
"""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						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		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					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

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

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

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

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   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:
						lowerCamelCase					:      str          =   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}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

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


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








"""simple docstring"""

import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A						=			logging.getLogger(__name__)






class 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			if index is not None and index.is_initialized() and len(UpperCAmelCase_ ) > 0:
					raise ValueError(
					    'When using Ray for distributed fine-tuning, '
					    'you\'ll need to provide the paths instead, '
					    'as the dataset and the index are loaded '
					    'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
			super().__init__(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
			lowerCamelCase					:      List[str]          =   retrieval_workers
			if len(self.retrieval_workers ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					        for worker in self.retrieval_workers
					    ] )




	def       _UpperCamelCase   (			self )		->							Dict:
			logger.info('initializing retrieval' )

			if len(self.retrieval_workers ) > 0:
					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
			else:
					# Non-distributed training. Load index into this same process.
					self.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)






