



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case		=  logging.getLogger(__name__)



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a=-1):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    label_idx







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Dict    =    mode.value
														_lowerCAmelCase     :							Optional[int]    =    os.path.join(__a,			f"{mode}.txt")
														_lowerCAmelCase     :							Optional[int]    =    1
														_lowerCAmelCase     :							str    =    []
														with open(__a,			encoding="utf-8") as f:
																					_lowerCAmelCase     :							Any    =    []
																					_lowerCAmelCase     :							int    =    []
																					for line in f:
																												if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																																			if words:
																																										examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
																																										guid_index += 1
																																										_lowerCAmelCase     :							List[Any]    =    []
																																										_lowerCAmelCase     :							Any    =    []
																												else:
																																			_lowerCAmelCase     :							int    =    line.split(" ")
																																			words.append(splits[0])
																																			if len(__a) > 1:
																																										labels.append(splits[self.label_idx].replace("\n",			""))
																																			else:
																																										# Examples could have no label for mode = "test"
																																										labels.append("O")
																					if words:
																												examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
														return examples







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    0
														for line in test_input_reader:
																					if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																												writer.write(__a)
																												if not preds_list[example_id]:
																																			example_id += 1
																					elif preds_list[example_id]:
																												_lowerCAmelCase     :							int    =    line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
																												writer.write(__a)
																					else:
																												logger.warning("Maximum sequence length exceeded: No prediction for '%s'.",			line.split()[0])







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												_lowerCAmelCase     :							Dict    =    f.read().splitlines()
																					if "O" not in labels:
																												_lowerCAmelCase     :							List[Any]    =    ["O"] + labels
																					return labels
														else:
																					return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   UpperCAmelCase_    (       a):







							def __init__(   self):

														'''simple docstring'''

														super().__init__(label_idx=-2)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												_lowerCAmelCase     :							Any    =    f.read().splitlines()
																					if "O" not in labels:
																												_lowerCAmelCase     :							Optional[Any]    =    ["O"] + labels
																					return labels
														else:
																					return [
																					    "O",
																					    "B-ADVP",
																					    "B-INTJ",
																					    "B-LST",
																					    "B-PRT",
																					    "B-NP",
																					    "B-SBAR",
																					    "B-VP",
																					    "B-ADJP",
																					    "B-CONJP",
																					    "B-PP",
																					    "I-ADVP",
																					    "I-INTJ",
																					    "I-LST",
																					    "I-PRT",
																					    "I-NP",
																					    "I-SBAR",
																					    "I-VP",
																					    "I-ADJP",
																					    "I-CONJP",
																					    "I-PP",
																					]



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							int    =    mode.value
														_lowerCAmelCase     :							List[str]    =    os.path.join(__a,			f"{mode}.txt")
														_lowerCAmelCase     :							Dict    =    1
														_lowerCAmelCase     :							Optional[int]    =    []

														with open(__a,			encoding="utf-8") as f:
																					for sentence in parse_incr(__a):
																												_lowerCAmelCase     :							List[str]    =    []
																												_lowerCAmelCase     :							Union[str, Any]    =    []
																												for token in sentence:
																																			words.append(token["form"])
																																			labels.append(token["upos"])
																												assert len(__a) == len(__a)
																												if words:
																																			examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
																																			guid_index += 1
														return examples







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    0
														for sentence in parse_incr(__a):
																					_lowerCAmelCase     :							List[Any]    =    preds_list[example_id]
																					_lowerCAmelCase     :							Union[str, Any]    =    ""
																					for token in sentence:
																												out += f"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
																					out += "\n"
																					writer.write(__a)
																					example_id += 1







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												return f.read().splitlines()
														else:
																					return [
																					    "ADJ",
																					    "ADP",
																					    "ADV",
																					    "AUX",
																					    "CCONJ",
																					    "DET",
																					    "INTJ",
																					    "NOUN",
																					    "NUM",
																					    "PART",
																					    "PRON",
																					    "PROPN",
																					    "PUNCT",
																					    "SCONJ",
																					    "SYM",
																					    "VERB",
																					    "X",
																					]





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    "huggingface/label-files"
							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Tuple    =    json.load(open(hf_hub_download(_lowerCamelCase				, _lowerCamelCase				, repo_type="dataset"  )				, "r"  )  )
							_lowerCAmelCase     :							Tuple    =    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
							_lowerCAmelCase     :							Union[str, Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

							# verify pixel values
							assert torch.allclose(_lowerCamelCase				, _lowerCamelCase  )

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

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


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT timm model you'd like to convert.",
						)
						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 to push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
