"""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()
lowerCAmelCase__						:						List[Any]		  =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase ):
				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(lowerCamelCase )-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(lowerCamelCase )-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(lowerCamelCase )-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(lowerCamelCase )-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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				# 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 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(lowerCamelCase      ,    stream=lowerCamelCase ).raw )

				return image



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False      ,    lowerCamelCase=None ):
				UpperCAmelCase__    =							GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,    decoder_hidden_size=6_4      ,    depths=[3, 8, 2_7, 3] )

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

				# prepare image
				UpperCAmelCase__    =							prepare_img()
				UpperCAmelCase__    =							image_processor(images=lowerCamelCase      ,    return_tensors='pt' ).pixel_values

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

				# load original state dict
				UpperCAmelCase__    =							torch.load(lowerCamelCase      ,    map_location=torch.device('cpu' ) )

				# rename keys
				UpperCAmelCase__    =							rename_keys(lowerCamelCase )

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

				# create HuggingFace model and load state dict
				UpperCAmelCase__    =							GLPNForDepthEstimation(lowerCamelCase )
				model.load_state_dict(lowerCamelCase )
				model.eval()

				# forward pass
				UpperCAmelCase__    =							model(lowerCamelCase )
				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, 4_8_0, 6_4_0] )

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


if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			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.',
						)
						lowerCAmelCase__						:						str		  =			parser.parse_args()
						convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




"""simple docstring"""






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "gelu_new"					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 1e-5					,lowerCamelCase__     :			float = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
													raise ValueError(
													    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
													    f''' `n_embd`: {n_embd} are not equal.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_inner_dim					,self.prefix_hidden_dim    )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_hidden_dim					,lowerCamelCase__    ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									UpperCAmelCase__    =							GPTaConfig(
									    vocab_size=lowerCamelCase__					,n_positions=lowerCamelCase__					,n_embd=lowerCamelCase__					,n_layer=lowerCamelCase__					,n_head=lowerCamelCase__					,n_inner=lowerCamelCase__					,activation_function=lowerCamelCase__					,resid_pdrop=lowerCamelCase__					,embd_pdrop=lowerCamelCase__					,attn_pdrop=lowerCamelCase__					,layer_norm_epsilon=lowerCamelCase__					,initializer_range=lowerCamelCase__					,scale_attn_weights=lowerCamelCase__					,use_cache=lowerCamelCase__					,scale_attn_by_inverse_layer_idx=lowerCamelCase__					,reorder_and_upcast_attn=lowerCamelCase__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__					,labels=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
									if self.prefix_hidden_dim is not None:
													return out, hidden
									else:
													return out







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.split(lowerCamelCase__					,1					,dim=0    )
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for feature in features:
													UpperCAmelCase__    =							self.decode_prefix(feature.to(lowerCamelCase__    )    )  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase__   ,			UpperCAmelCase__    =							self.generate_beam(
													    input_embeds=lowerCamelCase__					,device=lowerCamelCase__					,eos_token_id=lowerCamelCase__    )
													generated_tokens.append(output_tokens[0]    )
													generated_seq_lengths.append(seq_lengths[0]    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									return generated_tokens, generated_seq_lengths







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

									for i in range(lowerCamelCase__    ):
													UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__    )
													UpperCAmelCase__    =							outputs.logits
													UpperCAmelCase__    =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													UpperCAmelCase__    =							logits.softmax(-1    ).log()

													if scores is None:
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							logits.topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							generated.expand(lowerCamelCase__					,*generated.shape[1:]    )
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							next_tokens.permute(1					,0    ), scores.squeeze(0    )
																	if tokens is None:
																					UpperCAmelCase__    =							next_tokens
																	else:
																					UpperCAmelCase__    =							tokens.expand(lowerCamelCase__					,*tokens.shape[1:]    )
																					UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
													else:
																	UpperCAmelCase__    =							-float(np.inf    )
																	UpperCAmelCase__    =							0
																	UpperCAmelCase__    =							scores[:, None] + logits
																	seq_lengths[~is_stopped] += 1
																	UpperCAmelCase__    =							scores_sum / seq_lengths[:, None]
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							scores_sum_average.view(-1    ).topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							next_tokens // scores_sum.shape[1]
																	UpperCAmelCase__    =							seq_lengths[next_tokens_source]
																	UpperCAmelCase__    =							next_tokens % scores_sum.shape[1]
																	UpperCAmelCase__    =							next_tokens.unsqueeze(1    )
																	UpperCAmelCase__    =							tokens[next_tokens_source]
																	UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
																	UpperCAmelCase__    =							generated[next_tokens_source]
																	UpperCAmelCase__    =							scores_sum_average * seq_lengths
																	UpperCAmelCase__    =							is_stopped[next_tokens_source]

													UpperCAmelCase__    =							self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					,1					,-1    )
													UpperCAmelCase__    =							torch.cat((generated, next_token_embed)					,dim=1    )
													UpperCAmelCase__    =							is_stopped + next_tokens.eq(lowerCamelCase__    ).squeeze()
													if is_stopped.all():
																	break

									UpperCAmelCase__    =							scores / seq_lengths
									UpperCAmelCase__    =							scores.argsort(descending=lowerCamelCase__    )
									# tokens tensors are already padded to max_seq_length
									UpperCAmelCase__    =							[tokens[i] for i in order]
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__					,dim=0    )
									UpperCAmelCase__    =							torch.tensor([seq_lengths[i] for i in order]					,dtype=seq_lengths.dtype    )
									return output_texts, seq_lengths



