





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       Any	   =				{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""bart"""
 UpperCAmelCase__													=		["""past_key_values"""]
 UpperCAmelCase__													=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Optional[int]=50265			,     UpperCAmelCase  :      Optional[int]=1024			,     UpperCAmelCase  :      Optional[int]=12			,     UpperCAmelCase  :      Dict=4096			,     UpperCAmelCase  :      List[str]=16			,     UpperCAmelCase  :      List[Any]=12			,     UpperCAmelCase  :      List[Any]=4096			,     UpperCAmelCase  :      Optional[Any]=16			,     UpperCAmelCase  :      int=0.0			,     UpperCAmelCase  :      Dict=0.0			,     UpperCAmelCase  :      Optional[int]="gelu"			,     UpperCAmelCase  :      Tuple=1024			,     UpperCAmelCase  :      int=0.1			,     UpperCAmelCase  :      Dict=0.0			,     UpperCAmelCase  :      str=0.0			,     UpperCAmelCase  :      Tuple=0.0_2			,     UpperCAmelCase  :      List[str]=0.0			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      List[str]=True			,     UpperCAmelCase  :      str=3			,     UpperCAmelCase  :      Union[str, Any]=1			,     UpperCAmelCase  :      List[str]=0			,     UpperCAmelCase  :      List[Any]=2			,     UpperCAmelCase  :      Optional[Any]=True			,     UpperCAmelCase  :      Any=2			,     UpperCAmelCase  :      List[str]=2			,     **UpperCAmelCase  :      Dict			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      Tuple					 =		max_position_embeddings
     lowerCamelCase__    :      Optional[Any]					 =		d_model
     lowerCamelCase__    :      int					 =		encoder_ffn_dim
     lowerCamelCase__    :      int					 =		encoder_layers
     lowerCamelCase__    :      Optional[Any]					 =		encoder_attention_heads
     lowerCamelCase__    :      Dict					 =		decoder_ffn_dim
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_layers
     lowerCamelCase__    :      List[Any]					 =		decoder_attention_heads
     lowerCamelCase__    :      List[str]					 =		dropout
     lowerCamelCase__    :      List[Any]					 =		attention_dropout
     lowerCamelCase__    :      Optional[Any]					 =		activation_dropout
     lowerCamelCase__    :      str					 =		activation_function
     lowerCamelCase__    :      List[str]					 =		init_std
     lowerCamelCase__    :      Optional[Any]					 =		encoder_layerdrop
     lowerCamelCase__    :      Optional[int]					 =		decoder_layerdrop
     lowerCamelCase__    :      int					 =		classifier_dropout
     lowerCamelCase__    :      Union[str, Any]					 =		use_cache
     lowerCamelCase__    :      str					 =		encoder_layers
     lowerCamelCase__    :      str					 =		scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         num_labels=UpperCAmelCase			,     pad_token_id=UpperCAmelCase			,     bos_token_id=UpperCAmelCase			,     eos_token_id=UpperCAmelCase			,     is_encoder_decoder=UpperCAmelCase			,     decoder_start_token_id=UpperCAmelCase			,     forced_eos_token_id=UpperCAmelCase			,     **UpperCAmelCase			,     )

     # ensure backward compatibility for BART CNN models
     if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'			,     UpperCAmelCase	):
         lowerCamelCase__    :      Dict					 =		self.bos_token_id
         warnings.warn(
             F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
             'The config can simply be saved and uploaded again to be fixed.'	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 @property
 def      A_	(      self  :      Dict	)			->	Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      Any					 =		OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
             ]	)

         if self.use_past:
             lowerCamelCase__    :      Tuple					 =		{0: 'batch'}
             lowerCamelCase__    :      Tuple					 =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
             lowerCamelCase__    :      Optional[Any]					 =		{0: 'batch', 1: 'decoder_sequence'}
             lowerCamelCase__    :      Dict					 =		{0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
             self.fill_with_past_key_values_(UpperCAmelCase			,     direction='inputs'	)
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         lowerCamelCase__    :      int					 =		OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
             ]	)
         if self.use_past:
             lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		self.num_layers
             for i in range(UpperCAmelCase	):
                 lowerCamelCase__    :      List[Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
                 lowerCamelCase__    :      Optional[Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
     else:
         lowerCamelCase__    :      Union[str, Any]					 =		OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                 ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
             ]	)

     return common_inputs
 @property
 def      A_	(      self  :      List[Any]	)			->	Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      int					 =		super().outputs
     else:
         lowerCamelCase__    :      Optional[Any]					 =		super(UpperCAmelCase			,     self	).outputs
         if self.use_past:
             lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.num_layers
             for i in range(UpperCAmelCase	):
                 lowerCamelCase__    :      Union[str, Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
                 lowerCamelCase__    :      List[str]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
     return common_outputs
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

     # Generate decoder inputs
     lowerCamelCase__    :      List[str]					 =		seq_length if not self.use_past else 1
     lowerCamelCase__    :      Optional[int]					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     lowerCamelCase__    :      Optional[int]					 =		dict(**UpperCAmelCase			,     **UpperCAmelCase	)

     if self.use_past:
         if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
         else:
             import torch
         lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		common_inputs['input_ids'].shape
         lowerCamelCase__    :      List[str]					 =		common_inputs['decoder_input_ids'].shape[1]
         lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.num_attention_heads
         lowerCamelCase__    :      Any					 =		(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         lowerCamelCase__    :      Dict					 =		decoder_seq_length + 3
         lowerCamelCase__    :      int					 =		(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         lowerCamelCase__    :      Dict					 =		torch.cat(
             [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase			,     UpperCAmelCase	)]			,     dim=1	)

         lowerCamelCase__    :      Any					 =		[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		self.num_layers
         lowerCamelCase__    :      List[Any]					 =		min(UpperCAmelCase			,     UpperCAmelCase	)
         lowerCamelCase__    :      Tuple					 =		max(UpperCAmelCase			,     UpperCAmelCase	) - min_num_layers
         lowerCamelCase__    :      str					 =		'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

         for _ in range(UpperCAmelCase	):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                 )	)
         # TODO: test this.
         lowerCamelCase__    :      List[str]					 =		encoder_shape if remaining_side_name == 'encoder' else decoder_shape
         for _ in range(UpperCAmelCase			,     UpperCAmelCase	):
             common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase	), torch.zeros(UpperCAmelCase	))	)
     return common_inputs
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     lowerCamelCase__    :      Any					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

     if self.use_past:
         if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
         else:
             import torch
         lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		common_inputs['input_ids'].shape
         # Not using the same length for past_key_values
         lowerCamelCase__    :      Union[str, Any]					 =		seqlen + 2
         lowerCamelCase__ ,							lowerCamelCase__    :      List[Any]					 =		self.num_layers
         lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		self.num_attention_heads
         lowerCamelCase__    :      Tuple					 =		(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         lowerCamelCase__    :      Union[str, Any]					 =		common_inputs['attention_mask'].dtype
         lowerCamelCase__    :      int					 =		torch.cat(
             [common_inputs['attention_mask'], torch.ones(UpperCAmelCase			,     UpperCAmelCase			,     dtype=UpperCAmelCase	)]			,     dim=1	)
         lowerCamelCase__    :      Any					 =		[
             (torch.zeros(UpperCAmelCase	), torch.zeros(UpperCAmelCase	)) for _ in range(UpperCAmelCase	)
         ]
     return common_inputs
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     lowerCamelCase__    :      Tuple					 =		compute_effective_axis_dimension(
         UpperCAmelCase			,     fixed_dimension=OnnxConfig.default_fixed_batch			,     num_token_to_add=0	)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     lowerCamelCase__    :      List[str]					 =		tokenizer.num_special_tokens_to_add(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		compute_effective_axis_dimension(
         UpperCAmelCase			,     fixed_dimension=OnnxConfig.default_fixed_sequence			,     num_token_to_add=UpperCAmelCase	)

     # Generate dummy inputs according to compute batch and sequence
     lowerCamelCase__    :      str					 =		[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
     lowerCamelCase__    :      Dict					 =		dict(tokenizer(UpperCAmelCase			,     return_tensors=UpperCAmelCase	)	)
     return common_inputs
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      List[Any]					 =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             UpperCAmelCase			,     batch_size=UpperCAmelCase			,     seq_length=UpperCAmelCase			,     is_pair=UpperCAmelCase			,     framework=UpperCAmelCase	)

     elif self.task == "causal-lm":
         lowerCamelCase__    :      int					 =		self._generate_dummy_inputs_for_causal_lm(
             UpperCAmelCase			,     batch_size=UpperCAmelCase			,     seq_length=UpperCAmelCase			,     is_pair=UpperCAmelCase			,     framework=UpperCAmelCase	)
     else:
         lowerCamelCase__    :      Optional[int]					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             UpperCAmelCase			,     batch_size=UpperCAmelCase			,     seq_length=UpperCAmelCase			,     is_pair=UpperCAmelCase			,     framework=UpperCAmelCase	)

     return common_inputs







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      int	)			->	List[Any]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      Dict					 =		super()._flatten_past_key_values_(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     else:
         lowerCamelCase__    :      str					 =		super(UpperCAmelCase			,     self	)._flatten_past_key_values_(
             UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)












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()
_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


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

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    # 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)
            lowerCamelCase__    :      Any					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      Optional[Any]					 =		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[int]					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      Optional[int]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Any					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      Dict					 =		kv_bias[config.hidden_sizes[i] :]


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      List[str]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      Tuple					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )

    return image


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None )			->     Optional[int]:
    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__    :      Union[str, Any]					 =		GLPNImageProcessor()

    # prepare image
    lowerCamelCase__    :      str					 =		prepare_img()
    lowerCamelCase__    :      Tuple					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

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

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

    # rename keys
    lowerCamelCase__    :      str					 =		rename_keys(_UpperCAmelCase )

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

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

    # forward pass
    lowerCamelCase__    :      List[str]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__    :      List[Any]					 =		torch.tensor(
                [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
        elif "kitti" in model_name:
            lowerCamelCase__    :      List[str]					 =		torch.tensor(
                [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
        else:
            raise ValueError(F"""Unknown model name: {model_name}""" )

        lowerCamelCase__    :      Tuple					 =		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__":
    _UpperCAmelCase				:       Tuple	   =				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.""",
    )
    _UpperCAmelCase				:       int	   =				parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





