



"""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()
snake_case_				     =       logging.get_logger(__name__)

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

						return image

@torch.no_grad()
def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_=False		,	lowercase_=None						):
						UpperCAmelCase             = GLPNConfig(hidden_sizes=[64, 128, 320, 512]		,	decoder_hidden_size=64		,	depths=[3, 8, 27, 3]						)

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

						# prepare image
						UpperCAmelCase             = prepare_img()
						UpperCAmelCase             = image_processor(images=lowercase_		,	return_tensors='pt'						).pixel_values

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

						# load original state dict
						UpperCAmelCase             = torch.load(lowercase_		,	map_location=torch.device('cpu'						)						)

						# rename keys
						UpperCAmelCase             = rename_keys(lowercase_						)

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

						# create HuggingFace model and load state dict
						UpperCAmelCase             = GLPNForDepthEstimation(lowercase_						)
						model.load_state_dict(lowercase_						)
						model.eval()

						# forward pass
						UpperCAmelCase             = model(lowercase_						)
						UpperCAmelCase             = outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		UpperCAmelCase             = 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:
																		UpperCAmelCase             = 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}"""						)

												UpperCAmelCase             = torch.Size([1, 480, 640]						)

												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__":
				snake_case_				     =       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.""",
				)
				snake_case_				     =       parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					encoder="bert"   ,					max_pos=5_1_2   ,					enc_layers=6   ,					enc_hidden_size=5_1_2   ,					enc_heads=8   ,					enc_ff_size=5_1_2   ,					enc_dropout=0.2   ,					dec_layers=6   ,					dec_hidden_size=7_6_8   ,					dec_heads=8   ,					dec_ff_size=2_0_4_8   ,					dec_dropout=0.2   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" ) )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info("convert the model" )
      new_model.bert.load_state_dict(original.bert.state_dict() )
      new_model.decoder.load_state_dict(original.decoder.state_dict() )
      new_model.generator.load_state_dict(original.generator.state_dict() )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info("Make sure that the models' outputs are identical" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

      # prepare the model inputs
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample éàalj'-." )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0

      # forward pass
      SCREAMING_SNAKE_CASE_      =     encoder_input_ids
      SCREAMING_SNAKE_CASE_      =     decoder_input_ids
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      SCREAMING_SNAKE_CASE_      =     original(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )
      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      if are_identical:
            logging.info("all weights are equal up to 1e-3" )
      else:
            raise ValueError("the weights are different. The new model is likely different from the original one." )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info("saving the model's state dictionary" )
      torch.save(
          new_model.state_dict()   ,					"./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )


if __name__ == "__main__":
      A   :     Any       =				argparse.ArgumentParser()
      parser.add_argument(
          "--bertabs_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the output PyTorch model.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


