
from typing import TYPE_CHECKING

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


lowerCamelCase__    :		str          =   {
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		List[Any]          =   ["""FunnelTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		List[Any]          =   [
         """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FunnelBaseModel""",
         """FunnelForMaskedLM""",
         """FunnelForMultipleChoice""",
         """FunnelForPreTraining""",
         """FunnelForQuestionAnswering""",
         """FunnelForSequenceClassification""",
         """FunnelForTokenClassification""",
         """FunnelModel""",
         """FunnelPreTrainedModel""",
         """load_tf_weights_in_funnel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		List[str]          =   [
         """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFFunnelBaseModel""",
         """TFFunnelForMaskedLM""",
         """TFFunnelForMultipleChoice""",
         """TFFunnelForPreTraining""",
         """TFFunnelForQuestionAnswering""",
         """TFFunnelForSequenceClassification""",
         """TFFunnelForTokenClassification""",
         """TFFunnelModel""",
         """TFFunnelPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

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








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   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   (	unittest.TestCase							):
 __lowerCAmelCase  :     Optional[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):


   '''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      lowercase__     (					self):


   '''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      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

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

   with TemporaryDirectory() as bert_save_dir:
     lowercase__  :   Union[str, Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Any    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """tf"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Tuple    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   try:
     # Compute path
     with TemporaryDirectory() as tempdir:
       lowercase__  :   List[Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Dict    		=      BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Union[str, Any]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   from transformers import TFBertModel

   lowercase__  :   Union[str, Any]    		=      TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Optional[int]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """tf""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
   lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   List[str]    		=      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      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
   lowercase__  :   Tuple    		=      {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
   lowercase__		,    lowercase__  :   Optional[Any]    		=      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)
   lowercase__		,    lowercase__  :   Any    		=      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      lowercase__     (					self):


   '''simple docstring'''




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






