


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					:


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return None






class      UpperCamelCase					:


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return None






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Tuple									=[
       # (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		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase_   ,							'''tf'''   ,							12   ,							**UpperCamelCase_		)
   @require_torch
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							**UpperCamelCase_		)
   @require_torch
   @slow
   def 		UpperCamelCase  (     self		):
        from transformers import BertModel

        lowercase_       :List[str]     =       ['''[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()
             lowercase_       :Optional[Any]     =       BertTokenizerFast(vocab_file.name		)

        with TemporaryDirectory() as bert_save_dir:
             lowercase_       :List[Any]     =       BertModel(BertConfig(vocab_size=len(UpperCamelCase_		)		)		)
             model.save_pretrained(UpperCamelCase_		)
             self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							UpperCamelCase_		)
   @require_tf
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             lowercase_       :str     =       self._test_export(UpperCamelCase_   ,							'''tf'''   ,							12   ,							**UpperCamelCase_		)
             lowercase_       :int     =       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		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             lowercase_       :Dict     =       self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							**UpperCamelCase_		)
             lowercase_       :int     =       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_		):
        try:
             # Compute path
             with TemporaryDirectory() as tempdir:
                  lowercase_       :Optional[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 		UpperCamelCase  (     self		):
        from transformers import BertModel

        lowercase_       :int     =       BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''		)		)
        lowercase_       :Optional[int]     =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''		)
        self._test_infer_dynamic_axis(UpperCamelCase_   ,							UpperCamelCase_   ,							'''pt'''		)
   @require_tf
   @require_tokenizers
   @slow
   def 		UpperCamelCase  (     self		):
        from transformers import TFBertModel

        lowercase_       :Optional[int]     =       TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''		)		)
        lowercase_       :Any     =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''		)
        self._test_infer_dynamic_axis(UpperCamelCase_   ,							UpperCamelCase_   ,							'''tf'''		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       FeatureExtractionPipeline(UpperCamelCase_   ,							UpperCamelCase_		)

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





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!'''		)

     return velocity / c




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     return np.array(
         [
             [gamma(_a		), -gamma(_a		) * beta(_a		), 0, 0],
             [-gamma(_a		) * beta(_a		), gamma(_a		), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]		)




def     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





     if event is None:
          lowercase_       :Optional[int]     =       np.array([ct, x, y, z]		)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_a		) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   SCREAMING_SNAKE_CASE       :		List[str]         =				transform(29_979_245)
   print("Example of four vector: ")
   print(f"ct' = {four_vector[0]}")
   print(f"x' = {four_vector[1]}")
   print(f"y' = {four_vector[2]}")
   print(f"z' = {four_vector[3]}")

   # Substitute symbols with numerical values
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

