






"""simple docstring"""
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


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Tuple 			=					"""bart"""
	_A							:					Dict 			=					["""past_key_values"""]
	_A							:					List[str] 			=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(self  ,  lowerCAmelCase_=50265  ,  lowerCAmelCase_=1024  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=4096  ,  lowerCAmelCase_=16  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=4096  ,  lowerCAmelCase_=16  ,  lowerCAmelCase_=0.0  ,  lowerCAmelCase_=0.0  ,  lowerCAmelCase_="gelu"  ,  lowerCAmelCase_=1024  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.0  ,  lowerCAmelCase_=0.0  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=0.0  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=1  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=2  ,  **lowerCAmelCase_  ,  ):
		A_		:    str						     = vocab_size
		A_		:    Dict						     = max_position_embeddings
		A_		:    Optional[Any]						     = d_model
		A_		:    List[Any]						     = encoder_ffn_dim
		A_		:    List[str]						     = encoder_layers
		A_		:    Optional[Any]						     = encoder_attention_heads
		A_		:    Optional[Any]						     = decoder_ffn_dim
		A_		:    Optional[int]						     = decoder_layers
		A_		:    Optional[Any]						     = decoder_attention_heads
		A_		:    Any						     = dropout
		A_		:    Any						     = attention_dropout
		A_		:    List[str]						     = activation_dropout
		A_		:    List[Any]						     = activation_function
		A_		:    str						     = init_std
		A_		:    str						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop
		A_		:    List[str]						     = classifier_dropout
		A_		:    List[Any]						     = use_cache
		A_		:    Union[str, Any]						     = encoder_layers
		A_		:    Dict						     = scale_embedding  # scale factor will be sqrt(d_model) if True

		super().__init__(
		    num_labels=lowerCAmelCase_  ,  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_  ,  )

		# ensure backward compatibility for BART CNN models
		if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""  ,  lowerCAmelCase_				):
			A_		:    str						     = 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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@property
	def  lowerCamelCase(self				):
		if self.task in ["default", "seq2seq-lm"]:
			A_		:    Optional[int]						     = OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
			    ]				)

			if self.use_past:
				A_		:    Any						     = {0: """batch"""}
				A_		:    int						     = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
			else:
				A_		:    Optional[Any]						     = {0: """batch""", 1: """decoder_sequence"""}
				A_		:    Any						     = {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.
			A_		:    List[Any]						     = OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
			    ]				)
			if self.use_past:
				A_     ,  A_		:    Optional[int]						     = self.num_layers
				for i in range(lowerCAmelCase_				):
					A_		:    Any						     = {0: """batch""", 2: """past_sequence + sequence"""}
					A_		:    Dict						     = {0: """batch""", 2: """past_sequence + sequence"""}
		else:
			A_		:    int						     = 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				):
		if self.task in ["default", "seq2seq-lm"]:
			A_		:    Union[str, Any]						     = super().outputs
		else:
			A_		:    Union[str, Any]						     = super(lowerCAmelCase_  ,  self				).outputs
			if self.use_past:
				A_     ,  A_		:    int						     = self.num_layers
				for i in range(lowerCAmelCase_				):
					A_		:    List[Any]						     = {0: """batch""", 2: """past_sequence + sequence"""}
					A_		:    Union[str, Any]						     = {0: """batch""", 2: """past_sequence + sequence"""}
		return common_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = -1  ,  lowerCAmelCase_ = -1  ,  lowerCAmelCase_ = False  ,  lowerCAmelCase_ = None  ,  ):
		A_		:    List[Any]						     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
		    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

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

			A_		:    Optional[int]						     = torch.cat(
			    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase_  ,  lowerCAmelCase_				)]  ,  dim=1				)

			A_		:    Any						     = []
			# If the number of encoder and decoder layers are present in the model configuration, both are considered
			A_     ,  A_		:    List[Any]						     = self.num_layers
			A_		:    Tuple						     = min(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    str						     = max(lowerCAmelCase_  ,  lowerCAmelCase_				) - min_num_layers
			A_		:    Union[str, Any]						     = """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.
			A_		:    List[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  ,  ):
		A_		:    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
			A_     ,  A_		:    List[str]						     = common_inputs["""input_ids"""].shape
			# Not using the same length for past_key_values
			A_		:    Tuple						     = seqlen + 2
			A_     ,  A_		:    Tuple						     = self.num_layers
			A_     ,  A_		:    Dict						     = self.num_attention_heads
			A_		:    Optional[Any]						     = (
			    batch,
			    num_encoder_attention_heads,
			    past_key_values_length,
			    self._config.hidden_size // num_encoder_attention_heads,
			)

			A_		:    Tuple						     = common_inputs["""attention_mask"""].dtype
			A_		:    Union[str, Any]						     = torch.cat(
			    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase_  ,  lowerCAmelCase_  ,  dtype=lowerCAmelCase_				)]  ,  dim=1				)
			A_		:    str						     = [
			    (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  ,  ):
		# 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
		A_		:    Dict						     = 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
		A_		:    Union[str, Any]						     = tokenizer.num_special_tokens_to_add(lowerCAmelCase_				)
		A_		:    Any						     = 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
		A_		:    List[Any]						     = [""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
		A_		:    Union[str, Any]						     = dict(tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_				)				)
		return common_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = -1  ,  lowerCAmelCase_ = -1  ,  lowerCAmelCase_ = False  ,  lowerCAmelCase_ = None  ,  ):
		if self.task in ["default", "seq2seq-lm"]:
			A_		:    List[Any]						     = 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":
			A_		:    Any						     = self._generate_dummy_inputs_for_causal_lm(
			    lowerCAmelCase_  ,  batch_size=lowerCAmelCase_  ,  seq_length=lowerCAmelCase_  ,  is_pair=lowerCAmelCase_  ,  framework=lowerCAmelCase_				)
		else:
			A_		:    str						     = 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_				):
		if self.task in ["default", "seq2seq-lm"]:
			A_		:    str						     = super()._flatten_past_key_values_(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		else:
			A_		:    Union[str, Any]						     = super(lowerCAmelCase_  ,  self				)._flatten_past_key_values_(
			    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)









"""simple docstring"""
from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 10**-10       ):
	A_		:    Tuple						     = a
	while True:
		A_		:    List[str]						     = Decimal(snake_case__       ) - (
		    Decimal(eval(snake_case__       )       ) / Decimal(eval(str(diff(snake_case__       )       )       )       )  # noqa: S307
		)
		# This number dictates the accuracy of the answer
		if abs(eval(snake_case__       )       ) < precision:  # noqa: S307
			return float(snake_case__       )


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

