
"""simple docstring"""






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 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    '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      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE				):
			UpperCAmelCase_    :Union[str, Any]												=		"mbart"
			UpperCAmelCase_    :Dict												=		["past_key_values"]
			UpperCAmelCase_    :int												=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






			def __init__(   self			,  __A=5_0265			,  __A=1024			,  __A=12			,  __A=4096			,  __A=16			,  __A=12			,  __A=4096			,  __A=16			,  __A=0.0			,  __A=0.0			,  __A=True			,  __A=True			,  __A="gelu"			,  __A=1024			,  __A=0.1			,  __A=0.0			,  __A=0.0			,  __A=0.0_2			,  __A=0.0			,  __A=False			,  __A=1			,  __A=0			,  __A=2			,  __A=2			,  **__A			,  )     ->	Tuple:
						lowerCAmelCase_       :Dict     	=    vocab_size
						lowerCAmelCase_       :List[str]     	=    max_position_embeddings
						lowerCAmelCase_       :List[str]     	=    d_model
						lowerCAmelCase_       :Dict     	=    encoder_ffn_dim
						lowerCAmelCase_       :List[str]     	=    encoder_layers
						lowerCAmelCase_       :List[Any]     	=    encoder_attention_heads
						lowerCAmelCase_       :int     	=    decoder_ffn_dim
						lowerCAmelCase_       :List[Any]     	=    decoder_layers
						lowerCAmelCase_       :Any     	=    decoder_attention_heads
						lowerCAmelCase_       :List[str]     	=    dropout
						lowerCAmelCase_       :List[Any]     	=    attention_dropout
						lowerCAmelCase_       :int     	=    activation_dropout
						lowerCAmelCase_       :int     	=    activation_function
						lowerCAmelCase_       :List[Any]     	=    init_std
						lowerCAmelCase_       :str     	=    encoder_layerdrop
						lowerCAmelCase_       :Union[str, Any]     	=    decoder_layerdrop
						lowerCAmelCase_       :Dict     	=    classifier_dropout
						lowerCAmelCase_       :Any     	=    use_cache
						lowerCAmelCase_       :str     	=    encoder_layers
						lowerCAmelCase_       :int     	=    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      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE				):






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
									lowerCAmelCase_       :int     	=    OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]			)

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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = -1			,  __A = -1			,  __A = False			,  __A = None			,  )     ->	Mapping[str, Any]:
						lowerCAmelCase_       :Optional[int]     	=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _A			,  _A			,  _A			,  _A			,  _A			)

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

									lowerCAmelCase_       :List[Any]     	=    torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(_A			,  _A			)]			,  dim=1			)

									lowerCAmelCase_       :Dict     	=    []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									lowerCAmelCase_	,			lowerCAmelCase_       :List[Any]     	=    self.num_layers
									lowerCAmelCase_       :Optional[Any]     	=    min(_A			,  _A			)
									lowerCAmelCase_       :Optional[int]     	=    max(_A			,  _A			) - min_num_layers
									lowerCAmelCase_       :Dict     	=    """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.
									lowerCAmelCase_       :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 					__lowerCAmelCase					(   self			,  __A			,  __A = -1			,  __A = -1			,  __A = False			,  __A = None			,  )     ->	Mapping[str, Any]:
						lowerCAmelCase_       :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
									lowerCAmelCase_	,			lowerCAmelCase_       :Dict     	=    common_inputs["""input_ids"""].shape
									# Not using the same length for past_key_values
									lowerCAmelCase_       :Union[str, Any]     	=    seqlen + 2
									lowerCAmelCase_	,			lowerCAmelCase_       :Tuple     	=    self.num_layers
									lowerCAmelCase_	,			lowerCAmelCase_       :str     	=    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_       :Optional[int]     	=    common_inputs["""attention_mask"""].dtype
									lowerCAmelCase_       :str     	=    torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(_A			,  _A			,  dtype=_A			)]			,  dim=1			)
									lowerCAmelCase_       :List[Any]     	=    [
									    (torch.zeros(_A			), torch.zeros(_A			)) for _ in range(_A			)
									]
						return common_inputs






			def 					__lowerCAmelCase					(   self			,  __A			,  __A = -1			,  __A = -1			,  __A = False			,  __A = 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_       :Union[str, 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
						lowerCAmelCase_       :int     	=    tokenizer.num_special_tokens_to_add(_A			)
						lowerCAmelCase_       :Optional[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
						lowerCAmelCase_       :List[Any]     	=    [""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
						lowerCAmelCase_       :str     	=    dict(tokenizer(_A			,  return_tensors=_A			)			)
						return common_inputs






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








from __future__ import annotations

from collections.abc import Iterable





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      float(_A	), float(_A	)



       def __eq__(							self ,     _A	)				->				str:
          return self.x == other.x and self.y == other.y



       def __ne__(							self ,     _A	)				->				Any:
          return not self == other



       def __gt__(							self ,     _A	)				->				List[Any]:
          if self.x > other.x:
             return True
          elif self.x == other.x:
             return self.y > other.y
          return False



       def __lt__(							self ,     _A	)				->				Optional[int]:
          return not self > other



       def __ge__(							self ,     _A	)				->				int:
          if self.x > other.x:
             return True
          elif self.x == other.x:
             return self.y >= other.y
          return False



       def __le__(							self ,     _A	)				->				str:
          if self.x < other.x:
             return True
          elif self.x == other.x:
             return self.y <= other.y
          return False



       def __repr__(							self	)				->				str:
          return F'''({self.x}, {self.y})'''




       def __hash__(							self	)				->				List[str]:
          return hash(self.x	)







def  A__ (       __lowerCamelCase,  ):
   SCREAMING_SNAKE_CASE_	=      []
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(__lowerCamelCase,  __lowerCamelCase						):
            points.append(__lowerCamelCase						)
         else:
            try:
               points.append(Point(p[0],  p[1]						)						)
            except (IndexError, TypeError):
               print(
                   F'''Ignoring deformed point {p}. All points'''
                   ''' must have at least 2 coordinates.'''						)
   return points


def  A__ (       __lowerCamelCase						):

   if not hasattr(__lowerCamelCase,  '''__iter__'''						):
      SCREAMING_SNAKE_CASE_	=      F'''Expecting an iterable object but got an non-iterable type {points}'''
      raise ValueError(__lowerCamelCase						)

   if not points:
      SCREAMING_SNAKE_CASE_	=      F'''Expecting a list of points but got {points}'''
      raise ValueError(__lowerCamelCase						)

   return _construct_points(__lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
   return det


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      set()

   for i in range(n - 1						):
      for j in range(i + 1,  __lowerCamelCase						):
         SCREAMING_SNAKE_CASE_	=      SCREAMING_SNAKE_CASE_	=      False
         SCREAMING_SNAKE_CASE_	=      True
         for k in range(__lowerCamelCase						):
            if k != i and k != j:
               SCREAMING_SNAKE_CASE_	=      _det(points[i],  points[j],  points[k]						)

               if det_k > 0:
                  SCREAMING_SNAKE_CASE_	=      True
               elif det_k < 0:
                  SCREAMING_SNAKE_CASE_	=      True
               else:
                  # point[i], point[j], point[k] all lie on a straight line
                  # if point[k] is to the left of point[i] or it's to the
                  # right of point[j], then point[i], point[j] cannot be
                  # part of the convex hull of A
                  if points[k] < points[i] or points[k] > points[j]:
                     SCREAMING_SNAKE_CASE_	=      False
                     break

            if points_left_of_ij and points_right_of_ij:
               SCREAMING_SNAKE_CASE_	=      False
               break

         if ij_part_of_convex_hull:
            convex_set.update([points[i], points[j]]						)

   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)

   # divide all the points into an upper hull and a lower hull
   # the left most point and the right most point are definitely
   # members of the convex hull by definition.
   # use these two anchors to divide all the points into two hulls,
   # an upper hull and a lower hull.

   # all points to the left (above) the line joining the extreme points belong to the
   # upper hull
   # all points to the right (below) the line joining the extreme points below to the
   # lower hull
   # ignore all points on the line joining the extreme points since they cannot be
   # part of the convex hull

   SCREAMING_SNAKE_CASE_	=      points[0]
   SCREAMING_SNAKE_CASE_	=      points[n - 1]

   SCREAMING_SNAKE_CASE_	=      {left_most_point, right_most_point}
   SCREAMING_SNAKE_CASE_	=      []
   SCREAMING_SNAKE_CASE_	=      []

   for i in range(1,  n - 1						):
      SCREAMING_SNAKE_CASE_	=      _det(__lowerCamelCase,  __lowerCamelCase,  points[i]						)

      if det > 0:
         upper_hull.append(points[i]						)
      elif det < 0:
         lower_hull.append(points[i]						)

   _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)
   _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)

   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   if points:
      SCREAMING_SNAKE_CASE_	=      None
      SCREAMING_SNAKE_CASE_	=      float('''-inf'''						)
      SCREAMING_SNAKE_CASE_	=      []

      for p in points:
         SCREAMING_SNAKE_CASE_	=      _det(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)

         if det > 0:
            candidate_points.append(__lowerCamelCase						)

            if det > extreme_point_distance:
               SCREAMING_SNAKE_CASE_	=      det
               SCREAMING_SNAKE_CASE_	=      p

      if extreme_point:
         _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)
         convex_set.add(__lowerCamelCase						)
         _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      points[:2]
   for i in range(2,  __lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	=      _det(convex_hull[1],  convex_hull[0],  points[i]						)
      if det > 0:
         convex_hull.insert(0,  points[i]						)
         break
      elif det < 0:
         convex_hull.append(points[i]						)
         break
      else:
         SCREAMING_SNAKE_CASE_	=      points[i]
   i += 1

   for j in range(__lowerCamelCase,  __lowerCamelCase						):
      if (
          _det(convex_hull[0],  convex_hull[-1],  points[j]						) > 0
          and _det(convex_hull[-1],  convex_hull[0],  points[1]						) < 0
      ):
         # The point lies within the convex hull
         continue

      convex_hull.insert(0,  points[j]						)
      convex_hull.append(points[j]						)
      while _det(convex_hull[0],  convex_hull[1],  convex_hull[2]						) >= 0:
         del convex_hull[1]
      while _det(convex_hull[-1],  convex_hull[-2],  convex_hull[-3]						) <= 0:
         del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
   return sorted(convex_hull[1:] if len(__lowerCamelCase						) > 3 else convex_hull						)


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      [
       (0, 3),
       (2, 2),
       (1, 1),
       (2, 1),
       (3, 0),
       (0, 0),
       (3, 3),
       (2, -1),
       (2, -4),
       (1, -3),
   ]
   # the convex set of points is
   # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
   SCREAMING_SNAKE_CASE_	=      convex_hull_bf(__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      convex_hull_recursive(__lowerCamelCase						)
   assert results_bf == results_recursive

   SCREAMING_SNAKE_CASE_	=      convex_hull_melkman(__lowerCamelCase						)
   assert results_bf == results_melkman

   print(__lowerCamelCase						)


if __name__ == "__main__":
   main()


