
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		:





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Any    ,   _A				:   Dict    ,   _A				:   Optional[int]       )						->						Dict:
         return None







class 					lowerCamelCase		:





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   List[str]    ,   _A				:   str    ,   _A				:   Dict    ,   _A				:   Optional[Any]       )						->						Optional[Any]:
         return None







class 					lowerCamelCase		(  unittest.TestCase  ):
     UpperCAmelCase__    :							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 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(_A    ,   "tf"    ,   1_2    ,   **_A       )





     @require_torch
     @slow
     def 		UpperCAmelCase(self				:   int       )						->						Dict:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(_A    ,   "pt"    ,   1_2    ,   **_A       )





     @require_torch
     @slow
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Optional[Any]:
         from transformers import BertModel

         snake_case								= ["[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()
             snake_case								= BertTokenizerFast(vocab_file.name       )

         with TemporaryDirectory() as bert_save_dir:
             snake_case								= BertModel(BertConfig(vocab_size=len(_A       )       )       )
             model.save_pretrained(_A       )
             self._test_export(_A    ,   "pt"    ,   1_2    ,   _A       )





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

         snake_case								= BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"       )       )
         snake_case								= BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"       )
         self._test_infer_dynamic_axis(_A    ,   _A    ,   "pt"       )





     @require_tf
     @require_tokenizers
     @slow
     def 		UpperCAmelCase(self				:   int       )						->						List[str]:
         from transformers import TFBertModel

         snake_case								= TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"       )       )
         snake_case								= BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"       )
         self._test_infer_dynamic_axis(_A    ,   _A    ,   "tf"       )





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   str    ,   _A				:   List[str]    ,   _A				:   int       )						->						List[Any]:
         snake_case								= FeatureExtractionPipeline(_A    ,   _A       )

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



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 						lowercase_      (       A__		)							->							bool:
    """simple docstring"""

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


if __name__ == "__main__":
   print(f"{solution() = }")

