





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				:   Dict						       =						logging.get_logger()


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  field(default_factory=lowerCamelCase__				)
     __UpperCamelCase            =  field(default_factory=lowerCamelCase__				)



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:List[Any]      ,   snake_case							:Tensor      ,   snake_case							:Tensor	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      len(list(m.modules()	)	) == 1 or isinstance(snake_case      ,   nn.Convad	) or isinstance(snake_case      ,   nn.BatchNormad	)
        if has_not_submodules:
           self.traced.append(snake_case	)



     def __call__(				self							:Tuple      ,   snake_case							:Tensor	):





        '''simple docstring'''




        for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook	)	)
        self.module(snake_case	)
        [x.remove() for x in self.handles]
        return self





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return list(filter(lambda snake_case	: len(list(x.state_dict().keys()	)	) > 0      ,   self.traced	)	)




@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  0
     __UpperCamelCase            =  field(default_factory=lowerCamelCase__				)
     __UpperCamelCase            =  field(default_factory=lowerCamelCase__				)



     def __call__(				self							:List[Any]      ,   snake_case							:Tensor	):





        '''simple docstring'''




        A_       :					Dict        =      Tracker(self.dest	)(snake_case	).parametrized
        A_       :					Union[str, Any]        =      Tracker(self.src	)(snake_case	).parametrized

        A_       :					Tuple        =      list(filter(lambda snake_case	: type(snake_case	) not in self.src_skip      ,   snake_case	)	)
        A_       :					int        =      list(filter(lambda snake_case	: type(snake_case	) not in self.dest_skip      ,   snake_case	)	)

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

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




def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			ResNetConfig					,					_lowerCAmelCase			:			Path					,					_lowerCAmelCase			:			bool = True						)			->    Any:
   print(f"Converting {name}..."						)
   with torch.no_grad():
      A_       :					Any        =      timm.create_model(_lowerCAmelCase					,					pretrained=_lowerCAmelCase						).eval()
      A_       :					List[Any]        =      ResNetForImageClassification(_lowerCAmelCase						).eval()
      A_       :					Optional[Any]        =      ModuleTransfer(src=_lowerCAmelCase					,					dest=_lowerCAmelCase						)
      A_       :					Optional[int]        =      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."

   A_       :					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
      A_       :					Optional[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   __snake_case    (       _lowerCAmelCase			:			Path					,					_lowerCAmelCase			:			str = None					,					_lowerCAmelCase			:			bool = True						)			->    Union[str, Any]:
   A_       :					str        =      "imagenet-1k-id2label.json"
   A_       :					str        =      1000
   A_       :					Optional[int]        =      (1, num_labels)

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

   A_       :					str        =      idalabel
   A_       :					Tuple        =      {v: k for k, v in idalabel.items()}

   A_       :					Optional[int]        =      partial(_lowerCAmelCase					,					num_labels=_lowerCAmelCase					,					idalabel=_lowerCAmelCase					,					labelaid=_lowerCAmelCase						)

   A_       :					Any        =      {
       "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				:   Tuple						       =						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				:   Dict						       =						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)







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      act_info[0] == "gated"

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
           raise ValueError(
               f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
           A_       :					Any        =      "past_encoder_sequence + sequence"
           A_       :					Union[str, Any]        =      {0: "batch"}
           A_       :					Optional[int]        =      {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           A_       :					Dict        =      {0: "batch", 1: "decoder_sequence"}
           A_       :					str        =      {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
