




from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
     _lowerCamelCase		  =			1
     _lowerCamelCase		  =			2
     _lowerCamelCase		  =			3

     def __repr__(    self ):







      '''simple docstring'''




      return f"{self.__class__.__name__}.{self.name}"






def  snake_case (							A						,       A						,       A      )							->							float:




 '''simple docstring'''


 lowerCamelCase_							:		str							       =							point
 return degrees(atana(y - miny						,       x - minx      )      )






def  snake_case (							A						,       A						,       A      )							->							Direction:




 '''simple docstring'''


 lowerCamelCase_							:		Dict							       =							starting
 lowerCamelCase_							:		List[Any]							       =							via
 lowerCamelCase_							:		int							       =							target
 lowerCamelCase_							:		Dict							       =							degrees(atana(ya - ya						,       xa - xa      )      )
 via_angle %= 360
 lowerCamelCase_							:		Optional[int]							       =							degrees(atana(ya - ya						,       xa - xa      )      )
 target_angle %= 360
 # t-
 #  \ \
 #   \ v
 #    \|
 #     s
 # via_angle is always lower than target_angle, if direction is left.
 # If they are same, it means they are on a same line of convex hull.
 if target_angle > via_angle:
  return Direction.left
 elif target_angle == via_angle:
  return Direction.straight
 else:
  return Direction.right






def  snake_case (							A      )							->							list[tuple[int, int]]:




 '''simple docstring'''


 if len(A      ) <= 2:
  # There is no convex hull
  raise ValueError('graham_scan: argument must contain more than 3 points.'      )
 if len(A      ) == 3:
  return points
 # find the lowest and the most left point
 lowerCamelCase_							:		Any							       =							0
 lowerCamelCase_							:		Any							       =							maxsize, maxsize
 for i, point in enumerate(A      ):
  lowerCamelCase_							:		Dict							       =							point[0]
  lowerCamelCase_							:		Optional[Any]							       =							point[1]
  if y < miny:
   lowerCamelCase_							:		Dict							       =							y
   lowerCamelCase_							:		Optional[int]							       =							x
   lowerCamelCase_							:		int							       =							i
  if y == miny and x < minx:
   lowerCamelCase_							:		Union[str, Any]							       =							x
   lowerCamelCase_							:		Tuple							       =							i

    # remove the lowest and the most left point from points for preparing for sort
 points.pop(A      )

 lowerCamelCase_							:		List[Any]							       =							sorted(A						,       key=lambda A      : angle_comparer(A						,       A						,       A      )      )
 # This insert actually costs complexity,
 # and you should instead add (minx, miny) into stack later.
 # I'm using insert just for easy understanding.
 sorted_points.insert(0						,       (minx, miny)      )

 lowerCamelCase_							:		deque[tuple[int, int]]							       =							deque()
 stack.append(sorted_points[0]      )
 stack.append(sorted_points[1]      )
 stack.append(sorted_points[2]      )
 # In any ways, the first 3 points line are towards left.
 # Because we sort them the angle from minx, miny.
 lowerCamelCase_							:		List[Any]							       =							Direction.left

 for i in range(3						,       len(A      )      ):
  while True:
   lowerCamelCase_							:		List[str]							       =							stack[-2]
   lowerCamelCase_							:		Dict							       =							stack[-1]
   lowerCamelCase_							:		str							       =							sorted_points[i]
   lowerCamelCase_							:		Union[str, Any]							       =							check_direction(A						,       A						,       A      )

   if next_direction == Direction.left:
    lowerCamelCase_							:		List[str]							       =							Direction.left
    break
   if next_direction == Direction.straight:
    if current_direction == Direction.left:
     # We keep current_direction as left.
     # Because if the straight line keeps as straight,
     # we want to know if this straight line is towards left.
     break
    elif current_direction == Direction.right:
     # If the straight line is towards right,
     # every previous points on those straigh line is not convex hull.
     stack.pop()
   if next_direction == Direction.right:
    stack.pop()
  stack.append(sorted_points[i]      )
 return list(A      )






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

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


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			"""m2m_100"""
					_lowerCamelCase		  =			["""past_key_values"""]
					_lowerCamelCase		  =			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(    self			,	__lowercase=128_112			,	__lowercase=1_024			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=0.05			,	__lowercase=0.05			,	__lowercase=True			,	__lowercase=True			,	__lowercase="relu"			,	__lowercase=1_024			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.0			,	__lowercase=0.02			,	__lowercase=2			,	__lowercase=True			,	__lowercase=1			,	__lowercase=0			,	__lowercase=2			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							max_position_embeddings
						lowerCamelCase_							:		Tuple							       =							d_model
						lowerCamelCase_							:		Optional[int]							       =							encoder_ffn_dim
						lowerCamelCase_							:		Union[str, Any]							       =							encoder_layers
						lowerCamelCase_							:		Dict							       =							encoder_attention_heads
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_ffn_dim
						lowerCamelCase_							:		List[str]							       =							decoder_layers
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_attention_heads
						lowerCamelCase_							:		Optional[int]							       =							dropout
						lowerCamelCase_							:		str							       =							attention_dropout
						lowerCamelCase_							:		Optional[Any]							       =							activation_dropout
						lowerCamelCase_							:		str							       =							activation_function
						lowerCamelCase_							:		int							       =							init_std
						lowerCamelCase_							:		Optional[int]							       =							encoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							decoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							use_cache
						lowerCamelCase_							:		Any							       =							encoder_layers
						lowerCamelCase_							:		Any							       =							scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=__lowercase			,	bos_token_id=__lowercase			,	eos_token_id=__lowercase			,	is_encoder_decoder=__lowercase			,	decoder_start_token_id=__lowercase			,	**__lowercase			,	)


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )

						if self.use_past:
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch'}
							lowerCamelCase_							:		Optional[Any]							       =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
							lowerCamelCase_							:		Any							       =							{0: 'batch', 1: 'decoder_sequence'}
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
							self.fill_with_past_key_values_(__lowercase			,	direction='inputs' )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							compute_effective_axis_dimension(
						    __lowercase			,	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_							:		str							       =							tokenizer.num_special_tokens_to_add(__lowercase )
						lowerCamelCase_							:		Dict							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_sequence			,	num_token_to_add=__lowercase )

						# Generate dummy inputs according to compute batch and sequence
						lowerCamelCase_							:		Optional[int]							       =							[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
						lowerCamelCase_							:		Optional[Any]							       =							dict(tokenizer(__lowercase			,	return_tensors=__lowercase ) )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )

						# Generate decoder inputs
						lowerCamelCase_							:		Dict							       =							seq_length if not self.use_past else 1
						lowerCamelCase_							:		List[str]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Any							       =							{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						lowerCamelCase_							:		str							       =							dict(**__lowercase			,	**__lowercase )

						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_							:		List[Any]							       =							common_inputs['decoder_input_ids'].shape[1]
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_attention_heads
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							lowerCamelCase_							:		Tuple							       =							decoder_seq_length + 3
							lowerCamelCase_							:		Any							       =							(
							    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(__lowercase			,	__lowercase )]			,	dim=1 )

							lowerCamelCase_							:		Tuple							       =							[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_layers
							lowerCamelCase_							:		Optional[int]							       =							min(__lowercase			,	__lowercase )
							lowerCamelCase_							:		int							       =							max(__lowercase			,	__lowercase ) - min_num_layers
							lowerCamelCase_							:		Union[str, Any]							       =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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





					_lowerCamelCase		  =			_generate_dummy_inputs_for_default_and_seqaseq_lm
