





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

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


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

__UpperCAmelCase       :   Optional[Any]				       =							{
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     "bart"
       lowerCAmelCase__      		=     ["past_key_values"]
       lowerCAmelCase__      		=     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




       def __init__(  self :    List[Any]							,				A :    Optional[int]=50_265							,				A :    List[Any]=1_024							,				A :    Tuple=12							,				A :    Optional[Any]=4_096							,				A :    str=16							,				A :    Any=12							,				A :    int=4_096							,				A :    List[str]=16							,				A :    Any=0.0							,				A :    Any=0.0							,				A :    Dict="gelu"							,				A :    str=1_024							,				A :    Optional[int]=0.1							,				A :    List[str]=0.0							,				A :    List[str]=0.0							,				A :    Tuple=0.02							,				A :    Optional[Any]=0.0							,				A :    Any=False							,				A :    List[Any]=True							,				A :    Optional[Any]=3							,				A :    List[Any]=1							,				A :    List[str]=0							,				A :    Dict=2							,				A :    List[str]=True							,				A :    List[str]=2							,				A :    Union[str, Any]=2							,				**A :    int							,				):
         __snake_case:						Optional[Any]   =						vocab_size
         __snake_case:						Union[str, Any]   =						max_position_embeddings
         __snake_case:						List[str]   =						d_model
         __snake_case:						int   =						encoder_ffn_dim
         __snake_case:						str   =						encoder_layers
         __snake_case:						Dict   =						encoder_attention_heads
         __snake_case:						Union[str, Any]   =						decoder_ffn_dim
         __snake_case:						str   =						decoder_layers
         __snake_case:						Any   =						decoder_attention_heads
         __snake_case:						Optional[Any]   =						dropout
         __snake_case:						int   =						attention_dropout
         __snake_case:						int   =						activation_dropout
         __snake_case:						Union[str, Any]   =						activation_function
         __snake_case:						str   =						init_std
         __snake_case:						int   =						encoder_layerdrop
         __snake_case:						Optional[Any]   =						decoder_layerdrop
         __snake_case:						Union[str, Any]   =						classifier_dropout
         __snake_case:						str   =						use_cache
         __snake_case:						Any   =						encoder_layers
         __snake_case:						List[str]   =						scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=A							,				pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				is_encoder_decoder=A							,				decoder_start_token_id=A							,				forced_eos_token_id=A							,				**A							,				)

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,				A		):
           __snake_case:						Optional[Any]   =						self.bos_token_id
           warnings.warn(
               f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
               """The config can simply be saved and uploaded again to be fixed."""		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    int		):


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

           if self.use_past:
             __snake_case:						Optional[Any]   =						{0: """batch"""}
             __snake_case:						str   =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
             __snake_case:						Tuple   =						{0: """batch""", 1: """decoder_sequence"""}
             __snake_case:						Dict   =						{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
             self.fill_with_past_key_values_(A							,				direction="""inputs"""		)
         elif self.task == "causal-lm":
           # TODO: figure this case out.
           __snake_case:						str   =						OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]		)
           if self.use_past:
             __snake_case  ,   __snake_case:						Any   =						self.num_layers
             for i in range(A		):
               __snake_case:						List[str]   =						{0: """batch""", 2: """past_sequence + sequence"""}
               __snake_case:						List[str]   =						{0: """batch""", 2: """past_sequence + sequence"""}
         else:
           __snake_case:						Dict   =						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 							UpperCAmelCase__ (  self :    Dict		):


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




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    PreTrainedTokenizer							,				A :    int = -1							,				A :    int = -1							,				A :    bool = False							,				A :    Optional[TensorType] = None							,				):
         __snake_case:						Any   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A							,				A							,				A							,				A							,				A		)

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

           __snake_case:						Optional[Any]   =						torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(A							,				A		)]							,				dim=1		)

           __snake_case:						Optional[Any]   =						[]
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           __snake_case  ,   __snake_case:						str   =						self.num_layers
           __snake_case:						str   =						min(A							,				A		)
           __snake_case:						Any   =						max(A							,				A		) - min_num_layers
           __snake_case:						List[Any]   =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

           for _ in range(A		):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(A		),
                     torch.zeros(A		),
                     torch.zeros(A		),
                     torch.zeros(A		),
                 )		)
           # TODO: test this.
           __snake_case:						Optional[Any]   =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(A							,				A		):
             common_inputs["past_key_values"].append((torch.zeros(A		), torch.zeros(A		))		)
         return common_inputs




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    PreTrainedTokenizer							,				A :    int = -1							,				A :    int = -1							,				A :    bool = False							,				A :    Optional[TensorType] = None							,				):
         __snake_case:						Optional[int]   =						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
           __snake_case  ,   __snake_case:						Tuple   =						common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           __snake_case:						Union[str, Any]   =						seqlen + 2
           __snake_case  ,   __snake_case:						Dict   =						self.num_layers
           __snake_case  ,   __snake_case:						Dict   =						self.num_attention_heads
           __snake_case:						Optional[Any]   =						(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           __snake_case:						Optional[Any]   =						common_inputs["""attention_mask"""].dtype
           __snake_case:						int   =						torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(A							,				A							,				dtype=A		)]							,				dim=1		)
           __snake_case:						List[str]   =						[
               (torch.zeros(A		), torch.zeros(A		)) for _ in range(A		)
           ]
         return common_inputs




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    PreTrainedTokenizer							,				A :    int = -1							,				A :    int = -1							,				A :    bool = False							,				A :    Optional[TensorType] = None							,				):
         __snake_case:						Optional[Any]   =						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
         __snake_case:						List[Any]   =						tokenizer.num_special_tokens_to_add(A		)
         __snake_case:						int   =						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
         __snake_case:						Union[str, Any]   =						[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
         __snake_case:						Union[str, Any]   =						dict(tokenizer(A							,				return_tensors=A		)		)
         return common_inputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    PreTrainedTokenizer							,				A :    int = -1							,				A :    int = -1							,				A :    bool = False							,				A :    Optional[TensorType] = None							,				):


         if self.task in ["default", "seq2seq-lm"]:
           __snake_case:						List[str]   =						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":
           __snake_case:						str   =						self._generate_dummy_inputs_for_causal_lm(
               A							,				batch_size=A							,				seq_length=A							,				is_pair=A							,				framework=A		)
         else:
           __snake_case:						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 							UpperCAmelCase__ (  self :    Tuple							,				A :    Union[str, Any]							,				A :    Tuple							,				A :    int							,				A :    int		):


         if self.task in ["default", "seq2seq-lm"]:
           __snake_case:						Any   =						super()._flatten_past_key_values_(A							,				A							,				A							,				A		)
         else:
           __snake_case:						List[Any]   =						super(A							,				self		)._flatten_past_key_values_(
               A							,				A							,				A							,				A		)








from collections.abc import Sequence






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			return sum(c * (x**i) for i, c in enumerate(__lowercase ) )






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			_snake_case		=			0.0
			for coeff in reversed(__lowercase ):
						_snake_case		=			result * x + coeff
			return result


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[Any]       =			(0.0, 0.0, 5.0, 9.3, 7.0)
			_lowerCamelCase       :				Optional[int]       =			1_0.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))