"""simple docstring"""



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
   from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def 					_lowerCamelCase(					a ,  a ,  a ,  a=None ,  a=None ,  a=None ,  a=None ,  a=None ,  ):
       if attention_mask is None:
              __a     				=  input_ids.ne(config.pad_token_id							)
       if decoder_attention_mask is None:
              __a     				=  decoder_input_ids.ne(config.pad_token_id							)
       if head_mask is None:
              __a     				=  torch.ones(config.encoder_layers ,  config.encoder_attention_heads ,  device=a							)
       if decoder_head_mask is None:
              __a     				=  torch.ones(config.decoder_layers ,  config.decoder_attention_heads ,  device=a							)
       if cross_attn_head_mask is None:
              __a     				=  torch.ones(config.decoder_layers ,  config.decoder_attention_heads ,  device=a							)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=99  ,						lowerCamelCase=16  ,						lowerCamelCase=2  ,						lowerCamelCase=4  ,						lowerCamelCase=4  ,						lowerCamelCase="relu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=20  ,						lowerCamelCase=2  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  seq_length
        __a     				=  is_training
        __a     				=  use_labels
        __a     				=  vocab_size
        __a     				=  hidden_size
        __a     				=  num_hidden_layers
        __a     				=  num_attention_heads
        __a     				=  intermediate_size
        __a     				=  hidden_act
        __a     				=  hidden_dropout_prob
        __a     				=  attention_probs_dropout_prob
        __a     				=  encoder_layerdrop
        __a     				=  decoder_layerdrop
        __a     				=  max_position_embeddings
        __a     				=  eos_token_id
        __a     				=  pad_token_id
        __a     				=  bos_token_id


 def 		a__ (				self		):
        __a     				=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)
        __a     				=  self.eos_token_id  # Eos Token
        __a     				=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for M2M100 the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        __a     				=  input_ids.clamp(self.pad_token_id + 1		)
        __a     				=  decoder_input_ids.clamp(self.pad_token_id + 1		)

        __a     				=  self.get_config()
        __a     				=  prepare_mam_aaa_inputs_dict(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
        return config, inputs_dict


 def 		a__ (				self		):
        return MaMaaaConfig(
            vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						encoder_layers=self.num_hidden_layers  ,						decoder_layers=self.num_hidden_layers  ,						encoder_attention_heads=self.num_attention_heads  ,						decoder_attention_heads=self.num_attention_heads  ,						encoder_ffn_dim=self.intermediate_size  ,						decoder_ffn_dim=self.intermediate_size  ,						dropout=self.hidden_dropout_prob  ,						attention_dropout=self.attention_probs_dropout_prob  ,						encoder_layerdrop=self.encoder_layerdrop  ,						decoder_layerdrop=self.decoder_layerdrop  ,						max_position_embeddings=self.max_position_embeddings  ,						eos_token_id=self.eos_token_id  ,						bos_token_id=self.bos_token_id  ,						pad_token_id=self.pad_token_id  ,						)


 def 		a__ (				self		):
        __a				, __a     				=  self.prepare_config_and_inputs()
        return config, inputs_dict


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  MaMaaaModel(config=lowerCamelCase		).get_decoder().to(lowerCamelCase		).eval()
        __a     				=  inputs_dict["input_ids"]
        __a     				=  inputs_dict["attention_mask"]
        __a     				=  inputs_dict["head_mask"]

        # first forward pass
        __a     				=  model(lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						head_mask=lowerCamelCase  ,						use_cache=lowerCamelCase		)

        __a				, __a     				=  outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        __a     				=  ids_tensor((self.batch_size, 3)  ,						config.vocab_size		)
        __a     				=  ids_tensor((self.batch_size, 3)  ,						2		)

        # append to next input_ids and
        __a     				=  torch.cat([input_ids, next_tokens]  ,						dim=-1		)
        __a     				=  torch.cat([attention_mask, next_attn_mask]  ,						dim=-1		)

        __a     				=  model(lowerCamelCase  ,						attention_mask=lowerCamelCase		)["last_hidden_state"]
        __a     				=  model(lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						past_key_values=lowerCamelCase		)[
            "last_hidden_state"
        ]

        # select random slice
        __a     				=  ids_tensor((1,)  ,						output_from_past.shape[-1]		).item()
        __a     				=  output_from_no_past[:, -3:, random_slice_idx].detach()
        __a     				=  output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=1E-2		)		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  MaMaaaModel(config=lowerCamelCase		).to(lowerCamelCase		).eval()
        __a     				=  model(**lowerCamelCase		)

        __a     				=  outputs.encoder_last_hidden_state
        __a     				=  outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
               __a     				=  model.get_encoder()
               encoder.save_pretrained(lowerCamelCase		)
               __a     				=  MaMaaaEncoder.from_pretrained(lowerCamelCase		).to(lowerCamelCase		)

        __a     				=  encoder(inputs_dict["input_ids"]  ,						attention_mask=inputs_dict["attention_mask"]		)[
            0
        ]

        self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

        with tempfile.TemporaryDirectory() as tmpdirname:
               __a     				=  model.get_decoder()
               decoder.save_pretrained(lowerCamelCase		)
               __a     				=  MaMaaaDecoder.from_pretrained(lowerCamelCase		).to(lowerCamelCase		)

        __a     				=  decoder(
            input_ids=inputs_dict["decoder_input_ids"]  ,						attention_mask=inputs_dict["decoder_attention_mask"]  ,						encoder_hidden_states=lowerCamelCase  ,						encoder_attention_mask=inputs_dict["attention_mask"]  ,						)[0]

        self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[Any]           =		(
     (
         MaMaaaModel,
         MaMaaaForConditionalGeneration,
     )
     if is_torch_available()
     else ()
 )
 _snake_case   :			List[str]           =		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
 _snake_case   :			Any           =		(
     {
         """conversational""": MaMaaaForConditionalGeneration,
         """feature-extraction""": MaMaaaModel,
         """summarization""": MaMaaaForConditionalGeneration,
         """text2text-generation""": MaMaaaForConditionalGeneration,
         """translation""": MaMaaaForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 _snake_case   :			Optional[int]           =		True
 _snake_case   :			List[Any]           =		True
 _snake_case   :			List[str]           =		False
 _snake_case   :			Dict           =		False


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        if pipeline_test_casse_name == "TranslationPipelineTests":
               # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
               # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
               return True

        return False


 def 		a__ (				self		):
        __a     				=  MaMaaaModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase		)


 def 		a__ (				self		):
        self.config_tester.run_common_tests()


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               __a     				=  model_class(lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(lowerCamelCase		)
                      __a				, __a     				=  model_class.from_pretrained(lowerCamelCase  ,						output_loading_info=lowerCamelCase		)
               self.assertEqual(info["missing_keys"]  ,						[]		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
               __a     				=  model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()

               __a     				=  copy.deepcopy(self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)		)

               if not self.is_encoder_decoder:
                      __a     				=  inputs["input_ids"]
                      del inputs["input_ids"]
               else:
                      __a     				=  inputs["input_ids"]
                      __a     				=  inputs.get("decoder_input_ids"  ,						lowerCamelCase		)
                      del inputs["input_ids"]
                      inputs.pop("decoder_input_ids"  ,						lowerCamelCase		)

               __a     				=  model.get_input_embeddings()
               if not self.is_encoder_decoder:
                      __a     				=  wte(lowerCamelCase		)
               else:
                      __a     				=  wte(lowerCamelCase		)
                      __a     				=  wte(lowerCamelCase		)

               with torch.no_grad():
                      model(**lowerCamelCase		)[0]






 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs()
        __a     				=  input_dict["input_ids"]
        __a     				=  input_ids.ne(1		).to(lowerCamelCase		)
        __a     				=  MaMaaaForConditionalGeneration(lowerCamelCase		).eval().to(lowerCamelCase		)
        if torch_device == "cuda":
               model.half()
        model.generate(lowerCamelCase  ,						attention_mask=lowerCamelCase		)
        model.generate(num_beams=4  ,						do_sample=lowerCamelCase  ,						early_stopping=lowerCamelCase  ,						num_return_sequences=3		)



def 					_lowerCamelCase(					a							):
       return torch.tensor(a ,  dtype=torch.long ,  device=a							)


SCREAMING_SNAKE_CASE__:List[Any]			=	1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 				snake_case__     (   unittest.TestCase   ):


 @cached_property
 def 		a__ (				self		):
        return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"		)


 def 		a__ (				self		):
        __a     				=  MaMaaaModel.from_pretrained("facebook/m2m100_418M"		).to(lowerCamelCase		)
        __a     				=  _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
        __a     				=  _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
        __a     				=  prepare_mam_aaa_inputs_dict(model.config  ,						lowerCamelCase  ,						lowerCamelCase		)
        with torch.no_grad():
               __a     				=  model(**lowerCamelCase		)[0]
        __a     				=  torch.Size((1, 11, 1024)		)
        self.assertEqual(output.shape  ,						lowerCamelCase		)
        # change to expected output here
        __a     				=  torch.tensor(
            [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]  ,						device=lowerCamelCase		)
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,						lowerCamelCase  ,						atol=lowerCamelCase		)		)


 def 		a__ (				self		):
        __a     				=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(lowerCamelCase		)

        # change to intended input
        __a     				=  _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
        __a     				=  _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
        __a     				=  prepare_mam_aaa_inputs_dict(model.config  ,						lowerCamelCase  ,						lowerCamelCase		)
        with torch.no_grad():
               __a     				=  model(**lowerCamelCase		)[0]
        __a     				=  torch.Size((1, 11, model.config.vocab_size)		)
        self.assertEqual(output.shape  ,						lowerCamelCase		)
        # change to expected output here
        __a     				=  torch.tensor(
            [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]  ,						device=lowerCamelCase		)
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,						lowerCamelCase  ,						atol=lowerCamelCase		)		)






 def 		a__ (				self		):
        __a     				=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(lowerCamelCase		)
        __a     				=  MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  ,						src_lang="fr"  ,						tgt_lang="en"		)

        __a     				=  [
            "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
            "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
            "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
            " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
            " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
        ]

        # The below article tests that we don't add any hypotheses outside of the top n_beams
        __a     				=  tokenizer(lowerCamelCase  ,						padding=lowerCamelCase  ,						return_tensors="pt"		)

        __a     				=  model.generate(
            input_ids=dct["input_ids"].to(lowerCamelCase		)  ,						attention_mask=dct["attention_mask"].to(lowerCamelCase		)  ,						num_beams=5  ,						forced_bos_token_id=tokenizer.get_lang_id("en"		)  ,						)

        __a     				=  [
            "The NSA case highlights the total absence of intelligence debate",
            "I think there are two levels of response from the French government.",
            "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
            " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
            " communications in France.",
        ]

        __a     				=  tokenizer.batch_decode(
            hypotheses_batch.tolist()  ,						clean_up_tokenization_spaces=lowerCamelCase  ,						skip_special_tokens=lowerCamelCase		)
        assert generated == expected_en

"""simple docstring"""



import math



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  2
       __a     				=  int(math.sqrt(a							)							)  # Size of every segment
       __a     				=  [True] * (end + 1)
       __a     				=  []

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

       __a     				=  end + 1
       __a     				=  min(2 * end ,  a							)

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

                     for j in range(a ,  high + 1 ,  a							):
                            __a     				=  False

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

              __a     				=  high + 1
              __a     				=  min(high + end ,  a							)

       return prime




print(sieve(10**6))
