


"""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 		snake_case_:






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, Any]				):
           return None



class 		snake_case_:






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Optional[Any]				):
           return None



class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					[
         # (model_name, model_kwargs)
         ('''bert-base-cased''', {}),
         ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
     ]






     @require_tf
     @slow
     def      lowerCamelCase__     (	self  :					str				):
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(UpperCamelCase_					,				'''tf'''					,				1_2					,				**UpperCamelCase_				)






     @require_torch
     @slow
     def      lowerCamelCase__     (	self  :					Dict				):
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(UpperCamelCase_					,				'''pt'''					,				1_2					,				**UpperCamelCase_				)






     @require_torch
     @slow
     def      lowerCamelCase__     (	self  :					int				):
           from transformers import BertModel

           lowerCAmelCase					:	List[Any]       =   ['''[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					:	int       =   BertModel(BertConfig(vocab_size=len(UpperCamelCase_				)				)				)
                 model.save_pretrained(UpperCamelCase_				)
                 self._test_export(UpperCamelCase_					,				'''pt'''					,				1_2					,				UpperCamelCase_				)






     @require_tf
     @slow
     def      lowerCamelCase__     (	self  :					List[Any]				):
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCAmelCase					:	List[Any]       =   self._test_export(UpperCamelCase_					,				'''tf'''					,				1_2					,				**UpperCamelCase_				)
                 lowerCAmelCase					:	Tuple       =   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      lowerCamelCase__     (	self  :					List[Any]				):
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCAmelCase					:	Any       =   self._test_export(UpperCamelCase_					,				'''pt'''					,				1_2					,				**UpperCamelCase_				)
                 lowerCAmelCase					:	Union[str, Any]       =   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      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Optional[int]=None					,				**UpperCamelCase_  :					Dict				):
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCAmelCase					:	List[Any]       =   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      lowerCamelCase__     (	self  :					int				):
           from transformers import BertModel

           lowerCAmelCase					:	Any       =   BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''				)				)
           lowerCAmelCase					:	List[Any]       =   BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''				)
           self._test_infer_dynamic_axis(UpperCamelCase_					,				UpperCamelCase_					,				'''pt'''				)






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

           lowerCAmelCase					:	Optional[int]       =   TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''				)				)
           lowerCAmelCase					:	Dict       =   BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''				)
           self._test_infer_dynamic_axis(UpperCamelCase_					,				UpperCamelCase_					,				'''tf'''				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Optional[int]       =   FeatureExtractionPipeline(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	str       =   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	int       =   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      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Tuple       =   ['''input_ids''', '''attention_mask''', '''token_type_ids''']
           lowerCAmelCase					:	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					:	Tuple       =   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      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	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"""





snake_case__		:     List[Any]    				=				'''Tobias Carryer'''

from time import time



class 		snake_case_:






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict=int(time()				)				):  # noqa: B008
           lowerCAmelCase					:	str       =   multiplier
           lowerCAmelCase					:	Optional[int]       =   increment
           lowerCAmelCase					:	Optional[Any]       =   modulo
           lowerCAmelCase					:	Optional[Any]       =   seed






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   (self.multiplier * self.seed + self.increment) % self.modulo
           return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__		:     int    				=				LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
 while True:
  print(lcg.next_number())


