





'''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


lowercase_			       =				logging.get_logger(__name__)

lowercase_			       =				{
    """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       a_  (       snake_case_ ):

    '''simple docstring'''
    UpperCamelCase									=				'''blenderbot'''
    UpperCamelCase									=				['''past_key_values''']
    UpperCamelCase									=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


    def __init__(	self				,	A=8008				,	A=128				,	A=2				,	A=1_0240				,	A=32				,	A=24				,	A=1_0240				,	A=32				,	A=0.0				,	A=0.0				,	A=True				,	A=True				,	A="gelu"				,	A=2560				,	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				,	)	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    vocab_size
        _SCREAMING_SNAKE_CASE             =    max_position_embeddings
        _SCREAMING_SNAKE_CASE             =    d_model
        _SCREAMING_SNAKE_CASE             =    encoder_ffn_dim
        _SCREAMING_SNAKE_CASE             =    encoder_layers
        _SCREAMING_SNAKE_CASE             =    encoder_attention_heads
        _SCREAMING_SNAKE_CASE             =    decoder_ffn_dim
        _SCREAMING_SNAKE_CASE             =    decoder_layers
        _SCREAMING_SNAKE_CASE             =    decoder_attention_heads
        _SCREAMING_SNAKE_CASE             =    dropout
        _SCREAMING_SNAKE_CASE             =    attention_dropout
        _SCREAMING_SNAKE_CASE             =    activation_dropout
        _SCREAMING_SNAKE_CASE             =    activation_function
        _SCREAMING_SNAKE_CASE             =    init_std
        _SCREAMING_SNAKE_CASE             =    encoder_layerdrop
        _SCREAMING_SNAKE_CASE             =    decoder_layerdrop
        _SCREAMING_SNAKE_CASE             =    use_cache
        _SCREAMING_SNAKE_CASE             =    encoder_layers
        _SCREAMING_SNAKE_CASE             =    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       a_  (       snake_case_ ):

    '''simple docstring'''


    @property
    def     snake_case_(	self )	->     Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE             =    OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ] )
            if self.use_past:
                _SCREAMING_SNAKE_CASE             =    {0: """batch"""}
                _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
            else:
                _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
                _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
            if self.use_past:
                self.fill_with_past_key_values_(A				,	direction="""inputs""" )
        elif self.task == "causal-lm":
            _SCREAMING_SNAKE_CASE             =    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             =    self.num_layers
                for i in range(A ):
                    _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                    _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
        else:
            _SCREAMING_SNAKE_CASE             =    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"]:
            _SCREAMING_SNAKE_CASE             =    super().outputs
        else:
            _SCREAMING_SNAKE_CASE             =    super(A				,	self ).outputs
            if self.use_past:
                _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
                for i in range(A ):
                    _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                    _SCREAMING_SNAKE_CASE             =    {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]:
        _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A				,	A				,	A				,	A				,	A )
        # Generate decoder inputs
        _SCREAMING_SNAKE_CASE             =    seq_length if not self.use_past else 1
        _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A				,	A				,	A				,	A				,	A )
        _SCREAMING_SNAKE_CASE             =    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        _SCREAMING_SNAKE_CASE             =    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
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
            _SCREAMING_SNAKE_CASE             =    common_inputs["""decoder_input_ids"""].shape[1]
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
            _SCREAMING_SNAKE_CASE             =    (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE             =    decoder_seq_length
            _SCREAMING_SNAKE_CASE             =    (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE             =    torch.cat(
                [common_inputs["""decoder_attention_mask"""], torch.ones(A				,	A )]				,	dim=1 )
            _SCREAMING_SNAKE_CASE             =    []
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    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]:
        _SCREAMING_SNAKE_CASE             =    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
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
            _SCREAMING_SNAKE_CASE             =    seqlen
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
            _SCREAMING_SNAKE_CASE             =    (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE             =    common_inputs["""attention_mask"""].dtype
            _SCREAMING_SNAKE_CASE             =    torch.cat(
                [common_inputs["""attention_mask"""], torch.ones(A				,	A				,	dtype=A )]				,	dim=1 )
            _SCREAMING_SNAKE_CASE             =    [
                (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
        _SCREAMING_SNAKE_CASE             =    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
        _SCREAMING_SNAKE_CASE             =    tokenizer.num_special_tokens_to_add(A )
        _SCREAMING_SNAKE_CASE             =    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
        _SCREAMING_SNAKE_CASE             =    [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
        _SCREAMING_SNAKE_CASE             =    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"]:
            _SCREAMING_SNAKE_CASE             =    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":
            _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_causal_lm(
                A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
        else:
            _SCREAMING_SNAKE_CASE             =    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 )	->     Any:
        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE             =    super()._flatten_past_key_values_(A				,	A				,	A				,	A )
        else:
            _SCREAMING_SNAKE_CASE             =    super(A				,	self )._flatten_past_key_values_(
                A				,	A				,	A				,	A )





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

        _SCREAMING_SNAKE_CASE             =    """past_key_values""" if direction == """inputs""" else """present"""
        _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers

        _SCREAMING_SNAKE_CASE             =    """past_encoder_sequence"""
        _SCREAMING_SNAKE_CASE             =    """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

        for i in range(A ):
            _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: decoder_sequence}
            _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: decoder_sequence}
            _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: encoder_sequence}
            _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: encoder_sequence}










'''simple docstring'''

def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:

    if n <= 0:
        raise ValueError("""Please enter an integer greater than 0"""					)

    _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
    for number in range(2      , n + 1					):
        if phi[number] == number:
            phi[number] -= 1
            for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)

    _SCREAMING_SNAKE_CASE             =    1
    for number in range(1      , n + 1					):
        if (answer / phi[answer]) < (number / phi[number]):
            _SCREAMING_SNAKE_CASE             =    number

    return answer


if __name__ == "__main__":
      print(solution())



