





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__A							:			Dict         =			logging.get_logger(__name__)

__A							:			List[str]         =			{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}







class      lowerCamelCase(						lowerCAmelCase__	):
     '''simple docstring'''


     __magic_name__   =			'bart'
     __magic_name__   =			['past_key_values']
     __magic_name__   =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




     def __init__(    self				,     snake_case_=5_0265				,     snake_case_=1024				,     snake_case_=12				,     snake_case_=4096				,     snake_case_=16				,     snake_case_=12				,     snake_case_=4096				,     snake_case_=16				,     snake_case_=0.0				,     snake_case_=0.0				,     snake_case_="gelu"				,     snake_case_=1024				,     snake_case_=0.1				,     snake_case_=0.0				,     snake_case_=0.0				,     snake_case_=0.02				,     snake_case_=0.0				,     snake_case_=False				,     snake_case_=True				,     snake_case_=3				,     snake_case_=1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=True				,     snake_case_=2				,     snake_case_=2				,     **snake_case_				,     ):
         _A     =							vocab_size
         _A     =							max_position_embeddings
         _A     =							d_model
         _A     =							encoder_ffn_dim
         _A     =							encoder_layers
         _A     =							encoder_attention_heads
         _A     =							decoder_ffn_dim
         _A     =							decoder_layers
         _A     =							decoder_attention_heads
         _A     =							dropout
         _A     =							attention_dropout
         _A     =							activation_dropout
         _A     =							activation_function
         _A     =							init_std
         _A     =							encoder_layerdrop
         _A     =							decoder_layerdrop
         _A     =							classifier_dropout
         _A     =							use_cache
         _A     =							encoder_layers
         _A     =							scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=_lowerCamelCase				,     pad_token_id=_lowerCamelCase				,     bos_token_id=_lowerCamelCase				,     eos_token_id=_lowerCamelCase				,     is_encoder_decoder=_lowerCamelCase				,     decoder_start_token_id=_lowerCamelCase				,     forced_eos_token_id=_lowerCamelCase				,     **_lowerCamelCase				,     )

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'				,     _lowerCamelCase							):
             _A     =							self.bos_token_id
             warnings.warn(
                 F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                 'The config can simply be saved and uploaded again to be fixed.'							)







class      lowerCamelCase(						lowerCAmelCase__	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):

         if self.task in ["default", "seq2seq-lm"]:
             _A     =							OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]							)

             if self.use_past:
                 _A     =							{0: """batch"""}
                 _A     =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                 _A     =							{0: """batch""", 1: """decoder_sequence"""}
                 _A     =							{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                 self.fill_with_past_key_values_(_lowerCamelCase				,     direction='inputs'							)
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             _A     =							OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]							)
             if self.use_past:
                 _A     =							self.num_layers
                 for i in range(_lowerCamelCase							):
                     _A     =							{0: """batch""", 2: """past_sequence + sequence"""}
                     _A     =							{0: """batch""", 2: """past_sequence + sequence"""}
         else:
             _A     =							OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                     ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                 ]							)

         return common_inputs




     @property
     def        lowerCAmelCase__		(    self							):

         if self.task in ["default", "seq2seq-lm"]:
             _A     =							super().outputs
         else:
             _A     =							super(_lowerCamelCase				,     self							).outputs
             if self.use_past:
                 _A     =							self.num_layers
                 for i in range(_lowerCamelCase							):
                     _A     =							{0: """batch""", 2: """past_sequence + sequence"""}
                     _A     =							{0: """batch""", 2: """past_sequence + sequence"""}
         return common_outputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         _A     =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase							)

         # Generate decoder inputs
         _A     =							seq_length if not self.use_past else 1
         _A     =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase							)
         _A     =							{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         _A     =							dict(**_lowerCamelCase				,     **_lowerCamelCase							)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'							)
             else:
                 import torch
             _A     =							common_inputs["""input_ids"""].shape
             _A     =							common_inputs["""decoder_input_ids"""].shape[1]
             _A     =							self.num_attention_heads
             _A     =							(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             _A     =							decoder_seq_length + 3
             _A     =							(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             _A     =							torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(_lowerCamelCase				,     _lowerCamelCase							)]				,     dim=1							)

             _A     =							[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             _A     =							self.num_layers
             _A     =							min(_lowerCamelCase				,     _lowerCamelCase							)
             _A     =							max(_lowerCamelCase				,     _lowerCamelCase							) - min_num_layers
             _A     =							"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

             for _ in range(_lowerCamelCase							):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_lowerCamelCase							),
                         torch.zeros(_lowerCamelCase							),
                         torch.zeros(_lowerCamelCase							),
                         torch.zeros(_lowerCamelCase							),
                     )							)
             # TODO: test this.
             _A     =							encoder_shape if remaining_side_name == """encoder""" else decoder_shape
             for _ in range(_lowerCamelCase				,     _lowerCamelCase							):
                 common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase							), torch.zeros(_lowerCamelCase							))							)
         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         _A     =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase							)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'							)
             else:
                 import torch
             _A     =							common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             _A     =							seqlen + 2
             _A     =							self.num_layers
             _A     =							self.num_attention_heads
             _A     =							(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             _A     =							common_inputs["""attention_mask"""].dtype
             _A     =							torch.cat(
                 [common_inputs['attention_mask'], torch.ones(_lowerCamelCase				,     _lowerCamelCase				,     dtype=_lowerCamelCase							)]				,     dim=1							)
             _A     =							[
                 (torch.zeros(_lowerCamelCase							), torch.zeros(_lowerCamelCase							)) for _ in range(_lowerCamelCase							)
             ]
         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         _A     =							compute_effective_axis_dimension(
             _lowerCamelCase				,     fixed_dimension=OnnxConfig.default_fixed_batch				,     num_token_to_add=0							)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         _A     =							tokenizer.num_special_tokens_to_add(_lowerCamelCase							)
         _A     =							compute_effective_axis_dimension(
             _lowerCamelCase				,     fixed_dimension=OnnxConfig.default_fixed_sequence				,     num_token_to_add=_lowerCamelCase							)

         # Generate dummy inputs according to compute batch and sequence
         _A     =							[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
         _A     =							dict(tokenizer(_lowerCamelCase				,     return_tensors=_lowerCamelCase							)							)
         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):

         if self.task in ["default", "seq2seq-lm"]:
             _A     =							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase				,     batch_size=_lowerCamelCase				,     seq_length=_lowerCamelCase				,     is_pair=_lowerCamelCase				,     framework=_lowerCamelCase							)

         elif self.task == "causal-lm":
             _A     =							self._generate_dummy_inputs_for_causal_lm(
                 _lowerCamelCase				,     batch_size=_lowerCamelCase				,     seq_length=_lowerCamelCase				,     is_pair=_lowerCamelCase				,     framework=_lowerCamelCase							)
         else:
             _A     =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCamelCase				,     batch_size=_lowerCamelCase				,     seq_length=_lowerCamelCase				,     is_pair=_lowerCamelCase				,     framework=_lowerCamelCase							)

         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):

         if self.task in ["default", "seq2seq-lm"]:
             _A     =							super()._flatten_past_key_values_(_lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase							)
         else:
             _A     =							super(_lowerCamelCase				,     self							)._flatten_past_key_values_(
                 _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase							)








"""simple docstring"""
import math




def 					a__						(						lowerCAmelCase    )	-> list[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      UpperCAmelCase__			:							Union[str, Any]							=    2
      UpperCAmelCase__			:							List[Any]							=    int(math.sqrt(lowerCAmelCase    )    )  # Size of every segment
      UpperCAmelCase__			:							Dict							=    [True] * (end + 1)
      UpperCAmelCase__			:							str							=    []

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

      UpperCAmelCase__			:							Optional[Any]							=    end + 1
      UpperCAmelCase__			:							Dict							=    min(2 * end			,	lowerCAmelCase    )

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

                  for j in range(lowerCAmelCase			,	high + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							int							=    False

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

            UpperCAmelCase__			:							Dict							=    high + 1
            UpperCAmelCase__			:							int							=    min(high + end			,	lowerCAmelCase    )

      return prime





print(sieve(10**6))

