


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






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

							lowercase		:str       							=				'''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"
							lowercase		:Tuple       							=				BitConfig(
							    conv_layer=__snake_case				,				num_labels=1000				,				idalabel=__snake_case				,				labelaid=__snake_case				,				)

							return config






def       lowerCamelCase					(a_						:Optional[int])     ->							Dict:





							if "stem.conv" in name:
														lowercase		:int       							=				name.replace('''stem.conv'''				,				'''bit.embedder.convolution''')
							if "blocks" in name:
														lowercase		:Dict       							=				name.replace('''blocks'''				,				'''layers''')
							if "head.fc" in name:
														lowercase		:Dict       							=				name.replace('''head.fc'''				,				'''classifier.1''')
							if name.startswith('''norm'''):
														lowercase		:str       							=				'''bit.''' + name
							if "bit" not in name and "classifier" not in name:
														lowercase		:str       							=				'''bit.encoder.''' + name

							return name






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






@torch.no_grad()
def       lowerCamelCase					(a_						:Tuple				,				a_						:str				,				a_						:Dict=False)     ->							Union[str, Any]:
							lowercase		:Optional[Any]       							=				get_config(__snake_case)

							# load original model from timm
							lowercase		:List[Any]       							=				create_model(__snake_case				,				pretrained=__snake_case)
							timm_model.eval()

							# load state_dict of original model
							lowercase		:Union[str, Any]       							=				timm_model.state_dict()
							for key in state_dict.copy().keys():
														lowercase		:Tuple       							=				state_dict.pop(__snake_case)
														lowercase		:Tuple       							=				val.squeeze() if '''head''' in key else val

							# load HuggingFace model
							lowercase		:Dict       							=				BitForImageClassification(__snake_case)
							model.eval()
							model.load_state_dict(__snake_case)

							# create image processor
							lowercase		:str       							=				create_transform(**resolve_data_config({}				,				model=__snake_case))
							lowercase		:Any       							=				transform.transforms

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

							lowercase		:Optional[Any]       							=				BitImageProcessor(
							    do_resize=__snake_case				,				size={'''shortest_edge''': timm_transforms[0].size}				,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,				do_center_crop=__snake_case				,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}				,				do_normalize=__snake_case				,				image_mean=timm_transforms[-1].mean.tolist()				,				image_std=timm_transforms[-1].std.tolist()				,				)

							lowercase		:List[Any]       							=				prepare_img()
							lowercase		:Union[str, Any]       							=				transform(__snake_case).unsqueeze(0)
							lowercase		:Dict       							=				processor(__snake_case				,				return_tensors='''pt''').pixel_values

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

							# verify logits
							with torch.no_grad():
														lowercase		:Optional[Any]       							=				model(__snake_case)
														lowercase		:Optional[int]       							=				outputs.logits

							print('''Logits:'''				,				logits[0, :3])
							print('''Predicted class:'''				,				model.config.idalabel[logits.argmax(-1).item()])
							lowercase		:Tuple       							=				timm_model(__snake_case)
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(__snake_case				,				outputs.logits				,				atol=1E-3)
							print('''Looks ok!''')

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

							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__":
	UpperCAmelCase      		=			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.''',
	)

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





"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''				,				a_) is not None:
              return prompt_or_repo_id

       lowercase		:List[str]       							=				cached_file(
           a_				,				PROMPT_FILES[mode]				,				repo_type='''dataset'''				,				user_agent={'''agent''': agent_name})
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              return f.read()

