


"""simple docstring"""




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()
lowercase_			     =  logging.get_logger(__name__)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      Optional[Any]:
		__a           =			'''huggingface/label-files'''
		__a           =			'''imagenet-1k-id2label.json'''
		__a           =			json.load(open(hf_hub_download(lowerCAmelCase__    ,					lowerCAmelCase__    ,					repo_type='''dataset'''			)    ,					'''r'''			)			)
		__a           =			{int(lowerCAmelCase__			): v for k, v in idalabel.items()}
		__a           =			{v: k for k, v in idalabel.items()}

		__a           =			'''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"
		__a           =			BitConfig(
		    conv_layer=lowerCAmelCase__    ,					num_labels=1000    ,					idalabel=lowerCAmelCase__    ,					labelaid=lowerCAmelCase__    ,					)

		return config

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]			) ->      List[Any]:
		if "stem.conv" in name:
				__a           =			name.replace('''stem.conv'''    ,					'''bit.embedder.convolution'''			)
		if "blocks" in name:
				__a           =			name.replace('''blocks'''    ,					'''layers'''			)
		if "head.fc" in name:
				__a           =			name.replace('''head.fc'''    ,					'''classifier.1'''			)
		if name.startswith('''norm'''			):
				__a           =			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				__a           =			'''bit.encoder.''' + name

		return name

def        lowercase							(		) ->      List[Any]:
		__a           =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__a           =			Image.open(requests.get(lowerCAmelCase__    ,					stream=lowerCAmelCase__			).raw			)
		return im

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     List[Any]=False			) ->      Dict:
		__a           =			get_config(lowerCAmelCase__			)

		# load original model from timm
		__a           =			create_model(lowerCAmelCase__    ,					pretrained=lowerCAmelCase__			)
		timm_model.eval()

		# load state_dict of original model
		__a           =			timm_model.state_dict()
		for key in state_dict.copy().keys():
				__a           =			state_dict.pop(lowerCAmelCase__			)
				__a           =			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		__a           =			BitForImageClassification(lowerCAmelCase__			)
		model.eval()
		model.load_state_dict(lowerCAmelCase__			)

		# create image processor
		__a           =			create_transform(**resolve_data_config({}    ,					model=lowerCAmelCase__			)			)
		__a           =			transform.transforms

		__a           =			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		__a           =			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()    ,					)

		__a           =			prepare_img()
		__a           =			transform(lowerCAmelCase__			).unsqueeze(0			)
		__a           =			processor(lowerCAmelCase__    ,					return_tensors='''pt'''			).pixel_values

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

		# verify logits
		with torch.no_grad():
				__a           =			model(lowerCAmelCase__			)
				__a           =			outputs.logits

		print('''Logits:'''    ,					logits[0, :3]			)
		print('''Predicted class:'''    ,					model.config.idalabel[logits.argmax(-1			).item()]			)
		__a           =			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__":
				lowercase_			     =  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.",
				)

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










"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			parser.add_parser(
				    '''convert'''							, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							, )
				train_parser.add_argument('''--model_type'''							, type=_a							, required=_a							, help='''Model\'s type.'''      )
				train_parser.add_argument(
				    '''--tf_checkpoint'''							, type=_a							, required=_a							, help='''TensorFlow checkpoint path or folder.'''      )
				train_parser.add_argument(
				    '''--pytorch_dump_output'''							, type=_a							, required=_a							, help='''Path to the PyTorch saved model output.'''      )
				train_parser.add_argument('''--config'''							, type=_a							, default=''''''							, help='''Configuration file path or folder.'''      )
				train_parser.add_argument(
				    '''--finetuning_task_name'''							, type=_a							, default=_a							, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							, )
				train_parser.set_defaults(func=_a      )

		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

				self._logger.info(f'''Loading model {model_type}'''      )
				__a           =			model_type
				__a           =			tf_checkpoint
				__a           =			pytorch_dump_output
				__a           =			config
				__a           =			finetuning_task_name





		def 			__UpperCAmelCase       (  self      ):
				if self._model_type == "albert":
						try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "bert":
						try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "funnel":
						try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "t5":
						try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "transfo_xl":
						try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						if "ckpt" in self._tf_checkpoint.lower():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _a							, self._config							, self._pytorch_dump_output							, _a      )
				elif self._model_type == "gpt2":
						try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "xlnet":
						try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint							, self._config							, self._pytorch_dump_output							, self._finetuning_task_name      )
				elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				else:
						raise ValueError(
						    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''      )






