





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	:





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Optional[int]							,    A			:   Optional[int]							,    A			:   List[Any]		):
         return None







class    __snake_case	:





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   int							,    A			:   Dict							,    A			:   Tuple							,    A			:   Any		):
         return None







class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







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




       @require_torch
       @slow
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           self._test_export(A							,    """pt"""							,    12							,    **A		)




       @require_torch
       @slow
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         from transformers import BertModel

         __snake_case:						Union[str, Any]			  =						["""[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()
           __snake_case:						Optional[Any]			  =						BertTokenizerFast(vocab_file.name		)

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




       @require_tf
       @slow
       def 							UpperCAmelCase__			(  self			:   Dict		):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __snake_case:						Union[str, Any]			  =						self._test_export(A							,    """tf"""							,    12							,    **A		)
           __snake_case:						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 							UpperCAmelCase__			(  self			:   List[Any]		):
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __snake_case:						Any			  =						self._test_export(A							,    """pt"""							,    12							,    **A		)
           __snake_case:						Optional[int]			  =						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 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   List[Any]							,    A			:   Union[str, Any]							,    A			:   Any							,    A			:   Tuple=None							,    **A			:   List[str]		):
         try:
           # Compute path
           with TemporaryDirectory() as tempdir:
             __snake_case:						Union[str, 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 							UpperCAmelCase__			(  self			:   str		):
         from transformers import BertModel

         __snake_case:						int			  =						BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""		)		)
         __snake_case:						Optional[int]			  =						BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""		)
         self._test_infer_dynamic_axis(A							,    A							,    """pt"""		)




       @require_tf
       @require_tokenizers
       @slow
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         from transformers import TFBertModel

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




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   List[str]							,    A			:   int							,    A			:   Tuple		):
         __snake_case:						List[str]			  =						FeatureExtractionPipeline(A							,    A		)

         __snake_case:						str			  =						["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
         __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case:						Dict			  =						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 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Dict			  =						["""input_ids""", """attention_mask""", """token_type_ids"""]
         __snake_case:						Union[str, Any]			  =						{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
         __snake_case  ,       __snake_case:						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)
         __snake_case  ,       __snake_case:						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 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Any			  =						generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""		)							,    """-test"""		)
         self.assertEqual("""/home/something/my_fake_model-test.onnx"""							,    generated.as_posix()		)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






