

'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      					=     logging.get_logger(__name__)


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       int=False				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      List[str]						=  OrderedDict()
	for key, value in state_dict.items():
		if encoder_only and not key.startswith("head"				):
			UpperCAmelCase_     :      int						=  "segformer.encoder." + key
		if key.startswith("backbone"				):
			UpperCAmelCase_     :      Tuple						=  key.replace("backbone"	,				"segformer.encoder"				)
		if "patch_embed" in key:
			# replace for example patch_embed1 by patch_embeddings.0
			UpperCAmelCase_     :      Optional[int]						=  key[key.find("patch_embed"				) + len("patch_embed"				)]
			UpperCAmelCase_     :      Optional[int]						=  key.replace(F'''patch_embed{idx}'''	,				F'''patch_embeddings.{int(_SCREAMING_SNAKE_CASE				)-1}'''				)
		if "norm" in key:
			UpperCAmelCase_     :      List[Any]						=  key.replace("norm"	,				"layer_norm"				)
		if "segformer.encoder.layer_norm" in key:
			# replace for example layer_norm1 by layer_norm.0
			UpperCAmelCase_     :      Dict						=  key[key.find("segformer.encoder.layer_norm"				) + len("segformer.encoder.layer_norm"				)]
			UpperCAmelCase_     :      List[str]						=  key.replace(F'''layer_norm{idx}'''	,				F'''layer_norm.{int(_SCREAMING_SNAKE_CASE				)-1}'''				)
		if "layer_norm1" in key:
			UpperCAmelCase_     :      int						=  key.replace("layer_norm1"	,				"layer_norm_1"				)
		if "layer_norm2" in key:
			UpperCAmelCase_     :      str						=  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_     :      Any						=  key.replace(F'''block{idx}'''	,				F'''block.{int(_SCREAMING_SNAKE_CASE				)-1}'''				)
		if "attn.q" in key:
			UpperCAmelCase_     :      List[str]						=  key.replace("attn.q"	,				"attention.self.query"				)
		if "attn.proj" in key:
			UpperCAmelCase_     :      Union[str, Any]						=  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_     :      int						=  key.replace("fc1"	,				"dense1"				)
		if "fc2" in key:
			UpperCAmelCase_     :      int						=  key.replace("fc2"	,				"dense2"				)
		if "linear_pred" in key:
			UpperCAmelCase_     :      List[str]						=  key.replace("linear_pred"	,				"classifier"				)
		if "linear_fuse" in key:
			UpperCAmelCase_     :      Union[str, Any]						=  key.replace("linear_fuse.conv"	,				"linear_fuse"				)
			UpperCAmelCase_     :      Dict						=  key.replace("linear_fuse.bn"	,				"batch_norm"				)
		if "linear_c" in key:
			# replace for example linear_c4 by linear_c.3
			UpperCAmelCase_     :      List[str]						=  key[key.find("linear_c"				) + len("linear_c"				)]
			UpperCAmelCase_     :      List[Any]						=  key.replace(F'''linear_c{idx}'''	,				F'''linear_c.{int(_SCREAMING_SNAKE_CASE				)-1}'''				)
		if key.startswith("head"				):
			UpperCAmelCase_     :      Dict						=  key.replace("head"	,				"classifier"				)
		UpperCAmelCase_     :      str						=  value

	return new_state_dict


def  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					List[Any]:


	"""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)
			UpperCAmelCase_     :      int						=  state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
			UpperCAmelCase_     :      Optional[int]						=  state_dict.pop(F'''segformer.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_     :      int						=  kv_weight[
			    config.hidden_sizes[i] :, :
			]
			UpperCAmelCase_     :      Tuple						=  kv_bias[
			    config.hidden_sizes[i] :
			]


def  a__       (    )     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_     :      int						=  Image.open(requests.get(_SCREAMING_SNAKE_CASE	,				stream=_SCREAMING_SNAKE_CASE				).raw				)

	return image


@torch.no_grad()
def  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]				)     ->					str:


	"""simple docstring"""





	UpperCAmelCase_     :      Tuple						=  SegformerConfig()
	UpperCAmelCase_     :      Any						=  False

	# set attributes based on model_name
	UpperCAmelCase_     :      Optional[Any]						=  "huggingface/label-files"
	if "segformer" in model_name:
		UpperCAmelCase_     :      Optional[int]						=  model_name[len("segformer."				) : len("segformer."				) + 2]
		if "ade" in model_name:
			UpperCAmelCase_     :      List[str]						=  1_50
			UpperCAmelCase_     :      List[Any]						=  "ade20k-id2label.json"
			UpperCAmelCase_     :      str						=  (1, 1_50, 1_28, 1_28)
		elif "city" in model_name:
			UpperCAmelCase_     :      Optional[int]						=  19
			UpperCAmelCase_     :      Union[str, Any]						=  "cityscapes-id2label.json"
			UpperCAmelCase_     :      List[Any]						=  (1, 19, 1_28, 1_28)
		else:
			raise ValueError(F'''Model {model_name} not supported'''				)
	elif "mit" in model_name:
		UpperCAmelCase_     :      Tuple						=  True
		UpperCAmelCase_     :      List[Any]						=  model_name[4:6]
		UpperCAmelCase_     :      Optional[int]						=  10_00
		UpperCAmelCase_     :      str						=  "imagenet-1k-id2label.json"
		UpperCAmelCase_     :      Optional[int]						=  (1, 10_00)
	else:
		raise ValueError(F'''Model {model_name} not supported'''				)

	# set config attributes
	UpperCAmelCase_     :      Tuple						=  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				repo_type="dataset"				)	,				"r"				)				)
	UpperCAmelCase_     :      List[str]						=  {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
	UpperCAmelCase_     :      List[Any]						=  idalabel
	UpperCAmelCase_     :      Any						=  {v: k for k, v in idalabel.items()}
	if size == "b0":
		pass
	elif size == "b1":
		UpperCAmelCase_     :      Optional[Any]						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      Dict						=  2_56
	elif size == "b2":
		UpperCAmelCase_     :      Tuple						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      Union[str, Any]						=  7_68
		UpperCAmelCase_     :      Union[str, Any]						=  [3, 4, 6, 3]
	elif size == "b3":
		UpperCAmelCase_     :      Union[str, Any]						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      int						=  7_68
		UpperCAmelCase_     :      Optional[Any]						=  [3, 4, 18, 3]
	elif size == "b4":
		UpperCAmelCase_     :      List[Any]						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      List[Any]						=  7_68
		UpperCAmelCase_     :      Tuple						=  [3, 8, 27, 3]
	elif size == "b5":
		UpperCAmelCase_     :      Dict						=  [64, 1_28, 3_20, 5_12]
		UpperCAmelCase_     :      Optional[int]						=  7_68
		UpperCAmelCase_     :      List[Any]						=  [3, 6, 40, 3]
	else:
		raise ValueError(F'''Size {size} not supported'''				)

	# load image processor (only resize + normalize)
	UpperCAmelCase_     :      Dict						=  SegformerImageProcessor(
	    image_scale=(5_12, 5_12)	,				keep_ratio=_SCREAMING_SNAKE_CASE	,				align=_SCREAMING_SNAKE_CASE	,				do_random_crop=_SCREAMING_SNAKE_CASE				)

	# prepare image
	UpperCAmelCase_     :      Tuple						=  prepare_img()
	UpperCAmelCase_     :      int						=  image_processor(images=_SCREAMING_SNAKE_CASE	,				return_tensors="pt"				).pixel_values

	logger.info(F'''Converting model {model_name}...'''				)

	# load original state dict
	if encoder_only:
		UpperCAmelCase_     :      List[Any]						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location=torch.device("cpu"				)				)
	else:
		UpperCAmelCase_     :      Optional[int]						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location=torch.device("cpu"				)				)["state_dict"]

	# rename keys
	UpperCAmelCase_     :      Union[str, Any]						=  rename_keys(_SCREAMING_SNAKE_CASE	,				encoder_only=_SCREAMING_SNAKE_CASE				)
	if not encoder_only:
		del state_dict["decode_head.conv_seg.weight"]
		del state_dict["decode_head.conv_seg.bias"]

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

	# create HuggingFace model and load state dict
	if encoder_only:
		UpperCAmelCase_     :      Any						=  False
		UpperCAmelCase_     :      Optional[Any]						=  SegformerForImageClassification(_SCREAMING_SNAKE_CASE				)
	else:
		UpperCAmelCase_     :      Any						=  SegformerForSemanticSegmentation(_SCREAMING_SNAKE_CASE				)
	model.load_state_dict(_SCREAMING_SNAKE_CASE				)
	model.eval()

	# forward pass
	UpperCAmelCase_     :      int						=  model(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      List[str]						=  outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		UpperCAmelCase_     :      Any						=  torch.tensor(
		    [
		        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
		        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
		        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
		    ]				)
	elif model_name == "segformer.b1.512x512.ade.160k":
		UpperCAmelCase_     :      Optional[Any]						=  torch.tensor(
		    [
		        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
		        [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
		        [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
		    ]				)
	elif model_name == "segformer.b2.512x512.ade.160k":
		UpperCAmelCase_     :      Tuple						=  torch.tensor(
		    [
		        [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
		        [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
		        [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
		    ]				)
	elif model_name == "segformer.b3.512x512.ade.160k":
		UpperCAmelCase_     :      Any						=  torch.tensor(
		    [
		        [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
		        [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
		        [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
		    ]				)
	elif model_name == "segformer.b4.512x512.ade.160k":
		UpperCAmelCase_     :      Tuple						=  torch.tensor(
		    [
		        [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
		        [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
		        [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
		    ]				)
	elif model_name == "segformer.b5.640x640.ade.160k":
		UpperCAmelCase_     :      List[Any]						=  torch.tensor(
		    [
		        [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
		        [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
		        [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
		    ]				)
	# Cityscapes checkpoints
	elif model_name == "segformer.b0.1024x1024.city.160k":
		UpperCAmelCase_     :      Any						=  torch.tensor(
		    [
		        [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
		        [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
		        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
		    ]				)
	elif model_name == "segformer.b0.512x1024.city.160k":
		UpperCAmelCase_     :      Union[str, Any]						=  torch.tensor(
		    [
		        [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
		        [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
		        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
		    ]				)
	elif model_name == "segformer.b0.640x1280.city.160k":
		UpperCAmelCase_     :      Optional[Any]						=  torch.tensor(
		    [
		        [
		            [-1.1372E01, -1.2787E01, -1.3477E01],
		            [-1.2536E01, -1.4194E01, -1.4409E01],
		            [-1.3217E01, -1.4888E01, -1.5327E01],
		        ],
		        [
		            [-1.4791E01, -1.7122E01, -1.8277E01],
		            [-1.7163E01, -1.9192E01, -1.9533E01],
		            [-1.7897E01, -1.9991E01, -2.0315E01],
		        ],
		        [
		            [7.6723E-01, 4.1921E-01, -7.7878E-02],
		            [4.7772E-01, 9.5557E-03, -2.8082E-01],
		            [3.6032E-01, -2.4826E-01, -5.1168E-01],
		        ],
		    ]				)
	elif model_name == "segformer.b0.768x768.city.160k":
		UpperCAmelCase_     :      List[str]						=  torch.tensor(
		    [
		        [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
		        [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
		        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
		    ]				)
	elif model_name == "segformer.b1.1024x1024.city.160k":
		UpperCAmelCase_     :      List[str]						=  torch.tensor(
		    [
		        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
		        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
		        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
		    ]				)
	elif model_name == "segformer.b2.1024x1024.city.160k":
		UpperCAmelCase_     :      Union[str, Any]						=  torch.tensor(
		    [
		        [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
		        [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
		        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
		    ]				)
	elif model_name == "segformer.b3.1024x1024.city.160k":
		UpperCAmelCase_     :      int						=  torch.tensor(
		    [
		        [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
		        [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
		        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
		    ]				)
	elif model_name == "segformer.b4.1024x1024.city.160k":
		UpperCAmelCase_     :      Optional[Any]						=  torch.tensor(
		    [
		        [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
		        [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
		        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
		    ]				)
	elif model_name == "segformer.b5.1024x1024.city.160k":
		UpperCAmelCase_     :      List[Any]						=  torch.tensor(
		    [
		        [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
		        [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
		        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
		    ]				)
	else:
		UpperCAmelCase_     :      Any						=  logits.argmax(-1				).item()
		print("Predicted class:"	,				model.config.idalabel[predicted_class_idx]				)

	# verify logits
	if not encoder_only:
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3, :3, :3]	,				_SCREAMING_SNAKE_CASE	,				atol=1E-2				)

	# finally, save model and image processor
	logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
	Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
	model.save_pretrained(_SCREAMING_SNAKE_CASE				)
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					_lowerCamelCase      					=     argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    default="""segformer.b0.512x512.ade.160k""",
					    type=str,
					    help="""Name of the model you'd like to convert.""",
					)
					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."""
					)
					_lowerCamelCase      					=     parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

