






'''simple docstring'''

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     a_						:

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return None






class     a_						:

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        return None






class     a_						(      unittest.TestCase    ):
  lowercase							=				[
      # (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				)  ->				Optional[int]:



        """simple docstring"""






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

  @require_torch
  @slow
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






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

  @require_torch
  @slow
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        from transformers import BertModel

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

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

  @require_tf
  @slow
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






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

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

  @require_torch
  @slow
  def   A__						(				self				)  ->				int:



        """simple docstring"""






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

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






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

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

              # Export
              convert(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

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

  @require_torch
  @require_tokenizers
  @slow
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        from transformers import BertModel

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

  @require_tf
  @require_tokenizers
  @slow
  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        from transformers import TFBertModel

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = FeatureExtractionPipeline(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = infer_shapes(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

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

        # 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				)  ->				str:



        """simple docstring"""






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

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

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

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

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

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		1				)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		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				)  ->				Dict:



        """simple docstring"""






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







'''simple docstring'''

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()
SCREAMING_SNAKE_CASE__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






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

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






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


  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






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






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				0
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

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

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

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True   )->			Optional[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase      = timm.create_model(__UpperCamelCase		,						pretrained=__UpperCamelCase   ).eval()
            UpperCamelCase      = ResNetForImageClassification(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

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

      UpperCamelCase      = 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
            UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			str:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

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

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

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

      UpperCamelCase      = {
          """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(__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__":
     SCREAMING_SNAKE_CASE__     =       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.',
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     SCREAMING_SNAKE_CASE__     =       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)
