


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    UpperCAmelCase__	:






    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         return None






class    UpperCAmelCase__	:






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_   )  ->	str:
         return None






class    UpperCAmelCase__	( unittest.TestCase		):
    UpperCAmelCase__						:		str              =  [
        # (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   )  ->	Union[str, Any]:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(A_ ,       'tf' ,       12 ,       **A_   )






    @require_torch
    @slow
    def _a (      self   )  ->	int:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(A_ ,       'pt' ,       12 ,       **A_   )






    @require_torch
    @slow
    def _a (      self   )  ->	Tuple:
         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(A_   )   )
              vocab_file.flush()
              __UpperCamelCase   							=BertTokenizerFast(vocab_file.name   )

         with TemporaryDirectory() as bert_save_dir:
              __UpperCamelCase   							=BertModel(BertConfig(vocab_size=len(A_   )   )   )
              model.save_pretrained(A_   )
              self._test_export(A_ ,       'pt' ,       12 ,       A_   )






    @require_tf
    @slow
    def _a (      self   )  ->	str:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              __UpperCamelCase   							=self._test_export(A_ ,       'tf' ,       12 ,       **A_   )
              __UpperCamelCase   							=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 _a (      self   )  ->	str:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              __UpperCamelCase   							=self._test_export(A_ ,       'pt' ,       12 ,       **A_   )
              __UpperCamelCase   							=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 _a (      self ,       A_ ,       A_ ,       A_ ,       A_=None ,       **A_   )  ->	List[Any]:
         try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                   __UpperCamelCase   							=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 _a (      self   )  ->	Dict:
         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(A_ ,       A_ ,       'pt'   )






    @require_tf
    @require_tokenizers
    @slow
    def _a (      self   )  ->	List[Any]:
         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(A_ ,       A_ ,       'tf'   )






    def _a (      self ,       A_ ,       A_ ,       A_   )  ->	str:
         __UpperCamelCase   							=FeatureExtractionPipeline(A_ ,       A_   )

         __UpperCamelCase   							=['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=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 _a (      self   )  ->	Dict:
         __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() ,       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)
         __UpperCamelCase							,				__UpperCamelCase   							=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 _a (      self   )  ->	Tuple:
         __UpperCamelCase   							=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_torch_available,
    is_vision_available,
)


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





