



'''simple docstring'''






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


A_				:					List[str]							=      logging.get_logger(__name__)

A_				:					Dict							=      {
    """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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """blenderbot"""
 UpperCAmelCase		      =    ["""past_key_values"""]
 UpperCAmelCase		      =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







 def __init__(				self    ,a_=8_008    ,a_=128    ,a_=2    ,a_=10_240    ,a_=32    ,a_=24    ,a_=10_240    ,a_=32    ,a_=0.0    ,a_=0.0    ,a_=True    ,a_=True    ,a_="gelu"    ,a_=2_560    ,a_=0.1    ,a_=0.0    ,a_=0.0    ,a_=0.02    ,a_=1    ,a_=False    ,a_=0    ,a_=1    ,a_=2    ,a_=3    ,a_=2    ,**a_    ,)						->  Optional[Any]:
        _UpperCAmelCase    :		List[Any]     	=					vocab_size
        _UpperCAmelCase    :		Dict     	=					max_position_embeddings
        _UpperCAmelCase    :		int     	=					d_model
        _UpperCAmelCase    :		Any     	=					encoder_ffn_dim
        _UpperCAmelCase    :		Tuple     	=					encoder_layers
        _UpperCAmelCase    :		Optional[Any]     	=					encoder_attention_heads
        _UpperCAmelCase    :		int     	=					decoder_ffn_dim
        _UpperCAmelCase    :		List[Any]     	=					decoder_layers
        _UpperCAmelCase    :		str     	=					decoder_attention_heads
        _UpperCAmelCase    :		str     	=					dropout
        _UpperCAmelCase    :		int     	=					attention_dropout
        _UpperCAmelCase    :		Any     	=					activation_dropout
        _UpperCAmelCase    :		Any     	=					activation_function
        _UpperCAmelCase    :		Dict     	=					init_std
        _UpperCAmelCase    :		Dict     	=					encoder_layerdrop
        _UpperCAmelCase    :		str     	=					decoder_layerdrop
        _UpperCAmelCase    :		List[str]     	=					use_cache
        _UpperCAmelCase    :		Tuple     	=					encoder_layers
        _UpperCAmelCase    :		Any     	=					scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		Any     	=					OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]				)
               if self.use_past:
                      _UpperCAmelCase    :		List[Any]     	=					{0: """batch"""}
                      _UpperCAmelCase    :		Dict     	=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
               else:
                      _UpperCAmelCase    :		Tuple     	=					{0: """batch""", 1: """decoder_sequence"""}
                      _UpperCAmelCase    :		Any     	=					{0: """batch""", 1: """decoder_sequence"""}
               if self.use_past:
                      self.fill_with_past_key_values_(a_    ,direction="""inputs"""				)
        elif self.task == "causal-lm":
               _UpperCAmelCase    :		str     	=					OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]				)
               if self.use_past:
                      _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					self.num_layers
                      for i in range(a_				):
                             _UpperCAmelCase    :		Tuple     	=					{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: """past_sequence + sequence"""}
        else:
               _UpperCAmelCase    :		str     	=					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 _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		str     	=					super().outputs
        else:
               _UpperCAmelCase    :		List[str]     	=					super(a_    ,self				).outputs
               if self.use_past:
                      _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					self.num_layers
                      for i in range(a_				):
                             _UpperCAmelCase    :		Any     	=					{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase    :		List[Any]     	=					{0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		List[Any]     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)
        # Generate decoder inputs
        _UpperCAmelCase    :		Dict     	=					seq_length if not self.use_past else 1
        _UpperCAmelCase    :		Dict     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)
        _UpperCAmelCase    :		Tuple     	=					{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        _UpperCAmelCase    :		Tuple     	=					dict(**a_    ,**a_				)

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

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







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)

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







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        # 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
        _UpperCAmelCase    :		List[str]     	=					compute_effective_axis_dimension(
            a_    ,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
        _UpperCAmelCase    :		List[str]     	=					tokenizer.num_special_tokens_to_add(a_				)
        _UpperCAmelCase    :		str     	=					compute_effective_axis_dimension(
            a_    ,fixed_dimension=OnnxConfig.default_fixed_sequence    ,num_token_to_add=a_				)

        # Generate dummy inputs according to compute batch and sequence
        _UpperCAmelCase    :		Dict     	=					[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
        _UpperCAmelCase    :		Optional[Any]     	=					dict(tokenizer(a_    ,return_tensors=a_				)				)
        return common_inputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		int     	=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_				)

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

        return common_inputs







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_				)						->  str:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		Optional[Any]     	=					super()._flatten_past_key_values_(a_    ,a_    ,a_    ,a_				)
        else:
               _UpperCAmelCase    :		Tuple     	=					super(a_    ,self				)._flatten_past_key_values_(
                   a_    ,a_    ,a_    ,a_				)







 def _snake_case					(				self    ,a_    ,a_				)						->  Any:
        if direction not in ["inputs", "outputs"]:
               raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''				)

        _UpperCAmelCase    :		Union[str, Any]     	=					"""past_key_values""" if direction == """inputs""" else """present"""
        _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					self.num_layers

        _UpperCAmelCase    :		int     	=					"""past_encoder_sequence"""
        _UpperCAmelCase    :		str     	=					"""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

        for i in range(a_				):
               _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: decoder_sequence}
               _UpperCAmelCase    :		Dict     	=					{0: """batch""", 2: decoder_sequence}
               _UpperCAmelCase    :		Dict     	=					{0: """batch""", 2: encoder_sequence}
               _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: encoder_sequence}








from itertools import compress, repeat
from math import ceil, sqrt
def 						snake_case__							(       __lowercase							)		->		list[int]:







						"""simple docstring"""

						if num <= 2:
												return []
						if num == 3:
												return [2]

						# Odd sieve for numbers in range [3, num - 1]
						A__						:				Dict         =			bytearray(b"\x01"							) * ((num >> 1) - 1)

						for i in range(3				,  int(sqrt(__lowercase							)							) + 1				,  2							):
												if sieve[(i >> 1) - 1]:
																		A__						:				int         =			i**2
																		A__						:				List[str]         =			repeat(
																		    0				,  ceil((num - i_squared) / (i << 1)							)							)

						return [2] + list(compress(range(3				,  __lowercase				,  2							)				,  __lowercase							)							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()