last token is a CJK character, we print the characters.
      elif len(lowerCamelCase__   ) > 0 and self._is_chinese_char(ord(text[-1]   )   ):
         UpperCamelCase       =  text[self.print_len :]
         self.print_len += len(lowerCamelCase__   )
      # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
      # which may change with the subsequent token -- there are probably smarter ways to do this!)
      else:
         UpperCamelCase       =  text[self.print_len : text.rfind(''' '''   ) + 1]
         self.print_len += len(lowerCamelCase__   )

      self.on_finalized_text(lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if len(self.token_cache   ) > 0:
         UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      else:
         UpperCamelCase       =  ''''''

      UpperCamelCase       =  True
      self.on_finalized_text(lowerCamelCase__ ,   stream_end=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      print(lowerCamelCase__ ,   flush=lowerCamelCase__ ,   end='''''' if not stream_end else None   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if (
          (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
          or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
          or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
          or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
          or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
          or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
          or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
          or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
      ):  #
         return True

      return False




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   )
      UpperCamelCase       =  Queue()
      UpperCamelCase       =  None
      UpperCamelCase       =  timeout







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      self.text_queue.put(lowerCamelCase__ ,   timeout=self.timeout   )
      if stream_end:
         self.text_queue.put(self.stop_signal ,   timeout=self.timeout   )







   def __iter__(			self   ):

      '''simple docstring'''







      return self







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.text_queue.get(timeout=self.timeout   )
      if value == self.stop_signal:
         raise StopIteration()
      else:
         return value





