






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

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


snake_case			 =						logging.get_logger(__name__)

snake_case			 =						{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		'''blenderbot'''
     UpperCamelCase_							:						Dict								=		['''past_key_values''']
     UpperCamelCase_							:						Optional[Any]								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


     def __init__( self				:		List[str]    ,       UpperCAmelCase_				:		Optional[int]=8008    ,       UpperCAmelCase_				:		List[str]=128    ,       UpperCAmelCase_				:		List[Any]=2    ,       UpperCAmelCase_				:		Optional[int]=1_0240    ,       UpperCAmelCase_				:		int=32    ,       UpperCAmelCase_				:		List[Any]=24    ,       UpperCAmelCase_				:		Any=1_0240    ,       UpperCAmelCase_				:		Optional[int]=32    ,       UpperCAmelCase_				:		Dict=0.0    ,       UpperCAmelCase_				:		int=0.0    ,       UpperCAmelCase_				:		Optional[int]=True    ,       UpperCAmelCase_				:		Dict=True    ,       UpperCAmelCase_				:		Tuple="gelu"    ,       UpperCAmelCase_				:		List[Any]=2560    ,       UpperCAmelCase_				:		Tuple=0.1    ,       UpperCAmelCase_				:		List[Any]=0.0    ,       UpperCAmelCase_				:		Dict=0.0    ,       UpperCAmelCase_				:		str=0.02    ,       UpperCAmelCase_				:		int=1    ,       UpperCAmelCase_				:		Any=False    ,       UpperCAmelCase_				:		Tuple=0    ,       UpperCAmelCase_				:		Any=1    ,       UpperCAmelCase_				:		Any=2    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		str=2    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					str		  =			max_position_embeddings
       SCREAMING_SNAKE_CASE				:					Any		  =			d_model
       SCREAMING_SNAKE_CASE				:					Dict		  =			encoder_ffn_dim
       SCREAMING_SNAKE_CASE				:					Tuple		  =			encoder_layers
       SCREAMING_SNAKE_CASE				:					str		  =			encoder_attention_heads
       SCREAMING_SNAKE_CASE				:					int		  =			decoder_ffn_dim
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			decoder_layers
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			decoder_attention_heads
       SCREAMING_SNAKE_CASE				:					Dict		  =			dropout
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			attention_dropout
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			activation_dropout
       SCREAMING_SNAKE_CASE				:					str		  =			activation_function
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			init_std
       SCREAMING_SNAKE_CASE				:					int		  =			encoder_layerdrop
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			decoder_layerdrop
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			use_cache
       SCREAMING_SNAKE_CASE				:					Any		  =			encoder_layers
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=UpperCAmelCase_    ,       bos_token_id=UpperCAmelCase_    ,       eos_token_id=UpperCAmelCase_    ,       is_encoder_decoder=UpperCAmelCase_    ,       decoder_start_token_id=UpperCAmelCase_    ,       encoder_no_repeat_ngram_size=UpperCAmelCase_    ,       forced_eos_token_id=UpperCAmelCase_    ,       **UpperCAmelCase_    ,       )





class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     @property
     def  _A				( self				:		Optional[int]					):
       if self.task in ["default", "seq2seq-lm"]:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]					)
         if self.use_past:
           SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{0: "batch"}
           SCREAMING_SNAKE_CASE				:					Any		  =			{0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
           SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{0: "batch", 1: "decoder_sequence"}
           SCREAMING_SNAKE_CASE				:					int		  =			{0: "batch", 1: "decoder_sequence"}
         if self.use_past:
           self.fill_with_past_key_values_(UpperCAmelCase_    ,       direction="inputs"					)
       elif self.task == "causal-lm":
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]					)
         if self.use_past:
           SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			self.num_layers
           for i in range(UpperCAmelCase_					):
             SCREAMING_SNAKE_CASE				:					Tuple		  =			{0: "batch", 2: "past_sequence + sequence"}
             SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{0: "batch", 2: "past_sequence + sequence"}
       else:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def  _A				( self				:		Optional[Any]					):
       if self.task in ["default", "seq2seq-lm"]:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			super().outputs
       else:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			super(UpperCAmelCase_    ,       self					).outputs
         if self.use_past:
           SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.num_layers
           for i in range(UpperCAmelCase_					):
             SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{0: "batch", 2: "past_sequence + sequence"}
             SCREAMING_SNAKE_CASE				:					Dict		  =			{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs


     def  _A				( self				:		List[str]    ,       UpperCAmelCase_				:		PreTrainedTokenizer    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[TensorType] = None    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       # Generate decoder inputs
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			seq_length if not self.use_past else 1
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       SCREAMING_SNAKE_CASE				:					str		  =			dict(**UpperCAmelCase_    ,       **UpperCAmelCase_					)

       if self.use_past:
         if not is_torch_available():
           raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
         else:
           import torch
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Any		  =			common_inputs["input_ids"].shape
         SCREAMING_SNAKE_CASE				:					Any		  =			common_inputs["decoder_input_ids"].shape[1]
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.num_attention_heads
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			decoder_seq_length
         SCREAMING_SNAKE_CASE				:					Any		  =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase_    ,       UpperCAmelCase_					)]    ,       dim=1					)
         SCREAMING_SNAKE_CASE				:					Tuple		  =			[]
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			self.num_layers

         for _ in range(UpperCAmelCase_					):
           common_inputs["past_key_values"].append(
               (
                   torch.zeros(UpperCAmelCase_					),
                   torch.zeros(UpperCAmelCase_					),
                   torch.zeros(UpperCAmelCase_					),
                   torch.zeros(UpperCAmelCase_					),
               )					)
       return common_inputs


     def  _A				( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		PreTrainedTokenizer    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[TensorType] = None    ,       ):
       SCREAMING_SNAKE_CASE				:					str		  =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)

       if self.use_past:
         if not is_torch_available():
           raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
         else:
           import torch
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			common_inputs["input_ids"].shape
         SCREAMING_SNAKE_CASE				:					List[str]		  =			seqlen
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			self.num_layers
         SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.num_attention_heads
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			common_inputs["attention_mask"].dtype
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			torch.cat(
             [common_inputs["attention_mask"], torch.ones(UpperCAmelCase_    ,       UpperCAmelCase_    ,       dtype=UpperCAmelCase_					)]    ,       dim=1					)
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[
             (torch.zeros(UpperCAmelCase_					), torch.zeros(UpperCAmelCase_					)) for _ in range(UpperCAmelCase_					)
         ]
       return common_inputs


     def  _A				( self				:		str    ,       UpperCAmelCase_				:		PreTrainedTokenizer    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[TensorType] = None    ,       ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			compute_effective_axis_dimension(
           UpperCAmelCase_    ,       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
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.num_special_tokens_to_add(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			compute_effective_axis_dimension(
           UpperCAmelCase_    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=UpperCAmelCase_					)

       # Generate dummy inputs according to compute batch and sequence
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
       SCREAMING_SNAKE_CASE				:					List[str]		  =			dict(tokenizer(UpperCAmelCase_    ,       return_tensors=UpperCAmelCase_					)					)
       return common_inputs


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		PreTrainedTokenizer    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		int = -1    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[TensorType] = None    ,       ):
       if self.task in ["default", "seq2seq-lm"]:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             UpperCAmelCase_    ,       batch_size=UpperCAmelCase_    ,       seq_length=UpperCAmelCase_    ,       is_pair=UpperCAmelCase_    ,       framework=UpperCAmelCase_					)

       elif self.task == "causal-lm":
         SCREAMING_SNAKE_CASE				:					List[str]		  =			self._generate_dummy_inputs_for_causal_lm(
             UpperCAmelCase_    ,       batch_size=UpperCAmelCase_    ,       seq_length=UpperCAmelCase_    ,       is_pair=UpperCAmelCase_    ,       framework=UpperCAmelCase_					)
       else:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             UpperCAmelCase_    ,       batch_size=UpperCAmelCase_    ,       seq_length=UpperCAmelCase_    ,       is_pair=UpperCAmelCase_    ,       framework=UpperCAmelCase_					)

       return common_inputs


     def  _A				( self				:		Any    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Tuple    ,       UpperCAmelCase_				:		Dict    ,       UpperCAmelCase_				:		Optional[Any]					):
       if self.task in ["default", "seq2seq-lm"]:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			super()._flatten_past_key_values_(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       else:
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			super(UpperCAmelCase_    ,       self					)._flatten_past_key_values_(
             UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)






     def  _A				( self				:		Any    ,       UpperCAmelCase_				:		Mapping[str, Mapping[int, str]]    ,       UpperCAmelCase_				:		str					):
       if direction not in ["inputs", "outputs"]:
         raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			"past_key_values" if direction == "inputs" else "present"
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.num_layers

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			"past_encoder_sequence"
       SCREAMING_SNAKE_CASE				:					int		  =			"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

       for i in range(UpperCAmelCase_					):
         SCREAMING_SNAKE_CASE				:					str		  =			{0: "batch", 2: decoder_sequence}
         SCREAMING_SNAKE_CASE				:					int		  =			{0: "batch", 2: decoder_sequence}
         SCREAMING_SNAKE_CASE				:					List[str]		  =			{0: "batch", 2: encoder_sequence}
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{0: "batch", 2: encoder_sequence}














import math
from collections.abc import Generator


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    # only need to check for factors up to sqrt(i)
    SCREAMING_SNAKE_CASE				:					str		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1)  ,   2 ))
  # It's useless to test even numbers as they will not be prime
  if max_n > 2:
    yield 2  # Because 2 will not be tested, it's necessary to yield it now
  for i in (n for n in numbers if n > 1):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(3  ,   lowercase  ,   2 ):
      # As we removed the even numbers, we don't need them now
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   ):

  """simple docstring"""






  from timeit import timeit

  SCREAMING_SNAKE_CASE				:					Tuple		  =			"from __main__ import slow_primes, primes, fast_primes"
  print(timeit("slow_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("fast_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )


if __name__ == "__main__":
   snake_case			 =						int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()






