



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


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

@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Tensor     ,  UpperCamelCase :							Tensor				)->Any:
      __SCREAMING_SNAKE_CASE       :    int       =	len(list(m.modules()				)				) == 1 or isinstance(UpperCamelCase     ,  nn.Convad				) or isinstance(UpperCamelCase     ,  nn.BatchNormad				)
      if has_not_submodules:
       self.traced.append(UpperCamelCase				)





     def __call__(       self :							List[str]     ,  UpperCamelCase :							Tensor				)->List[str]:
      for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook				)				)
      self.module(UpperCamelCase				)
      [x.remove() for x in self.handles]
      return self






     @property
     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda UpperCamelCase				: len(list(x.state_dict().keys()				)				) > 0     ,  self.traced				)				)






@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			42
     lowerCAmelCase					=			0
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )





     def __call__(       self :							Any     ,  UpperCamelCase :							Tensor				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Tracker(self.dest				)(UpperCamelCase				).parametrized
      __SCREAMING_SNAKE_CASE       :    int       =	Tracker(self.src				)(UpperCamelCase				).parametrized

      __SCREAMING_SNAKE_CASE       :    Dict       =	list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.src_skip     ,  UpperCamelCase				)				)
      __SCREAMING_SNAKE_CASE       :    Any       =	list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.dest_skip     ,  UpperCamelCase				)				)

      if len(UpperCamelCase				) != len(UpperCamelCase				):
       raise Exception(
           F"""Numbers of operations are different. Source module has {len(UpperCamelCase				)} operations while"""
           F""" destination module has {len(UpperCamelCase				)}."""				)

      for dest_m, src_m in zip(UpperCamelCase     ,  UpperCamelCase				):
       dest_m.load_state_dict(src_m.state_dict()				)
       if self.verbose == 1:
        print(F"""Transfered from={src_m} to={dest_m}"""				)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     ResNetConfig		,	__lowerCamelCase   :     Path		,	__lowerCamelCase   :     bool = True     ):



 """simple docstring"""





 print(F"""Converting {name}..."""     )
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	timm.create_model(__lowerCamelCase		,	pretrained=__lowerCamelCase     ).eval()
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ResNetForImageClassification(__lowerCamelCase     ).eval()
  __SCREAMING_SNAKE_CASE       :    int       =	ModuleTransfer(src=__lowerCamelCase		,	dest=__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.randn((1, 3, 224, 224)     )
  module_transfer(__lowerCamelCase     )

 assert torch.allclose(from_model(__lowerCamelCase     )		,	our_model(__lowerCamelCase     ).logits     ), "The model logits don't match the original one."

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	F"""resnet{"-".join(name.split("resnet"     )     )}"""
 print(__lowerCamelCase     )

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,	commit_message="Add model"		,	use_temp_dir=__lowerCamelCase		,	)

  # we can use the convnext one
  __SCREAMING_SNAKE_CASE       :    int       =	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     )
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,	commit_message="Add image processor"		,	use_temp_dir=__lowerCamelCase		,	)

  print(F"""Pushed {checkpoint_name}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Path		,	__lowerCamelCase   :     str = None		,	__lowerCamelCase   :     bool = True     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"imagenet-1k-id2label.json"
 __SCREAMING_SNAKE_CASE       :    int       =	1000
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(1, num_labels)

 __SCREAMING_SNAKE_CASE       :    int       =	"huggingface/label-files"
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_labels
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	json.load(open(hf_hub_download(__lowerCamelCase		,	__lowerCamelCase		,	repo_type="dataset"     )		,	"r"     )     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{int(__lowerCamelCase     ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	idalabel
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{v: k for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	partial(__lowerCamelCase		,	num_labels=__lowerCamelCase		,	idalabel=__lowerCamelCase		,	labelaid=__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	{
     "resnet18": ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,	hidden_sizes=[64, 128, 256, 512]		,	layer_type="basic"     ),
     "resnet26": ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet34": ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,	hidden_sizes=[64, 128, 256, 512]		,	layer_type="basic"     ),
     "resnet50": ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet101": ImageNetPreTrainedConfig(
         depths=[3, 4, 23, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet152": ImageNetPreTrainedConfig(
         depths=[3, 8, 36, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
 }

 if model_name:
  convert_weight_and_push(__lowerCamelCase		,	names_to_config[model_name]		,	__lowerCamelCase		,	__lowerCamelCase     )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 return config, expected_shape


if __name__ == "__main__":
      _lowerCamelCase						    =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
              """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      _lowerCamelCase						    =  parser.parse_args()
      _lowerCamelCase						    =  args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)











# 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       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""philschmid/bart-large-cnn-samsum"""
     lowerCAmelCase					=			(
         """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."""
     )
     lowerCAmelCase					=			"""summarizer"""
     lowerCAmelCase					=			AutoTokenizer
     lowerCAmelCase					=			AutoModelForSeqaSeqLM

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






