


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__							:						List[Any]							     =   logging.get_logger()
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							List[nn.Module]	   =       field(default_factory=snake_case_				)
     __lowercase				:							list	   =       field(default_factory=snake_case_				)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str     ,		_a:Tensor     ,		_a:Tensor   ):
       snake_case__						= len(list(m.modules()   )   ) == 1 or isinstance(_a     ,		nn.Convad   ) or isinstance(_a     ,		nn.BatchNormad   )
       if has_not_submodules:
         self.traced.append(_a   )

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







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _a   : len(list(x.state_dict().keys()   )   ) > 0     ,		self.traced   )   )




@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							nn.Module
     __lowercase				:							int	   =       0
     __lowercase				:							List	   =       field(default_factory=snake_case_				)
     __lowercase				:							List	   =       field(default_factory=snake_case_				)

     def __call__(					self:Tuple     ,		_a:Tensor   ):
       snake_case__						= Tracker(self.dest   )(_a   ).parametrized
       snake_case__						= Tracker(self.src   )(_a   ).parametrized

       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.src_skip     ,		_a   )   )
       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.dest_skip     ,		_a   )   )

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

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = True				)    ->				int:
  print(F"""Converting {name}..."""				)
  with torch.no_grad():
    snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				).eval()
    snake_case__						= ResNetForImageClassification(__lowerCAmelCase				).eval()
    snake_case__						= ModuleTransfer(src=__lowerCAmelCase							,   dest=__lowerCAmelCase				)
    snake_case__						= 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."

  snake_case__						= 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
    snake_case__						= 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase = None							,   __lowerCAmelCase = True				)    ->				List[Any]:
  snake_case__						= '''imagenet-1k-id2label.json'''
  snake_case__						= 1000
  snake_case__						= (1, num_labels)

  snake_case__						= '''huggingface/label-files'''
  snake_case__						= num_labels
  snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
  snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}

  snake_case__						= idalabel
  snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= partial(__lowerCAmelCase							,   num_labels=__lowerCAmelCase							,   idalabel=__lowerCAmelCase							,   labelaid=__lowerCAmelCase				)

  snake_case__						= {
      '''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__							:						List[str]							     =   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__							:						Tuple							     =   parser.parse_args()
   lowerCamelCase__							:						Path							     =   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)









'''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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




