
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 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="mbart"
       UpperCAmelCase_				       =["past_key_values"]
       UpperCAmelCase_				       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



       def __init__(							self ,     _A=50265 ,     _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=0.02 ,     _A=0.0 ,     _A=False ,     _A=1 ,     _A=0 ,     _A=2 ,     _A=2 ,     **_A ,     )				->				Tuple:
          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__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          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     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          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     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     )				->				Mapping[str, Any]:
          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     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     )				->				Mapping[str, Any]:
          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     _UpperCamelCase	(							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
          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     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     )				->				Mapping[str, Any]:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A	)				->				Optional[Any]:
          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 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()


