





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 				__lowerCAmelCase       :







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        return None







class 				__lowerCAmelCase       :







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        return None







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







      @require_tf
      @slow
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(lowerCAmelCase__		,      "tf"		,      1_2		,      **lowerCAmelCase__							)







      @require_torch
      @slow
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(lowerCAmelCase__		,      "pt"		,      1_2		,      **lowerCAmelCase__							)







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



        '''simple docstring'''




        from transformers import BertModel

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

        with TemporaryDirectory() as bert_save_dir:
          a__	:					Any										=BertModel(BertConfig(vocab_size=len(lowerCAmelCase__							)							)							)
          model.save_pretrained(lowerCAmelCase__							)
          self._test_export(lowerCAmelCase__		,      "pt"		,      1_2		,      lowerCAmelCase__							)







      @require_tf
      @slow
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          a__	:					Optional[int]										=self._test_export(lowerCAmelCase__		,      "tf"		,      1_2		,      **lowerCAmelCase__							)
          a__	:					List[str]										=quantize(Path(lowerCAmelCase__							)							)

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







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



        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          a__	:					List[str]										=self._test_export(lowerCAmelCase__		,      "pt"		,      1_2		,      **lowerCAmelCase__							)
          a__	:					List[Any]										=quantize(lowerCAmelCase__							)

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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__=None		,      **lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        try:
          # Compute path
          with TemporaryDirectory() as tempdir:
            a__	:					Any										=Path(lowerCAmelCase__							).joinpath("model.onnx"							)

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

          # Export
          convert(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)

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







      @require_torch
      @require_tokenizers
      @slow
      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        from transformers import BertModel

        a__	:					List[Any]										=BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"							)							)
        a__	:					int										=BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"							)
        self._test_infer_dynamic_axis(lowerCAmelCase__		,      lowerCAmelCase__		,      "pt"							)







      @require_tf
      @require_tokenizers
      @slow
      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        from transformers import TFBertModel

        a__	:					Any										=TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"							)							)
        a__	:					str										=BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"							)
        self._test_infer_dynamic_axis(lowerCAmelCase__		,      lowerCAmelCase__		,      "tf"							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					int										=FeatureExtractionPipeline(lowerCAmelCase__		,      lowerCAmelCase__							)

        a__	:					int										=["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
        a__					,							a__					,							a__					,							a__	:					str										=infer_shapes(lowerCAmelCase__		,      lowerCAmelCase__							)

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

        # 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    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					int										=["input_ids", "attention_mask", "token_type_ids"]
        a__	:					Any										={"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
        a__					,							a__	:					Tuple										=ensure_valid_input(FuncContiguousArgs()		,      lowerCAmelCase__		,      lowerCAmelCase__							)

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

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

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

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        a__					,							a__	:					Any										=ensure_valid_input(FuncNonContiguousArgs()		,      lowerCAmelCase__		,      lowerCAmelCase__							)

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

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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[Any]										=generate_identified_filename(Path("/home/something/my_fake_model.onnx"							)		,      "-test"							)
        self.assertEqual("/home/something/my_fake_model-test.onnx"		,      generated.as_posix()							)













import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






