





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 							lowercase__				(			self		: Any     ,				lowercase		: List[str]     ,				lowercase		: List[Any]     ,				lowercase		: Optional[int]						):





        """simple docstring"""




        return None







class 				a_       :





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Optional[Any]     ,				lowercase		: Optional[int]     ,				lowercase		: List[str]     ,				lowercase		: Union[str, Any]						):





        """simple docstring"""




        return None







class 				a_       (    unittest.TestCase							):
     __A								=      [
         # (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		: List[Any]						):





        """simple docstring"""




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





     @require_torch
     @slow
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




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





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





        """simple docstring"""




        from transformers import BertModel

        lowercase_      :Optional[int]         =			['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
        with NamedTemporaryFile(mode="w+t"						) as vocab_file:
           vocab_file.write("\n".join(_A						)						)
           vocab_file.flush()
           lowercase_      :int         =			BertTokenizerFast(vocab_file.name						)

        with TemporaryDirectory() as bert_save_dir:
           lowercase_      :List[Any]         =			BertModel(BertConfig(vocab_size=len(_A						)						)						)
           model.save_pretrained(_A						)
           self._test_export(_A     ,				"pt"     ,				12     ,				_A						)





     @require_tf
     @slow
     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           lowercase_      :int         =			self._test_export(_A     ,				"tf"     ,				12     ,				**_A						)
           lowercase_      :List[Any]         =			quantize(Path(_A						)						)

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





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





        """simple docstring"""




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           lowercase_      :Dict         =			self._test_export(_A     ,				"pt"     ,				12     ,				**_A						)
           lowercase_      :Optional[Any]         =			quantize(_A						)

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





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: str     ,				lowercase		: Optional[int]     ,				lowercase		: List[Any]     ,				lowercase		: List[str]=None     ,				**lowercase		: str						):





        """simple docstring"""




        try:
           # Compute path
           with TemporaryDirectory() as tempdir:
              lowercase_      :List[Any]         =			Path(_A						).joinpath("model.onnx"						)

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

           # Export
           convert(_A     ,				_A     ,				_A     ,				_A     ,				_A     ,				**_A						)

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





     @require_torch
     @require_tokenizers
     @slow
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        from transformers import BertModel

        lowercase_      :Any         =			BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        lowercase_      :Optional[Any]         =			BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(_A     ,				_A     ,				"pt"						)





     @require_tf
     @require_tokenizers
     @slow
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        from transformers import TFBertModel

        lowercase_      :List[Any]         =			TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        lowercase_      :Dict         =			BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(_A     ,				_A     ,				"tf"						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Any     ,				lowercase		: Any     ,				lowercase		: int						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			FeatureExtractionPipeline(_A     ,				_A						)

        lowercase_      :List[str]         =			['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
        lowercase_      :str         =			infer_shapes(_A     ,				_A						)

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

        # 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		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			['input_ids', 'attention_mask', 'token_type_ids']
        lowercase_      :Any         =			{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
        lowercase_      :int         =			ensure_valid_input(FuncContiguousArgs()     ,				_A     ,				_A						)

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

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

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

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        lowercase_      :List[Any]         =			ensure_valid_input(FuncNonContiguousArgs()     ,				_A     ,				_A						)

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(_A						)     ,				1						)
        self.assertEqual(len(_A						)     ,				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		: List[Any]						):





        """simple docstring"""




        lowercase_      :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()						)









'''simple docstring'''


# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Dict 							={
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Union[str, Any] 							=[
     '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XmodForCausalLM''',
     '''XmodForMaskedLM''',
     '''XmodForMultipleChoice''',
     '''XmodForQuestionAnswering''',
     '''XmodForSequenceClassification''',
     '''XmodForTokenClassification''',
     '''XmodModel''',
     '''XmodPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xmod import (
      XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
      XmodForCausalLM,
      XmodForMaskedLM,
      XmodForMultipleChoice,
      XmodForQuestionAnswering,
      XmodForSequenceClassification,
      XmodForTokenClassification,
      XmodModel,
      XmodPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Optional[Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


