



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    :		Optional[Any]  =logging.get_logger(__name__)

__snake_case    :		Optional[int]  ={
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""blenderbot-small"""
		snake_case_     =["""past_key_values"""]
		snake_case_     ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self		,__lowerCamelCase=5_02_65		,__lowerCamelCase=5_12		,__lowerCamelCase=8		,__lowerCamelCase=20_48		,__lowerCamelCase=16		,__lowerCamelCase=8		,__lowerCamelCase=20_48		,__lowerCamelCase=16		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase="gelu"		,__lowerCamelCase=5_12		,__lowerCamelCase=0.1		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1		,__lowerCamelCase=False		,__lowerCamelCase=0		,__lowerCamelCase=1		,__lowerCamelCase=2		,__lowerCamelCase=2		,**__lowerCamelCase		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					vocab_size
						lowerCAmelCase__					:    int												=					max_position_embeddings
						lowerCAmelCase__					:    Any												=					d_model
						lowerCAmelCase__					:    str												=					encoder_ffn_dim
						lowerCAmelCase__					:    List[Any]												=					encoder_layers
						lowerCAmelCase__					:    Dict												=					encoder_attention_heads
						lowerCAmelCase__					:    List[str]												=					decoder_ffn_dim
						lowerCAmelCase__					:    Optional[Any]												=					decoder_layers
						lowerCAmelCase__					:    str												=					decoder_attention_heads
						lowerCAmelCase__					:    List[str]												=					dropout
						lowerCAmelCase__					:    int												=					attention_dropout
						lowerCAmelCase__					:    Union[str, Any]												=					activation_dropout
						lowerCAmelCase__					:    Tuple												=					activation_function
						lowerCAmelCase__					:    Union[str, Any]												=					init_std
						lowerCAmelCase__					:    Dict												=					encoder_layerdrop
						lowerCAmelCase__					:    Dict												=					decoder_layerdrop
						lowerCAmelCase__					:    List[Any]												=					use_cache
						lowerCAmelCase__					:    Optional[Any]												=					encoder_layers
						lowerCAmelCase__					:    Optional[Any]												=					scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    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		,)



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		@property
		def 		lowerCAmelCase__				(self			)					->      Mapping[str, Mapping[int, str]]:

						"""simple docstring"""






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

										if self.use_past:
														lowerCAmelCase__					:    Tuple												=					{0: '''batch'''}
														lowerCAmelCase__					:    Dict												=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										else:
														lowerCAmelCase__					:    Any												=					{0: '''batch''', 1: '''decoder_sequence'''}
														lowerCAmelCase__					:    Optional[int]												=					{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.
										lowerCAmelCase__					:    Union[str, Any]												=					OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]			)
										if self.use_past:
														lowerCAmelCase__						,		lowerCAmelCase__					:    Union[str, Any]												=					self.num_layers
														for i in range(__lowerCamelCase			):
																		lowerCAmelCase__					:    Optional[Any]												=					{0: '''batch''', 2: '''past_sequence + sequence'''}
																		lowerCAmelCase__					:    Dict												=					{0: '''batch''', 2: '''past_sequence + sequence'''}
						else:
										lowerCAmelCase__					:    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
		def 		lowerCAmelCase__				(self			)					->      Mapping[str, Mapping[int, str]]:

						"""simple docstring"""






						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__					:    Dict												=					super().outputs
						else:
										lowerCAmelCase__					:    int												=					super(__lowerCamelCase		,self			).outputs
										if self.use_past:
														lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					self.num_layers
														for i in range(__lowerCamelCase			):
																		lowerCAmelCase__					:    str												=					{0: '''batch''', 2: '''past_sequence + sequence'''}
																		lowerCAmelCase__					:    Union[str, Any]												=					{0: '''batch''', 2: '''past_sequence + sequence'''}
						return common_outputs



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = -1		,__lowerCamelCase = -1		,__lowerCamelCase = False		,__lowerCamelCase = None		,)					->      Mapping[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)

						# Generate decoder inputs
						lowerCAmelCase__					:    List[str]												=					seq_length if not self.use_past else 1
						lowerCAmelCase__					:    int												=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
						lowerCAmelCase__					:    Union[str, Any]												=					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
										lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					common_inputs['''input_ids'''].shape
										lowerCAmelCase__					:    List[Any]												=					common_inputs['''decoder_input_ids'''].shape[1]
										lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					self.num_attention_heads
										lowerCAmelCase__					:    Optional[Any]												=					(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										lowerCAmelCase__					:    Tuple												=					decoder_seq_length + 3
										lowerCAmelCase__					:    int												=					(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										lowerCAmelCase__					:    Optional[int]												=					torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCamelCase		,__lowerCamelCase			)]		,dim=1			)

										lowerCAmelCase__					:    Dict												=					[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					self.num_layers
										lowerCAmelCase__					:    Tuple												=					min(__lowerCamelCase		,__lowerCamelCase			)
										lowerCAmelCase__					:    Optional[int]												=					max(__lowerCamelCase		,__lowerCamelCase			) - min_num_layers
										lowerCAmelCase__					:    Optional[int]												=					'''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.
										lowerCAmelCase__					:    Union[str, Any]												=					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		,__lowerCamelCase		,__lowerCamelCase = -1		,__lowerCamelCase = -1		,__lowerCamelCase = False		,__lowerCamelCase = None		,)					->      Mapping[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					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
										lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					common_inputs['''input_ids'''].shape
										# Not using the same length for past_key_values
										lowerCAmelCase__					:    List[Any]												=					seqlen + 2
										lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					self.num_layers
										lowerCAmelCase__						,		lowerCAmelCase__					:    Union[str, Any]												=					self.num_attention_heads
										lowerCAmelCase__					:    List[Any]												=					(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										lowerCAmelCase__					:    List[str]												=					common_inputs['''attention_mask'''].dtype
										lowerCAmelCase__					:    List[str]												=					torch.cat(
										    [common_inputs['''attention_mask'''], torch.ones(__lowerCamelCase		,__lowerCamelCase		,dtype=__lowerCamelCase			)]		,dim=1			)
										lowerCAmelCase__					:    Tuple												=					[
										    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
										]
						return common_inputs



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = -1		,__lowerCamelCase = -1		,__lowerCamelCase = False		,__lowerCamelCase = None		,)					->      Mapping[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					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
						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					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
						lowerCAmelCase__					:    Union[str, Any]												=					[''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
						lowerCAmelCase__					:    List[str]												=					dict(tokenizer(__lowerCamelCase		,return_tensors=__lowerCamelCase			)			)
						return common_inputs



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = -1		,__lowerCamelCase = -1		,__lowerCamelCase = False		,__lowerCamelCase = None		,)					->      Mapping[str, Any]:

						"""simple docstring"""






						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__					:    List[str]												=					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":
										lowerCAmelCase__					:    List[str]												=					self._generate_dummy_inputs_for_causal_lm(
										    __lowerCamelCase		,batch_size=__lowerCamelCase		,seq_length=__lowerCamelCase		,is_pair=__lowerCamelCase		,framework=__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					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		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__					:    Union[str, Any]												=					super()._flatten_past_key_values_(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Optional[int]												=					super(__lowerCamelCase		,self			)._flatten_past_key_values_(
										    __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


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

