"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class 				snake_case__     :
 _snake_case   :			Dict           =		BlenderbotSmallConfig
 _snake_case   :			Dict           =		{}
 _snake_case   :			Any           =		"""gelu"""


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=False  ,						lowerCamelCase=99  ,						lowerCamelCase=32  ,						lowerCamelCase=2  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						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_dropout_prob
        __a     				=  attention_probs_dropout_prob
        __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 - 1]  ,						self.vocab_size		)
        __a     				=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)  ,						1		)
        __a     				=  tf.concat([input_ids, eos_tensor]  ,						axis=1		)

        __a     				=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)

        __a     				=  self.config_cls(
            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  ,						max_position_embeddings=self.max_position_embeddings  ,						eos_token_ids=[2]  ,						bos_token_id=self.bos_token_id  ,						pad_token_id=self.pad_token_id  ,						decoder_start_token_id=self.pad_token_id  ,						**self.config_updates  ,						)
        __a     				=  prepare_blenderbot_small_inputs_dict(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
        return config, inputs_dict






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  TFBlenderbotSmallModel(config=lowerCamelCase		).get_decoder()
        __a     				=  inputs_dict["input_ids"]

        __a     				=  input_ids[:1, :]
        __a     				=  inputs_dict["attention_mask"][:1, :]
        __a     				=  inputs_dict["head_mask"]
        __a     				=  1

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

        __a				, __a     				=  outputs.to_tuple()

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

        # append to next input_ids and
        __a     				=  tf.concat([input_ids, next_tokens]  ,						axis=-1		)
        __a     				=  tf.concat([attention_mask, next_attn_mask]  ,						axis=-1		)

        __a     				=  model(lowerCamelCase  ,						attention_mask=lowerCamelCase		)[0]
        __a     				=  model(lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						past_key_values=lowerCamelCase		)[0]

        self.parent.assertEqual(next_tokens.shape[1]  ,						output_from_past.shape[1]		)

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

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCamelCase  ,						lowerCamelCase  ,						rtol=1E-3		)



def 					_lowerCamelCase(					a ,  a ,  a ,  a=None ,  a=None ,  a=None ,  a=None ,  a=None ,  ):
       if attention_mask is None:
              __a     				=  tf.cast(tf.math.not_equal(a ,  config.pad_token_id							) ,  tf.inta							)
       if decoder_attention_mask is None:
              __a     				=  tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape ,  dtype=tf.inta							),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,  config.pad_token_id							) ,  tf.inta							),
                  ] ,  axis=-1 ,  )
       if head_mask is None:
              __a     				=  tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
       if decoder_head_mask is None:
              __a     				=  tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
       if cross_attn_head_mask is None:
              __a     				=  tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }






@require_tf
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[Any]           =		(
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 _snake_case   :			str           =		(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 _snake_case   :			List[str]           =		(
     {
         """conversational""": TFBlenderbotSmallForConditionalGeneration,
         """feature-extraction""": TFBlenderbotSmallModel,
         """summarization""": TFBlenderbotSmallForConditionalGeneration,
         """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
         """translation""": TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _snake_case   :			List[Any]           =		True
 _snake_case   :			List[Any]           =		False
 _snake_case   :			Dict           =		False


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


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






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






@require_tokenizers
@require_tf
class 				snake_case__     (   unittest.TestCase   ):
 _snake_case   :			Tuple           =		[
     """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
     """  i'm going to throw up.\nand why is that?"""
 ]
 _snake_case   :			List[Any]           =		"""facebook/blenderbot_small-90M"""


 @cached_property
 def 		a__ (				self		):
        # use "old" tokenizer here because of bug when downloading new tokenizer
        return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)


 @cached_property
 def 		a__ (				self		):
        __a     				=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
        return model






 @slow
 def 		a__ (				self		):
        __a     				=  self.tokenizer(self.src_text  ,						return_tensors="tf"		)
        __a     				=  self.model.generate(
            model_inputs.input_ids  ,						attention_mask=model_inputs.attention_mask  ,						num_beams=2  ,						use_cache=lowerCamelCase  ,						)
        __a     				=  self.tokenizer.batch_decode(generated_ids.numpy()  ,						skip_special_tokens=lowerCamelCase		)[0]
        assert generated_words in (
            "i don't know. i just feel like i'm going to throw up. it's not fun.",
            "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
            "i'm not sure. i just feel like i've been in a bad situation.",
        )

"""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))
