






import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      Optional[int]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  SwinConfig.from_pretrained(
	    """microsoft/swin-tiny-patch4-window7-224"""				,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
	__lowercase             =  MaskFormerConfig(backbone_config=lowerCamelCase					)

	__lowercase             =  """huggingface/label-files"""
	if "ade20k-full" in model_name:
		# this should be ok
		__lowercase             =  847
		__lowercase             =  """maskformer-ade20k-full-id2label.json"""
	elif "ade" in model_name:
		# this should be ok
		__lowercase             =  150
		__lowercase             =  """ade20k-id2label.json"""
	elif "coco-stuff" in model_name:
		# this should be ok
		__lowercase             =  171
		__lowercase             =  """maskformer-coco-stuff-id2label.json"""
	elif "coco" in model_name:
		# TODO
		__lowercase             =  133
		__lowercase             =  """coco-panoptic-id2label.json"""
	elif "cityscapes" in model_name:
		# this should be ok
		__lowercase             =  19
		__lowercase             =  """cityscapes-id2label.json"""
	elif "vistas" in model_name:
		# this should be ok
		__lowercase             =  65
		__lowercase             =  """mapillary-vistas-id2label.json"""

	__lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
	__lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}

	return config





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  []
	# stem
	# fmt: off
	rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")					)
	rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")					)
	rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")					)
	rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")					)
	# stages
	for i in range(len(config.backbone_config.depths					)					):
		for j in range(config.backbone_config.depths[i]					):
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')					)
			rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')					)

		if i < 3:
			rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')					)
			rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')					)
			rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')					)
		rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')					)
		rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')					)

	# FPN
	rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")					)
	rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")					)
	rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")					)
	for source_index, target_index in zip(range(3				,  0				,  -1					)				,  range(0				,  3					)					):
		rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')					)
		rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')					)
		rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')					)
		rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')					)
		rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')					)
		rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')					)
	rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")					)
	rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")					)

	# Transformer decoder
	for idx in range(config.decoder_config.decoder_layers					):
		# self-attention out projection
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')					)
		# cross-attention out projection
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')					)
		# MLP 1
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')					)
		# MLP 2
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')					)
		# layernorm 1 (self-attention layernorm)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')					)
		# layernorm 2 (cross-attention layernorm)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')					)
		# layernorm 3 (final layernorm)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')					)

	rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")					)
	rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")					)

	# heads on top
	rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")					)

	rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")					)
	rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")					)

	rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")					)
	rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")					)

	for i in range(3					):
		rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')					)
		rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')					)
	# fmt: on

	return rename_keys





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  dct.pop(lowerCamelCase					)
	__lowercase             =  val





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
	for i in range(len(backbone_config.depths					)					):
		__lowercase             =  num_features[i]
		for j in range(backbone_config.depths[i]					):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			__lowercase             =  state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'					)
			__lowercase             =  state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'					)
			# next, add query, keys and values (in that order) to the state dict
			__lowercase             =  in_proj_weight[:dim, :]
			__lowercase             =  in_proj_bias[: dim]
			__lowercase             =  in_proj_weight[
			    dim : dim * 2, :
			]
			__lowercase             =  in_proj_bias[
			    dim : dim * 2
			]
			__lowercase             =  in_proj_weight[
			    -dim :, :
			]
			__lowercase             =  in_proj_bias[-dim :]
			# fmt: on





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  config.decoder_config.hidden_size
	for idx in range(config.decoder_config.decoder_layers					):
		# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
		__lowercase             =  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'					)
		__lowercase             =  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'					)
		# next, add query, keys and values (in that order) to the state dict
		__lowercase             =  in_proj_weight[: hidden_size, :]
		__lowercase             =  in_proj_bias[:config.hidden_size]
		__lowercase             =  in_proj_weight[hidden_size : hidden_size * 2, :]
		__lowercase             =  in_proj_bias[hidden_size : hidden_size * 2]
		__lowercase             =  in_proj_weight[-hidden_size :, :]
		__lowercase             =  in_proj_bias[-hidden_size :]
		# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
		__lowercase             =  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'					)
		__lowercase             =  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'					)
		# next, add query, keys and values (in that order) to the state dict
		__lowercase             =  in_proj_weight[: hidden_size, :]
		__lowercase             =  in_proj_bias[:config.hidden_size]
		__lowercase             =  in_proj_weight[hidden_size : hidden_size * 2, :]
		__lowercase             =  in_proj_bias[hidden_size : hidden_size * 2]
		__lowercase             =  in_proj_weight[-hidden_size :, :]
		__lowercase             =  in_proj_bias[-hidden_size :]
	# fmt: on





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
	__lowercase             =  Image.open(requests.get(lowerCamelCase				,  stream=lowerCamelCase					).raw					)
	return im





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False					):




	'''simple docstring'''







	__lowercase             =  get_maskformer_config(lowerCamelCase					)

	# load original state_dict
	with open(lowerCamelCase				,  """rb"""					) as f:
		__lowercase             =  pickle.load(lowerCamelCase					)
	__lowercase             =  data["""model"""]

	# for name, param in state_dict.items():
	#     print(name, param.shape)

	# rename keys
	__lowercase             =  create_rename_keys(lowerCamelCase					)
	for src, dest in rename_keys:
		rename_key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
	read_in_swin_q_k_v(lowerCamelCase				,  config.backbone_config					)
	read_in_decoder_q_k_v(lowerCamelCase				,  lowerCamelCase					)

	# update to torch tensors
	for key, value in state_dict.items():
		__lowercase             =  torch.from_numpy(lowerCamelCase					)

	# load 🤗 model
	__lowercase             =  MaskFormerForInstanceSegmentation(lowerCamelCase					)
	model.eval()

	for name, param in model.named_parameters():
		print(lowerCamelCase				,  param.shape					)

	__lowercase       ,						__lowercase             =  model.load_state_dict(lowerCamelCase				,  strict=lowerCamelCase					)
	assert missing_keys == [
	    "model.pixel_level_module.encoder.model.layernorm.weight",
	    "model.pixel_level_module.encoder.model.layernorm.bias",
	]
	assert len(lowerCamelCase					) == 0, F'Unexpected keys: {unexpected_keys}'

	# verify results
	__lowercase             =  prepare_img()
	if "vistas" in model_name:
		__lowercase             =  65
	elif "cityscapes" in model_name:
		__lowercase             =  65_535
	else:
		__lowercase             =  255
	__lowercase             =  True if """ade""" in model_name else False
	__lowercase             =  MaskFormerImageProcessor(ignore_index=lowerCamelCase				,  reduce_labels=lowerCamelCase					)

	__lowercase             =  image_processor(lowerCamelCase				,  return_tensors="""pt"""					)

	__lowercase             =  model(**lowerCamelCase					)

	print("""Logits:"""				,  outputs.class_queries_logits[0, :3, :3]					)

	if model_name == "maskformer-swin-tiny-ade":
		__lowercase             =  torch.tensor(
		    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
	assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,  lowerCamelCase				,  atol=1e-4					)
	print("""Looks ok!"""					)

	if pytorch_dump_folder_path is not None:
		print(F'Saving model and image processor to {pytorch_dump_folder_path}'					)
		Path(lowerCamelCase					).mkdir(exist_ok=lowerCamelCase					)
		model.save_pretrained(lowerCamelCase					)
		image_processor.save_pretrained(lowerCamelCase					)

	if push_to_hub:
		print("""Pushing model and image processor to the hub..."""					)
		model.push_to_hub(F'nielsr/{model_name}'					)
		image_processor.push_to_hub(F'nielsr/{model_name}'					)


if __name__ == "__main__":
						__UpperCamelCase      :      Tuple     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default="""maskformer-swin-tiny-ade""",
						    type=str,
						    help=("""Name of the MaskFormer model you'd like to convert""",),
						)
						parser.add_argument(
						    """--checkpoint_path""",
						    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
						    type=str,
						    help="""Path to the original state dict (.pth file).""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)
						parser.add_argument(
						    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
						)

						__UpperCamelCase      :      List[Any]     						=						parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)









import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__UpperCamelCase      :      Tuple     						=						2


class        __UpperCamelCase    :







	def __init__(  self      :							List[str]       ,				*,  # begin keyword-only arguments
	    _lowerCAmelCase      :							Optional[int]="<s>"       ,				_lowerCAmelCase      :							Optional[int]="<pad>"       ,				_lowerCAmelCase      :							int="</s>"       ,				_lowerCAmelCase      :							str="<unk>"       ,				_lowerCAmelCase      :							List[str]=None       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  bos, unk, pad, eos
		__lowercase             =  []
		__lowercase             =  []
		__lowercase             =  {}
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		if extra_special_symbols:
			for s in extra_special_symbols:
				self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  len(self.symbols			)







	def __eq__(  self      :							Dict       ,				_lowerCAmelCase      :							List[str]			)		-> Any:
		"""simple docstring"""







		return self.indices == other.indices







	def __getitem__(  self      :							Any       ,				_lowerCAmelCase      :							str			)		-> Dict:
		"""simple docstring"""







		if idx < len(self.symbols			):
			return self.symbols[idx]
		return self.unk_word







	def __len__(  self      :							str			)		-> List[str]:
		"""simple docstring"""







		return len(self.symbols			)







	def __contains__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		return sym in self.indices







	@classmethod
	def  _a   (  cls      :							Dict       ,				_lowerCAmelCase      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  cls()
		d.add_from_file(_lowerCAmelCase			)
		return d







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Union[str, Any]=1       ,				_lowerCAmelCase      :							Optional[int]=False			)		-> Union[str, Any]:
		"""simple docstring"""







		if word in self.indices and not overwrite:
			__lowercase             =  self.indices[word]
			__lowercase             =  self.count[idx] + n
			return idx
		else:
			__lowercase             =  len(self.symbols			)
			__lowercase             =  idx
			self.symbols.append(_lowerCAmelCase			)
			self.count.append(_lowerCAmelCase			)
			return idx







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[Any]:
		"""simple docstring"""







		return 0







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict			)		-> str:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				with open(_lowerCAmelCase       ,				"""r"""       ,				encoding="""utf-8"""			) as fd:
					self.add_from_file(_lowerCAmelCase			)
			except FileNotFoundError as fnfe:
				raise fnfe
			except UnicodeError:
				raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase			)			)
			return

		__lowercase             =  f.readlines()
		__lowercase             =  self._load_meta(_lowerCAmelCase			)

		for line in lines[indices_start_line:]:
			try:
				__lowercase       ,						__lowercase             =  line.rstrip().rsplit(""" """       ,				1			)
				if field == "#fairseq:overwrite":
					__lowercase             =  True
					__lowercase       ,						__lowercase             =  line.rsplit(""" """       ,				1			)
				else:
					__lowercase             =  False
				__lowercase             =  int(_lowerCAmelCase			)
				__lowercase             =  line
				if word in self and not overwrite:
					raise RuntimeError(
					    """Duplicate word found when loading Dictionary: '{}'. """
					    """Duplicate words can overwrite earlier ones by adding the """
					    """#fairseq:overwrite flag at the end of the corresponding row """
					    """in the dictionary file. If using the Camembert model, please """
					    """download an updated copy of the model file.""".format(_lowerCAmelCase			)			)
				self.add_symbol(_lowerCAmelCase       ,				n=_lowerCAmelCase       ,				overwrite=_lowerCAmelCase			)
			except ValueError:
				raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""			)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  dict((re.sub(r"""@@$"""				,  """"""				,  lowerCamelCase					), v) if k.endswith("""@@"""					) else (re.sub(r"""$"""				,  """</w>"""				,  lowerCamelCase					), v) for k, v in d.items()					)
	__lowercase             =  """<s> <pad> </s> <unk>""".split()
	# restore the special tokens
	for k in keep_keys:
		del da[F'{k}</w>']
		__lowercase             =  d[k]  # restore
	return da





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if not os.path.exists(lowerCamelCase					):
		raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'					)
	os.makedirs(lowerCamelCase				,  exist_ok=lowerCamelCase					)
	print(F'Writing results to {pytorch_dump_folder_path}'					)

	# handle various types of models

	__lowercase             =  os.path.join(lowerCamelCase				,  """checkpoint.pt"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {checkpoint_file} does not exist!'					)
	__lowercase             =  torch.load(lowerCamelCase				,  map_location="""cpu"""					)

	__lowercase             =  chkpt["""cfg"""]["""model"""]

	# dicts
	__lowercase             =  os.path.join(lowerCamelCase				,  """dict.txt"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {dict_file} does not exist!'					)
	__lowercase             =  Dictionary.load(lowerCamelCase					)
	__lowercase             =  rewrite_dict_keys(src_dict.indices					)
	__lowercase             =  len(lowerCamelCase					)
	__lowercase             =  os.path.join(lowerCamelCase				,  VOCAB_FILES_NAMES["""vocab_file"""]					)
	print(F'Generating {src_vocab_file} of {src_vocab_size} records'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# merges_file (bpecodes)
	__lowercase             =  os.path.join(lowerCamelCase				,  """bpecodes"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {bpecodes_file} does not exist!'					)

	__lowercase             =  os.path.join(lowerCamelCase				,  VOCAB_FILES_NAMES["""merges_file"""]					)
	shutil.copyfile(lowerCamelCase				,  lowerCamelCase					)

	# model config
	__lowercase             =  os.path.join(lowerCamelCase				,  """config.json"""					)

	__lowercase             =  {
	    """activation_dropout""": args["""activation_dropout"""],
	    """architectures""": ["""BioGptForCausalLM"""],
	    """attention_probs_dropout_prob""": args["""attention_dropout"""],
	    """bos_token_id""": 0,
	    """eos_token_id""": 2,
	    """hidden_act""": args["""activation_fn"""],
	    """hidden_dropout_prob""": args["""dropout"""],
	    """hidden_size""": args["""decoder_embed_dim"""],
	    """initializer_range""": 0.02,
	    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
	    """layer_norm_eps""": 1e-12,
	    """layerdrop""": args["""decoder_layerdrop"""],
	    """max_position_embeddings""": args["""max_target_positions"""],
	    """model_type""": """biogpt""",
	    """num_attention_heads""": args["""decoder_attention_heads"""],
	    """num_hidden_layers""": args["""decoder_layers"""],
	    """pad_token_id""": 1,
	    """scale_embedding""": not args["""no_scale_embedding"""],
	    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
	    """vocab_size""": src_vocab_size,
	}

	# good hparam defaults to start with

	print(F'Generating {biogpt_model_config_file}'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# tokenizer config
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  {
	    """bos_token""": """<s>""",
	    """eos_token""": """</s>""",
	    """model_max_length""": 1_024,
	    """pad_token""": """<pad>""",
	    """special_tokens_map_file""": None,
	    """tokenizer_class""": """BioGptTokenizer""",
	    """unk_token""": """<unk>""",
	}

	print(F'Generating {biogpt_tokenizer_config_file}'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# model
	__lowercase             =  chkpt["""model"""]

	# remove unneeded keys
	__lowercase             =  [
	    """decoder.version""",
	]
	for k in ignore_keys:
		model_state_dict.pop(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  list(model_state_dict.keys()					)
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""					):
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)
		else:
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)

	__lowercase             =  BioGptConfig.from_pretrained(lowerCamelCase					)
	__lowercase             =  BioGptForCausalLM(lowerCamelCase					)

	# check that it loads ok
	model_new.load_state_dict(lowerCamelCase					)

	# save
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
	print(F'Generating {pytorch_weights_dump_path}'					)
	torch.save(lowerCamelCase				,  lowerCamelCase					)

	print("""Conversion is done!"""					)


if __name__ == "__main__":
						__UpperCamelCase      :      int     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--biogpt_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
						        """ bpecodes, etc."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Optional[Any]     						=						parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

