


"""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()
snake_case__		:     Dict    				=				logging.get_logger(__name__)


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

      lowerCAmelCase					:	Any       =   '''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					:	Tuple       =   BitConfig(
          conv_layer=_snake_case ,   num_labels=1000 ,   idalabel=_snake_case ,   labelaid=_snake_case ,   )

      return config


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

      return name


def      _snake_case						(						):
      lowerCAmelCase					:	List[Any]       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase					:	Optional[Any]       =   Image.open(requests.get(_snake_case ,   stream=_snake_case			).raw			)
      return im


@torch.no_grad()
def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			Union[str, Any] ,   _snake_case				:			Optional[int]=False			):
      lowerCAmelCase					:	Any       =   get_config(_snake_case			)

      # load original model from timm
      lowerCAmelCase					:	Dict       =   create_model(_snake_case ,   pretrained=_snake_case			)
      timm_model.eval()

      # load state_dict of original model
      lowerCAmelCase					:	Any       =   timm_model.state_dict()
      for key in state_dict.copy().keys():
            lowerCAmelCase					:	Optional[Any]       =   state_dict.pop(_snake_case			)
            lowerCAmelCase					:	Dict       =   val.squeeze() if '''head''' in key else val

      # load HuggingFace model
      lowerCAmelCase					:	List[str]       =   BitForImageClassification(_snake_case			)
      model.eval()
      model.load_state_dict(_snake_case			)

      # create image processor
      lowerCAmelCase					:	Optional[int]       =   create_transform(**resolve_data_config({} ,   model=_snake_case			)			)
      lowerCAmelCase					:	Optional[Any]       =   transform.transforms

      lowerCAmelCase					:	Optional[int]       =   {
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

      lowerCAmelCase					:	Optional[int]       =   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() ,   )

      lowerCAmelCase					:	int       =   prepare_img()
      lowerCAmelCase					:	str       =   transform(_snake_case			).unsqueeze(0			)
      lowerCAmelCase					:	List[Any]       =   processor(_snake_case ,   return_tensors='''pt'''			).pixel_values

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

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

      print('''Logits:''' ,   logits[0, :3]			)
      print('''Predicted class:''' ,   model.config.idalabel[logits.argmax(-1			).item()]			)
      lowerCAmelCase					:	Union[str, Any]       =   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__":
 snake_case__		:     Any    				=				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__		:     Optional[Any]    				=				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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


