
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       =					logging.get_logger(__name__)

_A       =					{
    "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      _lowerCAmelCase (     __a	):
				_lowercase      		='''blenderbot'''
				_lowercase      		=['''past_key_values''']
				_lowercase      		={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__( self    ,    _UpperCamelCase=8_008    ,    _UpperCamelCase=128    ,    _UpperCamelCase=2    ,    _UpperCamelCase=10_240    ,    _UpperCamelCase=32    ,    _UpperCamelCase=24    ,    _UpperCamelCase=10_240    ,    _UpperCamelCase=32    ,    _UpperCamelCase=0.0    ,    _UpperCamelCase=0.0    ,    _UpperCamelCase=True    ,    _UpperCamelCase=True    ,    _UpperCamelCase="gelu"    ,    _UpperCamelCase=2_560    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=0.0    ,    _UpperCamelCase=0.0    ,    _UpperCamelCase=0.02    ,    _UpperCamelCase=1    ,    _UpperCamelCase=False    ,    _UpperCamelCase=0    ,    _UpperCamelCase=1    ,    _UpperCamelCase=2    ,    _UpperCamelCase=3    ,    _UpperCamelCase=2    ,    **_UpperCamelCase    ,    )    ->				Any:
							lowerCAmelCase_      =     vocab_size
							lowerCAmelCase_      =     max_position_embeddings
							lowerCAmelCase_      =     d_model
							lowerCAmelCase_      =     encoder_ffn_dim
							lowerCAmelCase_      =     encoder_layers
							lowerCAmelCase_      =     encoder_attention_heads
							lowerCAmelCase_      =     decoder_ffn_dim
							lowerCAmelCase_      =     decoder_layers
							lowerCAmelCase_      =     decoder_attention_heads
							lowerCAmelCase_      =     dropout
							lowerCAmelCase_      =     attention_dropout
							lowerCAmelCase_      =     activation_dropout
							lowerCAmelCase_      =     activation_function
							lowerCAmelCase_      =     init_std
							lowerCAmelCase_      =     encoder_layerdrop
							lowerCAmelCase_      =     decoder_layerdrop
							lowerCAmelCase_      =     use_cache
							lowerCAmelCase_      =     encoder_layers
							lowerCAmelCase_      =     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      _lowerCAmelCase (     __a	):




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




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = False    ,    _UpperCamelCase = None    ,    )    ->				Mapping[str, Any]:
							lowerCAmelCase_      =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							# Generate decoder inputs
							lowerCAmelCase_      =     seq_length if not self.use_past else 1
							lowerCAmelCase_      =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							lowerCAmelCase_      =     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
										lowerCAmelCase_     ,  lowerCAmelCase_      =     common_inputs["input_ids"].shape
										lowerCAmelCase_      =     common_inputs["decoder_input_ids"].shape[1]
										lowerCAmelCase_     ,  lowerCAmelCase_      =     self.num_attention_heads
										lowerCAmelCase_      =     (
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										lowerCAmelCase_      =     decoder_seq_length
										lowerCAmelCase_      =     (
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)
										lowerCAmelCase_      =     torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(_UpperCamelCase    ,    _UpperCamelCase					)]    ,    dim=1					)
										lowerCAmelCase_      =     []
										lowerCAmelCase_     ,  lowerCAmelCase_      =     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    ,    _UpperCamelCase    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = False    ,    _UpperCamelCase = None    ,    )    ->				Mapping[str, Any]:
							lowerCAmelCase_      =     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
										lowerCAmelCase_     ,  lowerCAmelCase_      =     common_inputs["input_ids"].shape
										lowerCAmelCase_      =     seqlen
										lowerCAmelCase_     ,  lowerCAmelCase_      =     self.num_layers
										lowerCAmelCase_     ,  lowerCAmelCase_      =     self.num_attention_heads
										lowerCAmelCase_      =     (
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										lowerCAmelCase_      =     common_inputs["attention_mask"].dtype
										lowerCAmelCase_      =     torch.cat(
										    [common_inputs["attention_mask"], torch.ones(_UpperCamelCase    ,    _UpperCamelCase    ,    dtype=_UpperCamelCase					)]    ,    dim=1					)
										lowerCAmelCase_      =     [
										    (torch.zeros(_UpperCamelCase					), torch.zeros(_UpperCamelCase					)) for _ in range(_UpperCamelCase					)
										]
							return common_inputs




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = False    ,    _UpperCamelCase = 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
							lowerCAmelCase_      =     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
							lowerCAmelCase_      =     tokenizer.num_special_tokens_to_add(_UpperCamelCase					)
							lowerCAmelCase_      =     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
							lowerCAmelCase_      =     [" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
							lowerCAmelCase_      =     dict(tokenizer(_UpperCamelCase    ,    return_tensors=_UpperCamelCase					)					)
							return common_inputs




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = -1    ,    _UpperCamelCase = False    ,    _UpperCamelCase = None    ,    )    ->				Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase_      =     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":
										lowerCAmelCase_      =     self._generate_dummy_inputs_for_causal_lm(
										    _UpperCamelCase    ,    batch_size=_UpperCamelCase    ,    seq_length=_UpperCamelCase    ,    is_pair=_UpperCamelCase    ,    framework=_UpperCamelCase					)
							else:
										lowerCAmelCase_      =     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    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase_      =     super()._flatten_past_key_values_(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							else:
										lowerCAmelCase_      =     super(_UpperCamelCase    ,    self					)._flatten_past_key_values_(
										    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)




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

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

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

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





import math






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5   ,							int(math.sqrt(__lowerCAmelCase    ) + 1    )   ,							6    ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True






def        lowerCamelCase__    (    __lowerCAmelCase  :  int = 600851475143    ):




			"""simple docstring"""


			try:
						lowerCAmelCase_      =     int(__lowerCAmelCase    )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."    )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."    )
			lowerCAmelCase_      =     0
			if is_prime(__lowerCAmelCase    ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(__lowerCAmelCase    ):
						return n
			for i in range(3   ,							int(math.sqrt(__lowerCAmelCase    )    ) + 1   ,							2    ):
						if n % i == 0:
									if is_prime(n // i    ):
												lowerCAmelCase_      =     n // i
												break
									elif is_prime(__lowerCAmelCase    ):
												lowerCAmelCase_      =     i
			return max_number


if __name__ == "__main__":
			print(f"""{solution() = }""")



