





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/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "mbart"
							lowerCamelCase_							=    ["past_key_values"]
							lowerCamelCase_							=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(							self			:int							, __A			:List[str]=5_0265							, __A			:Optional[Any]=1024							, __A			:Optional[Any]=12							, __A			:str=4096							, __A			:List[str]=16							, __A			:List[str]=12							, __A			:Dict=4096							, __A			:Optional[int]=16							, __A			:Optional[Any]=0.0							, __A			:Optional[int]=0.0							, __A			:Optional[int]=True							, __A			:Dict=True							, __A			:Dict="gelu"							, __A			:Dict=1024							, __A			:Tuple=0.1							, __A			:List[Any]=0.0							, __A			:int=0.0							, __A			:Union[str, Any]=0.0_2							, __A			:int=0.0							, __A			:Any=False							, __A			:List[str]=1							, __A			:Optional[int]=0							, __A			:Dict=2							, __A			:Optional[int]=2							, **__A			:List[str]							, )      ->      Union[str, Any]:



									"""simple docstring"""



									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__								=					classifier_dropout
									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							, forced_eos_token_id=__A							, **__A							, )




class      UpperCamelCase_				(   UpperCamelCase__			):


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



									"""simple docstring"""



									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":
											# TODO: figure this case out.
											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
							def    _snake_case     (							self			:Any						)      ->      Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



									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			:Any							, __A			:PreTrainedTokenizer							, __A			:int = -1							, __A			:int = -1							, __A			:bool = False							, __A			:Optional[TensorType] = None							, )      ->      Mapping[str, Any]:



									"""simple docstring"""



									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 + 3
											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__								=					[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.num_layers
											SCREAMING_SNAKE_CASE__								=					min(__A							, __A						)
											SCREAMING_SNAKE_CASE__								=					max(__A							, __A						) - min_num_layers
											SCREAMING_SNAKE_CASE__								=					"""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.
											SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Tuple							, __A			:PreTrainedTokenizer							, __A			:int = -1							, __A			:int = -1							, __A			:bool = False							, __A			:Optional[TensorType] = None							, )      ->      Mapping[str, Any]:



									"""simple docstring"""



									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
											# Not using the same length for past_key_values
											SCREAMING_SNAKE_CASE__								=					seqlen + 2
											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			:Any							, __A			:PreTrainedTokenizer							, __A			:int = -1							, __A			:int = -1							, __A			:bool = False							, __A			:Optional[TensorType] = None							, )      ->      Mapping[str, Any]:



									"""simple docstring"""



									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			:Optional[int]							, __A			:PreTrainedTokenizer							, __A			:int = -1							, __A			:int = -1							, __A			:bool = False							, __A			:Optional[TensorType] = None							, )      ->      Mapping[str, Any]:



									"""simple docstring"""



									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			:List[str]							, __A			:Tuple							, __A			:Optional[int]							, __A			:int							, __A			:List[Any]						)      ->      Dict:



									"""simple docstring"""



									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						)


from __future__ import annotations

from math import pi, pow


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int | float				):





    """simple docstring"""


    if side_length < 0:
        raise ValueError('vol_cube() only accepts non-negative values'				)
    return pow(_SCREAMING_SNAKE_CASE       ,      3				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_spherical_cap() only accepts non-negative values'				)
    # Volume is 1/3 pi * height squared * (3 * radius - height)
    return 1 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,      2				) * (3 * radius - height)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a < 0 or radius_a < 0 or centers_distance < 0:
        raise ValueError('vol_spheres_intersect() only accepts non-negative values'				)
    if centers_distance == 0:
        return vol_sphere(min(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)				)

    SCREAMING_SNAKE_CASE_													=  (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )
    SCREAMING_SNAKE_CASE_													=  (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )

    return vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				) + vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
        raise ValueError(
            'vol_spheres_union() only accepts non-negative values, non-zero radius'				)

    if centers_distance == 0:
        return vol_sphere(max(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)				)

    return (
        vol_sphere(_SCREAMING_SNAKE_CASE				)
        + vol_sphere(_SCREAMING_SNAKE_CASE				)
        - vol_spheres_intersect(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
    )


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if width < 0 or height < 0 or length < 0:
        raise ValueError('vol_cuboid() only accepts non-negative values'				)
    return float(width * height * length				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_cone() only accepts non-negative values'				)
    return area_of_base * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_right_circ_cone() only accepts non-negative values'				)
    return pi * pow(_SCREAMING_SNAKE_CASE       ,      2				) * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_prism() only accepts non-negative values'				)
    return float(area_of_base * height				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or area_of_base < 0:
        raise ValueError('vol_pyramid() only accepts non-negative values'				)
    return area_of_base * height / 3.0


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius < 0:
        raise ValueError('vol_sphere() only accepts non-negative values'				)
    # Volume is 4/3 * pi * radius cubed
    return 4 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,      3				)


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius < 0:
        raise ValueError('vol_hemisphere() only accepts non-negative values'				)
    # Volume is radius cubed * pi * 2/3
    return pow(_SCREAMING_SNAKE_CASE       ,      3				) * pi * 2 / 3


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if height < 0 or radius < 0:
        raise ValueError('vol_circular_cylinder() only accepts non-negative values'				)
    # Volume is radius squared * height * pi
    return pow(_SCREAMING_SNAKE_CASE       ,      2				) * height * pi


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if inner_radius < 0 or outer_radius < 0 or height < 0:
        raise ValueError(
            'vol_hollow_circular_cylinder() only accepts non-negative values'				)
    if outer_radius <= inner_radius:
        raise ValueError('outer_radius must be greater than inner_radius'				)
    return pi * (pow(_SCREAMING_SNAKE_CASE       ,      2				) - pow(_SCREAMING_SNAKE_CASE       ,      2				)) * height


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if radius_a < 0 or radius_a < 0 or height < 0:
        raise ValueError('vol_conical_frustum() only accepts non-negative values'				)
    return (
        1
        / 3
        * pi
        * height
        * (pow(_SCREAMING_SNAKE_CASE       ,      2				) + pow(_SCREAMING_SNAKE_CASE       ,      2				) + radius_a * radius_a)
    )


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						float       ,      _SCREAMING_SNAKE_CASE    :						float				):





    """simple docstring"""


    if torus_radius < 0 or tube_radius < 0:
        raise ValueError('vol_torus() only accepts non-negative values'				)
    return 2 * pow(_SCREAMING_SNAKE_CASE       ,      2				) * torus_radius * pow(_SCREAMING_SNAKE_CASE       ,      2				)


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    print('Volumes:'				)
    print(f"""Cube: {vol_cube(2				) = }"""				)  # = 8
    print(f"""Cuboid: {vol_cuboid(2       ,      2       ,      2				) = }"""				)  # = 8
    print(f"""Cone: {vol_cone(2       ,      2				) = }"""				)  # ~= 1.33
    print(f"""Right Circular Cone: {vol_right_circ_cone(2       ,      2				) = }"""				)  # ~= 8.38
    print(f"""Prism: {vol_prism(2       ,      2				) = }"""				)  # = 4
    print(f"""Pyramid: {vol_pyramid(2       ,      2				) = }"""				)  # ~= 1.33
    print(f"""Sphere: {vol_sphere(2				) = }"""				)  # ~= 33.5
    print(f"""Hemisphere: {vol_hemisphere(2				) = }"""				)  # ~= 16.75
    print(f"""Circular Cylinder: {vol_circular_cylinder(2       ,      2				) = }"""				)  # ~= 25.1
    print(f"""Torus: {vol_torus(2       ,      2				) = }"""				)  # ~= 157.9
    print(f"""Conical Frustum: {vol_conical_frustum(2       ,      2       ,      4				) = }"""				)  # ~= 58.6
    print(f"""Spherical cap: {vol_spherical_cap(1       ,      2				) = }"""				)  # ~= 5.24
    print(f"""Spheres intersetion: {vol_spheres_intersect(2       ,      2       ,      1				) = }"""				)  # ~= 21.21
    print(f"""Spheres union: {vol_spheres_union(2       ,      2       ,      1				) = }"""				)  # ~= 45.81
    print(
        f"""Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,      2       ,      3				) = }"""				)  # ~= 28.3


if __name__ == "__main__":
       main()


