




'''simple docstring'''



import math
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				[]
   _SCREAMING_SNAKE_CASE	=				2
   _SCREAMING_SNAKE_CASE	=				int(math.sqrt(__A  )  )  # Size of every segment
   _SCREAMING_SNAKE_CASE	=				[True] * (end + 1)
   _SCREAMING_SNAKE_CASE	=				[]

   while start <= end:
      if temp[start] is True:
         in_prime.append(__A  )
         for i in range(start * start	,					end + 1	,					__A  ):
            _SCREAMING_SNAKE_CASE	=				False
      start += 1
   prime += in_prime

   _SCREAMING_SNAKE_CASE	=				end + 1
   _SCREAMING_SNAKE_CASE	=				min(2 * end	,					__A  )

   while low <= n:
      _SCREAMING_SNAKE_CASE	=				[True] * (high - low + 1)
      for each in in_prime:
         _SCREAMING_SNAKE_CASE	=				math.floor(low / each  ) * each
         if t < low:
            t += each

         for j in range(__A	,					high + 1	,					__A  ):
            _SCREAMING_SNAKE_CASE	=				False

      for j in range(len(__A  )  ):
         if temp[j] is True:
            prime.append(j + low  )

      _SCREAMING_SNAKE_CASE	=				high + 1
      _SCREAMING_SNAKE_CASE	=				min(high + end	,					__A  )

   return prime






print(sieve(10**6))









'''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 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (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 :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

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

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



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

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



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

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

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

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



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

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



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

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



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

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

      # 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 :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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

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

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncNonContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__lowerCamelCase   )  ,				1   )
      self.assertEqual(len(__lowerCamelCase   )  ,				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 :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_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()   )

