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    :				str							     =	logging.get_logger()







@dataclass
class __snake_case       :
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							field(default_factory=UpperCAmelCase_	)
   lowerCAmelCase_														=							field(default_factory=UpperCAmelCase_	)




   def 			__a		(					self    :       str ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   len(list(m.modules()   )   ) == 1 or isinstance(_lowercase ,			nn.Convad   ) or isinstance(_lowercase ,			nn.BatchNormad   )
     if has_not_submodules:
       self.traced.append(_lowercase   )




   def __call__(					self    :       Optional[int] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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







@dataclass
class __snake_case       :
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							field(default_factory=UpperCAmelCase_	)
   lowerCAmelCase_														=							field(default_factory=UpperCAmelCase_	)




   def __call__(					self    :       Optional[Any] ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   Tracker(self.dest   )(_lowercase   ).parametrized
     SCREAMING_SNAKE_CASE__    							=   Tracker(self.src   )(_lowercase   ).parametrized

     SCREAMING_SNAKE_CASE__    							=   list(filter(lambda _lowercase   : type(_lowercase   ) not in self.src_skip ,			_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   list(filter(lambda _lowercase   : type(_lowercase   ) not in self.dest_skip ,			_lowercase   )   )

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

     for dest_m, src_m in zip(_lowercase ,			_lowercase   ):
       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						(      __UpperCamelCase					: str				,  __UpperCamelCase					: ResNetConfig				,  __UpperCamelCase					: Path				,  __UpperCamelCase					: bool = True			)							->      Any:







  """simple docstring"""





  print(f"""Converting {name}..."""			)
  with torch.no_grad():
    SCREAMING_SNAKE_CASE__    							=   timm.create_model(__UpperCamelCase				,  pretrained=__UpperCamelCase			).eval()
    SCREAMING_SNAKE_CASE__    							=   ResNetForImageClassification(__UpperCamelCase			).eval()
    SCREAMING_SNAKE_CASE__    							=   ModuleTransfer(src=__UpperCamelCase				,  dest=__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   torch.randn((1, 3, 2_24, 2_24)			)
    module_transfer(__UpperCamelCase			)

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

  SCREAMING_SNAKE_CASE__    							=   f"""resnet{'-'.join(name.split('resnet'			)			)}"""
  print(__UpperCamelCase			)

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

    # we can use the convnext one
    SCREAMING_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=__UpperCamelCase				,  )

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Path				,  __UpperCamelCase					: str = None				,  __UpperCamelCase					: bool = True			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   'imagenet-1k-id2label.json'
  SCREAMING_SNAKE_CASE__    							=   10_00
  SCREAMING_SNAKE_CASE__    							=   (1, num_labels)

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

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

  SCREAMING_SNAKE_CASE__    							=   partial(__UpperCamelCase				,  num_labels=__UpperCamelCase				,  idalabel=__UpperCamelCase				,  labelaid=__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   {
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]				,  hidden_sizes=[64, 1_28, 2_56, 5_12]				,  layer_type="""basic"""			),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]				,  hidden_sizes=[2_56, 5_12, 10_24, 20_48]				,  layer_type="""bottleneck"""			),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]				,  hidden_sizes=[64, 1_28, 2_56, 5_12]				,  layer_type="""basic"""			),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]				,  hidden_sizes=[2_56, 5_12, 10_24, 20_48]				,  layer_type="""bottleneck"""			),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]				,  hidden_sizes=[2_56, 5_12, 10_24, 20_48]				,  layer_type="""bottleneck"""			),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]				,  hidden_sizes=[2_56, 5_12, 10_24, 20_48]				,  layer_type="""bottleneck"""			),
  }

  if model_name:
    convert_weight_and_push(__UpperCamelCase				,  names_to_config[model_name]				,  __UpperCamelCase				,  __UpperCamelCase			)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
  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    :				Union[str, Any]							     =	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)

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class __snake_case       :




   def 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







class __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							[
       # (model_name, model_kwargs)
       ("bert-base-cased", {}),
       ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
   ]




   @require_tf
   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
     with NamedTemporaryFile(mode="""w+t"""   ) as vocab_file:
       vocab_file.write("""\n""".join(_lowercase   )   )
       vocab_file.flush()
       SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast(vocab_file.name   )

     with TemporaryDirectory() as bert_save_dir:
       SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig(vocab_size=len(_lowercase   )   )   )
       model.save_pretrained(_lowercase   )
       self._test_export(_lowercase ,			"""pt""" ,			12 ,			_lowercase   )




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(Path(_lowercase   )   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




   @require_torch
   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(_lowercase   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ).joinpath("""model.onnx"""   )

       # Remove folder if exists
       if path.parent.exists():
         path.parent.rmdir()

       # Export
       convert(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   )

       return path
     except Exception as e:
       self.fail(_lowercase   )




   @require_torch
   @require_tokenizers
   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""pt"""   )




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import TFBertModel

     SCREAMING_SNAKE_CASE__    							=   TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""tf"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   infer_shapes(_lowercase ,			_lowercase   )

     # Assert all variables are present
     self.assertEqual(len(_lowercase   ) ,			len(_lowercase   )   )
     self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
     self.assertSequenceEqual(variable_names[:3] ,			_lowercase   )
     self.assertSequenceEqual(variable_names[3:] ,			_lowercase   )

     # Assert inputs are {0: batch, 1: sequence}
     for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
       self.assertDictEqual(shapes[var_name] ,			{0: """batch""", 1: """sequence"""}   )

     # Assert outputs are {0: batch, 1: sequence} and {0: batch}
     self.assertDictEqual(shapes["""output_0"""] ,			{0: """batch""", 1: """sequence"""}   )
     self.assertDictEqual(shapes["""output_1"""] ,			{0: """batch"""}   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """attention_mask""", """token_type_ids"""]
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(_lowercase   ) ,			3   )

     # Should have exactly the same input names
     self.assertEqual(set(_lowercase   ) ,			set(_lowercase   )   )

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(_lowercase ,			(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])   )

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncNonContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(_lowercase   ) ,			1   )
     self.assertEqual(len(_lowercase   ) ,			1   )

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0] ,			tokens["""input_ids"""]   )
     self.assertEqual(ordered_input_names[0] ,			"""input_ids"""   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""   ) ,			"""-test"""   )
     self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,			generated.as_posix()   )
