"""simple docstring"""





from ...configuration_utils import PretrainedConfig


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    int   =  "bert-generation"


      def __init__(   self		,			a=5_0_3_5_8		,			a=1_0_2_4		,			a=2_4		,			a=1_6		,			a=4_0_9_6		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=5_1_2		,			a=0.02		,			a=1e-12		,			a=0		,			a=2		,			a=1		,			a="absolute"		,			a=True		,			**a		,			)       ->					Optional[int]:
             super().__init__(pad_token_id=a		,			bos_token_id=a		,			eos_token_id=a		,			**a							)

             lowercase__			:		List[Any]       =	vocab_size
             lowercase__			:		List[str]       =	hidden_size
             lowercase__			:		Any       =	num_hidden_layers
             lowercase__			:		str       =	num_attention_heads
             lowercase__			:		List[str]       =	hidden_act
             lowercase__			:		str       =	intermediate_size
             lowercase__			:		List[Any]       =	hidden_dropout_prob
             lowercase__			:		str       =	attention_probs_dropout_prob
             lowercase__			:		Union[str, Any]       =	max_position_embeddings
             lowercase__			:		Optional[Any]       =	initializer_range
             lowercase__			:		str       =	layer_norm_eps
             lowercase__			:		Tuple       =	position_embedding_type
             lowercase__			:		Tuple       =	use_cache






'''simple docstring'''





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_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[Any]:
      return None







class        lowerCAmelCase_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      return None







class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      [
      # (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      )    ->		int:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      from transformers import BertModel

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

      with TemporaryDirectory() as bert_save_dir:
          lowerCAmelCase__ :  Tuple							      =					BertModel(BertConfig(vocab_size=len(__UpperCAmelCase      )      )      )
          model.save_pretrained(__UpperCAmelCase      )
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,__UpperCAmelCase      )




  @require_tf
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowerCAmelCase__ :  Dict							      =					self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  List[str]							      =					quantize(Path(__UpperCAmelCase      )      )

          # Ensure the actual quantized model is not bigger than the original one
          if quantized_path.stat().st_size >= Path(__UpperCAmelCase      ).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:
          lowerCAmelCase__ :  Any							      =					self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					quantize(__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[Any]:
      try:
          # Compute path
          with TemporaryDirectory() as tempdir:
              lowerCAmelCase__ :  Optional[int]							      =					Path(__UpperCAmelCase      ).joinpath("""model.onnx"""      )

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

          # Export
          convert(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase      )

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




  @require_torch
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      from transformers import BertModel

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




  @require_tf
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      from transformers import TFBertModel

      lowerCAmelCase__ :  int							      =					TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
      lowerCAmelCase__ :  Optional[int]							      =					BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
      self._test_infer_dynamic_axis(__UpperCAmelCase  ,__UpperCAmelCase  ,"""tf"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Any							      =					FeatureExtractionPipeline(__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					infer_shapes(__UpperCAmelCase  ,__UpperCAmelCase      )

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

      # 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      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """attention_mask""", """token_type_ids"""]
      lowerCAmelCase__ :  Union[str, Any]							      =					{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					ensure_valid_input(FuncContiguousArgs()  ,__UpperCAmelCase  ,__UpperCAmelCase      )

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

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

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

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

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__UpperCAmelCase      )  ,1      )
      self.assertEqual(len(__UpperCAmelCase      )  ,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      )    ->		Tuple:
      lowerCAmelCase__ :  Dict							      =					generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""      )  ,"""-test"""      )
      self.assertEqual("""/home/something/my_fake_model-test.onnx"""  ,generated.as_posix()      )


