

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
      from transformers import (
          AutoConfig,
          BertConfig,
          GPTaConfig,
          TaConfig,
          TFAutoModel,
          TFAutoModelForCausalLM,
          TFAutoModelForMaskedLM,
          TFAutoModelForPreTraining,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSequenceClassification,
          TFAutoModelWithLMHead,
          TFBertForMaskedLM,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertModel,
          TFGPTaLMHeadModel,
          TFRobertaForMaskedLM,
          TFTaForConditionalGeneration,
      )
      from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
      from transformers import (
          AutoModel,
          AutoModelForCausalLM,
          AutoModelForMaskedLM,
          AutoModelForPreTraining,
          AutoModelForQuestionAnswering,
          AutoModelForSeqaSeqLM,
          AutoModelForSequenceClassification,
          AutoModelWithLMHead,
          BertForMaskedLM,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          BertModel,
          GPTaLMHeadModel,
          RobertaForMaskedLM,
          TaForConditionalGeneration,
      )




@is_pt_tf_cross_test
class   UpperCamelCase			(  unittest.TestCase ):

    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     for model_name in ["bert-base-uncased"]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModel.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModel.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     for model_name in ["bert-base-uncased"]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForPreTraining.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForPreTraining.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForCausalLM.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      TFAutoModelForCausalLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForCausalLM.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      AutoModelForCausalLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelWithLMHead.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelWithLMHead.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForMaskedLM.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      TFAutoModelForMaskedLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForMaskedLM.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      AutoModelForMaskedLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForSeqaSeqLM.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      TFAutoModelForSeqaSeqLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForSeqaSeqLM.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      AutoModelForSeqaSeqLM.from_pretrained(
          snake_case__				,     output_loading_info=snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     for model_name in ["bert-base-uncased"]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForSequenceClassification.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForSequenceClassification.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    @slow
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     for model_name in ["bert-base-uncased"]:
      SCREAMING_SNAKE_CASE		    =      AutoConfig.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      TFAutoModelForQuestionAnswering.from_pretrained(snake_case__				,     from_pt=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      AutoModelForQuestionAnswering.from_pretrained(snake_case__				,     from_tf=snake_case__     )
      self.assertIsNotNone(snake_case__     )
      self.assertIsInstance(snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFAutoModelWithLMHead.from_pretrained(snake_case__				,     from_pt=snake_case__     )
     self.assertIsInstance(snake_case__				,     snake_case__     )
     self.assertEqual(model.num_parameters()				,     1_4_4_1_0     )
     self.assertEqual(model.num_parameters(only_trainable=snake_case__     )				,     1_4_4_1_0     )

     SCREAMING_SNAKE_CASE		    =      AutoModelWithLMHead.from_pretrained(snake_case__				,     from_tf=snake_case__     )
     self.assertIsInstance(snake_case__				,     snake_case__     )
     self.assertEqual(model.num_parameters()				,     1_4_4_1_0     )
     self.assertEqual(model.num_parameters(only_trainable=snake_case__     )				,     1_4_4_1_0     )




    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFAutoModelWithLMHead.from_pretrained(snake_case__				,     from_pt=snake_case__     )
     self.assertIsInstance(snake_case__				,     snake_case__     )
     self.assertEqual(model.num_parameters()				,     1_4_4_1_0     )
     self.assertEqual(model.num_parameters(only_trainable=snake_case__     )				,     1_4_4_1_0     )

     SCREAMING_SNAKE_CASE		    =      AutoModelWithLMHead.from_pretrained(snake_case__				,     from_tf=snake_case__     )
     self.assertIsInstance(snake_case__				,     snake_case__     )
     self.assertEqual(model.num_parameters()				,     1_4_4_1_0     )
     self.assertEqual(model.num_parameters(only_trainable=snake_case__     )				,     1_4_4_1_0     )




import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

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

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 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(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

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

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

