



'''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()
a   :			str					  =    logging.get_logger(__name__)



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

							return new_state_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			str:
							# 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     :		Dict							     =		state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
																					UpperCAmelCase     :		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
																					UpperCAmelCase     :		Optional[int]							     =		kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					UpperCAmelCase     :		Optional[int]							     =		kv_bias[: config.hidden_sizes[i]]
																					UpperCAmelCase     :		Dict							     =		kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					UpperCAmelCase     :		Optional[int]							     =		kv_bias[config.hidden_sizes[i] :]



def       __lowerCamelCase  (					)      ->			int:
							UpperCAmelCase     :		Any							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Optional[int]							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)

							return image



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=False	,				_lowercase=None		)      ->			Optional[int]:
							UpperCAmelCase     :		List[str]							     =		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     :		Dict							     =		GLPNImageProcessor()

							# prepare image
							UpperCAmelCase     :		Union[str, Any]							     =		prepare_img()
							UpperCAmelCase     :		Tuple							     =		image_processor(images=_lowercase	,				return_tensors="""pt"""		).pixel_values

							logger.info("""Converting model..."""		)

							# load original state dict
							UpperCAmelCase     :		str							     =		torch.load(_lowercase	,				map_location=torch.device("""cpu"""		)		)

							# rename keys
							UpperCAmelCase     :		Optional[Any]							     =		rename_keys(_lowercase		)

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

							# create HuggingFace model and load state dict
							UpperCAmelCase     :		Union[str, Any]							     =		GLPNForDepthEstimation(_lowercase		)
							model.load_state_dict(_lowercase		)
							model.eval()

							# forward pass
							UpperCAmelCase     :		Optional[Any]							     =		model(_lowercase		)
							UpperCAmelCase     :		Any							     =		outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					UpperCAmelCase     :		List[str]							     =		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     :		List[Any]							     =		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     :		List[str]							     =		torch.Size([1, 4_8_0, 6_4_0]		)

														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__":
							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   :			List[Any]					  =    parser.parse_args()
							convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






'''simple docstring'''




import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		[
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """decoder.output_projection.weight""",
							    """_float_tensor""",
							    """encoder.embed_positions._float_tensor""",
							    """decoder.embed_positions._float_tensor""",
							]
							for k in ignore_keys:
														state_dict.pop(_lowercase	,				_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		emb.weight.shape
							UpperCAmelCase     :		Dict							     =		nn.Linear(_lowercase	,				_lowercase	,				bias=_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		emb.weight.data
							return lin_layer



def       __lowerCamelCase  (					_lowercase	,				_lowercase=None		)      ->			List[Any]:
							UpperCAmelCase     :		Dict							     =		{}
							for old_key in state_dict.keys():
														UpperCAmelCase     :		str							     =		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												UpperCAmelCase     :		int							     =		key.replace("""moe_layer.experts.0"""	,				F'''ffn.experts.expert_{expert_idx}'''		)
																					else:
																												UpperCAmelCase     :		str							     =		key.replace("""moe_layer.experts."""	,				"""ffn.experts.expert_"""		)
														if "gate" in key:
																					UpperCAmelCase     :		str							     =		key.replace(""".moe_layer.gate.wg"""	,				""".ffn.router.classifier"""		)
														if "fc2" and "experts" not in key:
																					UpperCAmelCase     :		Tuple							     =		key.replace(""".fc2."""	,				""".ffn.fc2."""		)
														if "fc1" and "experts" not in key:
																					UpperCAmelCase     :		List[Any]							     =		key.replace(""".fc1."""	,				""".ffn.fc1."""		)
														if ".encoder_attn." in key:
																					UpperCAmelCase     :		Union[str, Any]							     =		key.replace(""".encoder_attn."""	,				""".cross_attention."""		)
														if "encoder_attn_layer_norm" in key:
																					UpperCAmelCase     :		str							     =		key.replace("""encoder_attn_layer_norm"""	,				"""cross_attention_layer_norm"""		)
														if "final_layer_norm" in key:
																					UpperCAmelCase     :		Optional[int]							     =		key.replace("""final_layer_norm"""	,				"""ff_layer_norm"""		)
														UpperCAmelCase     :		str							     =		state_dict[old_key]
							return new_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = WEIGHTS_NAME		)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Dict							     =		0
							os.makedirs(_lowercase	,				exist_ok=_lowercase		)

							for expert in range(_lowercase		):
														UpperCAmelCase     :		str							     =		switch_checkpoint_path + F'''-rank-{expert}.pt'''
														if os.path.isfile(_lowercase		):
																					UpperCAmelCase     :		Optional[Any]							     =		torch.load(_lowercase		)["""model"""]
																					remove_ignore_keys_(_lowercase		)
																					UpperCAmelCase     :		List[str]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
																					UpperCAmelCase     :		Optional[Any]							     =		os.path.join(
																					    _lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
																					torch.save(_lowercase	,				_lowercase		)
																					sharded_state_dicts.append(expert_state.keys()		)
																					total_size += sum([value.numel() for key, value in expert_state.items()]		) * dtype_byte_size(
																					    expert_state[list(_lowercase		)[0]].dtype		)

    # Add the last block
							UpperCAmelCase     :		Optional[Any]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
							UpperCAmelCase     :		Optional[int]							     =		torch.load(switch_checkpoint_path + """-shared.pt"""		)["""model"""]
							remove_ignore_keys_(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
							UpperCAmelCase     :		int							     =		shared_weights["""decoder.embed_tokens.weight"""]
							sharded_state_dicts.append(shared_weights.keys()		)

							# If we only have the shared weights (dummy model/experts saved on the same file)
							if len(_lowercase		) == 1:
														UpperCAmelCase     :		Dict							     =		os.path.join(_lowercase	,				_lowercase		)
														torch.save(_lowercase	,				_lowercase		)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_lowercase	,				_lowercase		)
							# Otherwise, let's build the index
							UpperCAmelCase     :		Optional[int]							     =		{}
							for idx, shard in enumerate(_lowercase		):
														UpperCAmelCase     :		List[str]							     =		weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-{len(_lowercase		):05d}.bin'''		)
														UpperCAmelCase     :		List[str]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-???.bin'''		)		)
														os.rename(_lowercase	,				os.path.join(_lowercase	,				_lowercase		)		)
														for key in shard:
																					UpperCAmelCase     :		Tuple							     =		shard_file

    # Add the metadata
							UpperCAmelCase     :		Any							     =		{"""total_size""": total_size}
							UpperCAmelCase     :		Optional[Any]							     =		{"""metadata""": metadata, """weight_map""": weight_map}

							with open(os.path.join(_lowercase	,				_lowercase		)	,				"""w"""	,				encoding="""utf-8"""		) as f:
														UpperCAmelCase     :		str							     =		json.dumps(_lowercase	,				indent=2	,				sort_keys=_lowercase		) + """\n"""
														f.write(_lowercase		)

							return metadata, index


if __name__ == "__main__":
							a   :			List[Any]					  =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--nllb_moe_checkpoint_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
							    type=str,
							    required=False,
							    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
							)
							parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
							    type=str,
							    required=False,
							    help="""Path to the output pytorch model.""",
							)
							a   :			int					  =    parser.parse_args()
							a  ,      a   :			Any					  =    shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							a   :			str					  =    NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							a   :			Optional[int]					  =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

