
"""simple docstring"""

import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_A						=			data_utils.TransfoXLTokenizer
_A						=			data_utils.TransfoXLCorpus
_A						=			data_utils
_A						=			data_utils
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		if transfo_xl_dataset_file:
				# Convert a pre-processed corpus (see original TensorFlow repo)
				with open(a_,						'rb'      ) as fp:
						lowerCamelCase					:      Tuple          =   pickle.load(a_,						encoding='latin1'      )
				# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
				lowerCamelCase					:      Any          =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
				print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
				lowerCamelCase					:      str          =   corpus.vocab.__dict__
				torch.save(a_,						a_      )

				lowerCamelCase					:      str          =   corpus.__dict__
				corpus_dict_no_vocab.pop('vocab',						a_      )
				lowerCamelCase					:      Union[str, Any]          =   pytorch_dump_folder_path + '/' + CORPUS_NAME
				print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
				torch.save(a_,						a_      )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				lowerCamelCase					:      Union[str, Any]          =   os.path.abspath(a_      )
				lowerCamelCase					:      Union[str, Any]          =   os.path.abspath(a_      )

				print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						lowerCamelCase					:      Any          =   TransfoXLConfig()
				else:
						lowerCamelCase					:      int          =   TransfoXLConfig.from_json_file(a_      )
				print(F"""Building PyTorch model from configuration: {config}"""      )
				lowerCamelCase					:      str          =   TransfoXLLMHeadModel(a_      )

				lowerCamelCase					:      Any          =   load_tf_weights_in_transfo_xl(a_,						a_,						a_      )
				# Save pytorch-model
				lowerCamelCase					:      Dict          =   os.path.join(a_,						a_      )
				lowerCamelCase					:      Tuple          =   os.path.join(a_,						a_      )
				print(F"""Save PyTorch model to {os.path.abspath(a_      )}"""      )
				torch.save(model.state_dict(),						a_      )
				print(F"""Save configuration file to {os.path.abspath(a_      )}"""      )
				with open(a_,						'w',						encoding='utf-8'      ) as f:
						f.write(config.to_json_string()      )


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to the folder to store the PyTorch model or dataset/vocab.',
					)
					parser.add_argument(
					    '--tf_checkpoint_path',
					    default='',
					    type=str,
					    help='An optional path to a TensorFlow checkpoint path to be converted.',
					)
					parser.add_argument(
					    '--transfo_xl_config_file',
					    default='',
					    type=str,
					    help=(
					        'An optional config json file corresponding to the pre-trained BERT model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument(
					    '--transfo_xl_dataset_file',
					    default='',
					    type=str,
					    help='An optional dataset file to be converted in a vocabulary.',
					)
					_A						=			parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)








"""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)






