

'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
					from ...onnx.config import PatchingSpec
					from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Tuple        ="longformer"






			def __init__(  self			,_snake_case = 5_12			,_snake_case = 2			,_snake_case = 1			,_snake_case = 0			,_snake_case = 2			,_snake_case = 3_05_22			,_snake_case = 7_68			,_snake_case = 12			,_snake_case = 12			,_snake_case = 30_72			,_snake_case = "gelu"			,_snake_case = 0.1			,_snake_case = 0.1			,_snake_case = 5_12			,_snake_case = 2			,_snake_case = 0.02			,_snake_case = 1E-12			,_snake_case = False			,**_snake_case			,):
				super().__init__(pad_token_id=_snake_case			,**_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  attention_window
				UpperCAmelCase_     :      str						=  sep_token_id
				UpperCAmelCase_     :      List[str]						=  bos_token_id
				UpperCAmelCase_     :      Optional[Any]						=  eos_token_id
				UpperCAmelCase_     :      Union[str, Any]						=  vocab_size
				UpperCAmelCase_     :      Dict						=  hidden_size
				UpperCAmelCase_     :      Union[str, Any]						=  num_hidden_layers
				UpperCAmelCase_     :      Union[str, Any]						=  num_attention_heads
				UpperCAmelCase_     :      Optional[Any]						=  hidden_act
				UpperCAmelCase_     :      List[str]						=  intermediate_size
				UpperCAmelCase_     :      Optional[Any]						=  hidden_dropout_prob
				UpperCAmelCase_     :      int						=  attention_probs_dropout_prob
				UpperCAmelCase_     :      Optional[int]						=  max_position_embeddings
				UpperCAmelCase_     :      Optional[Any]						=  type_vocab_size
				UpperCAmelCase_     :      Dict						=  initializer_range
				UpperCAmelCase_     :      int						=  layer_norm_eps
				UpperCAmelCase_     :      Optional[int]						=  onnx_export




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case			,_snake_case = "default"			,_snake_case = None     ):
				super().__init__(_snake_case			,_snake_case			,_snake_case     )
				UpperCAmelCase_     :      int						=  True






			@property
			def 		UpperCamelCase__							(  self     ):
				if self.task == "multiple-choice":
					UpperCAmelCase_     :      Union[str, Any]						=  {0: "batch", 1: "choice", 2: "sequence"}
				else:
					UpperCAmelCase_     :      Union[str, Any]						=  {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("global_attention_mask", dynamic_axis),
				    ]     )






			@property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[Any]						=  super().outputs
				if self.task == "default":
					UpperCAmelCase_     :      str						=  {0: "batch"}
				return outputs






			@property
			def 		UpperCamelCase__							(  self     ):
				return 1E-4






			@property
			def 		UpperCamelCase__							(  self     ):
				# needs to be >= 14 to support tril operator
				return max(super().default_onnx_opset			,14     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = -1			,_snake_case = -1			,_snake_case = False			,_snake_case = None			,):
				UpperCAmelCase_     :      Any						=  super().generate_dummy_inputs(
				    preprocessor=_snake_case			,batch_size=_snake_case			,seq_length=_snake_case			,is_pair=_snake_case			,framework=_snake_case     )
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				UpperCAmelCase_     :      Tuple						=  torch.zeros_like(inputs["input_ids"]     )
				# make every second token global
				UpperCAmelCase_     :      List[str]						=  1

				return inputs






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      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase			)       ->      str:
     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(_lowercase			)-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(_lowercase			)-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(_lowercase			)-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(_lowercase			)-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  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     # 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  __lowerCamelCase					( )       ->      Any:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=None			)       ->      Tuple:
     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=_lowercase						,     return_tensors='pt'			).pixel_values

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

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

     # rename keys
     UpperCamelCase						    =					rename_keys(_lowercase			)

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

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

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

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               UpperCamelCase						    =					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:
               UpperCamelCase						    =					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}'			)

          UpperCamelCase						    =					torch.Size([1, 480, 640]			)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]						,     _lowercase						,     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(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add model'						,     use_temp_dir=_lowercase						,     )
          image_processor.push_to_hub(
              repo_path_or_name=Path(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add image processor'						,     use_temp_dir=_lowercase						,     )


if __name__ == "__main__":
 _snake_case      = 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      = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


