
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


__a							  =						logging.get_logger(__name__)

__a							  =						{
    "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        UpperCamelCase__(    snake_case__	):

	"""simple docstring"""

	_A  =		"m2m_100"
	_A  =		["past_key_values"]
	_A  =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







	def __init__(      self				:			str							,			snake_case__				:			Tuple=12_81_12							,			snake_case__				:			Optional[Any]=10_24							,			snake_case__				:			Union[str, Any]=12							,			snake_case__				:			Optional[int]=40_96							,			snake_case__				:			List[str]=16							,			snake_case__				:			List[Any]=12							,			snake_case__				:			str=40_96							,			snake_case__				:			Tuple=16							,			snake_case__				:			List[str]=0.05							,			snake_case__				:			Any=0.05							,			snake_case__				:			Tuple=True							,			snake_case__				:			Any=True							,			snake_case__				:			List[str]="relu"							,			snake_case__				:			Any=10_24							,			snake_case__				:			Any=0.1							,			snake_case__				:			Dict=0.1							,			snake_case__				:			int=0.0							,			snake_case__				:			int=0.02							,			snake_case__				:			Tuple=2							,			snake_case__				:			int=True							,			snake_case__				:			int=1							,			snake_case__				:			Union[str, Any]=0							,			snake_case__				:			int=2							,			**snake_case__				:			str							,			):
			"""simple docstring"""





			A				=vocab_size
			A				=max_position_embeddings
			A				=d_model
			A				=encoder_ffn_dim
			A				=encoder_layers
			A				=encoder_attention_heads
			A				=decoder_ffn_dim
			A				=decoder_layers
			A				=decoder_attention_heads
			A				=dropout
			A				=attention_dropout
			A				=activation_dropout
			A				=activation_function
			A				=init_std
			A				=encoder_layerdrop
			A				=decoder_layerdrop
			A				=use_cache
			A				=encoder_layers
			A				=scale_embedding  # scale factor will be sqrt(d_model) if True

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


class        UpperCamelCase__(    snake_case__	):

	"""simple docstring"""

	@property
	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			A				=OrderedDict(
			    [
			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
			    ] )

			if self.use_past:
					A				={0: "batch"}
					A				={0: "batch", 1: "past_decoder_sequence + sequence"}
			else:
					A				={0: "batch", 1: "decoder_sequence"}
					A				={0: "batch", 1: "decoder_sequence"}

			if self.use_past:
					self.fill_with_past_key_values_(UpperCAmelCase_							,			direction="inputs" )
			return common_inputs







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=compute_effective_axis_dimension(
			    UpperCAmelCase_							,			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				=tokenizer.num_special_tokens_to_add(UpperCAmelCase_ )
			A				=compute_effective_axis_dimension(
			    UpperCAmelCase_							,			fixed_dimension=OnnxConfig.default_fixed_sequence							,			num_token_to_add=UpperCAmelCase_ )

			# Generate dummy inputs according to compute batch and sequence
			A				=[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
			A				=dict(tokenizer(UpperCAmelCase_							,			return_tensors=UpperCAmelCase_ ) )
			return common_inputs







	def        _a					(      self				:			str							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_ )

			# Generate decoder inputs
			A				=seq_length if not self.use_past else 1
			A				=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_ )
			A				={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
			A				=dict(**UpperCAmelCase_							,			**UpperCAmelCase_ )

			if self.use_past:
					if not is_torch_available():
							raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
					else:
							import torch
					A				=common_inputs["input_ids"].shape
					A				=common_inputs["decoder_input_ids"].shape[1]
					A				=self.num_attention_heads
					A				=(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					A				=decoder_seq_length + 3
					A				=(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					A				=torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase_							,			UpperCAmelCase_ )]							,			dim=1 )

					A				=[]
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					A				=self.num_layers
					A				=min(UpperCAmelCase_							,			UpperCAmelCase_ )
					A				=max(UpperCAmelCase_							,			UpperCAmelCase_ ) - min_num_layers
					A				="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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

	_A  =		_generate_dummy_inputs_for_default_and_seqaseq_lm











'''simple docstring'''

class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     val
							# Number of nodes in left subtree
							__UpperCAmelCase	:							Dict     =     0
							__UpperCAmelCase	:							int     =     None
							__UpperCAmelCase	:							Optional[int]     =     None
							__UpperCAmelCase	:							List[str]     =     None






					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									__UpperCAmelCase	:							str     =     self.right
									__UpperCAmelCase	:							Tuple     =     None
									if self.right:
											__UpperCAmelCase	:							int     =     other
									__UpperCAmelCase	:							List[str]     =     other
									__UpperCAmelCase	:							Any     =     self.left_tree_size * 2 + 1
									return self
							else:
									__UpperCAmelCase	:							List[Any]     =     other.right
									__UpperCAmelCase	:							Union[str, Any]     =     None
									if other.right:
											__UpperCAmelCase	:							List[str]     =     self
									__UpperCAmelCase	:							Optional[Any]     =     self
									__UpperCAmelCase	:							Any     =     other.left_tree_size * 2 + 1
									return other






class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=0		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     heap_size
							__UpperCAmelCase	:							Any     =     bottom_root
							__UpperCAmelCase	:							List[str]     =     min_node
					def 		lowerCamelCase_   (   self       :      List[str]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									__UpperCAmelCase	:							Dict     =     other.size
									__UpperCAmelCase	:							Optional[Any]     =     other.bottom_root
									__UpperCAmelCase	:							str     =     other.min_node
									return None
							# Update size
							__UpperCAmelCase	:							str     =     self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									__UpperCAmelCase	:							Tuple     =     other.min_node
							# Merge

							# Order roots by left_subtree_size
							__UpperCAmelCase	:							int     =     []
							__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     self.bottom_root, other.bottom_root
							while i or j:
									if i and ((not j) or i.left_tree_size < j.left_tree_size):
											combined_roots_list.append((i, True)		)
											__UpperCAmelCase	:							Optional[Any]     =     i.parent
									else:
											combined_roots_list.append((j, False)		)
											__UpperCAmelCase	:							Tuple     =     j.parent
        # Insert links between them
							for i in range(len(UpperCAmelCase_		) - 1		):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											__UpperCAmelCase	:							Optional[Any]     =     combined_roots_list[i + 1][0]
											__UpperCAmelCase	:							Any     =     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							__UpperCAmelCase	:							List[str]     =     combined_roots_list[0][0]
							while i.parent:
									if (
									    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
									) or (
									    i.left_tree_size == i.parent.left_tree_size
									    and i.left_tree_size != i.parent.parent.left_tree_size
									):
											# Neighbouring Nodes
											__UpperCAmelCase	:							List[str]     =     i.left
											__UpperCAmelCase	:							Dict     =     i.parent.parent

											# Merging trees
											__UpperCAmelCase	:							Optional[int]     =     i.merge_trees(i.parent		)

											# Updating links
											__UpperCAmelCase	:							Dict     =     previous_node
											__UpperCAmelCase	:							Union[str, Any]     =     next_node
											if previous_node:
													__UpperCAmelCase	:							List[str]     =     i
											if next_node:
													__UpperCAmelCase	:							Dict     =     i
									else:
											__UpperCAmelCase	:							Optional[int]     =     i.parent
        # Updating self.bottom_root
							while i.left:
									__UpperCAmelCase	:							str     =     i.left
							__UpperCAmelCase	:							List[Any]     =     i

							# Update other
							__UpperCAmelCase	:							int     =     self.size
							__UpperCAmelCase	:							Dict     =     self.bottom_root
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node

							# Return the merged heap
							return self
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							if self.size == 0:
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     1
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
							else:
									# Create new node
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											__UpperCAmelCase	:							Optional[int]     =     new_node
									# Put new_node as a bottom_root in heap
									__UpperCAmelCase	:							Union[str, Any]     =     new_node
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
									__UpperCAmelCase	:							List[str]     =     new_node

									# Consecutively merge roots with same left_tree_size
									while (
									    self.bottom_root.parent
									    and self.bottom_root.left_tree_size
									    == self.bottom_root.parent.left_tree_size
									):
											# Next node
											__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root.parent.parent

											# Merge
											__UpperCAmelCase	:							str     =     self.bottom_root.merge_trees(self.bottom_root.parent		)

											# Update Links
											__UpperCAmelCase	:							int     =     next_node
											__UpperCAmelCase	:							Any     =     None
											if next_node:
													__UpperCAmelCase	:							Any     =     self.bottom_root
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self.min_node.val
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							return self.size == 0
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							str     =     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									__UpperCAmelCase	:							Optional[int]     =     0

									# Update bottom root
									__UpperCAmelCase	:							List[str]     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     None

									return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
									# Update size
									self.size -= 1

									# Update bottom root
									__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root.parent
									__UpperCAmelCase	:							str     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     self.bottom_root
									__UpperCAmelCase	:							int     =     self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     i
											__UpperCAmelCase	:							Optional[int]     =     i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							__UpperCAmelCase	:							Optional[int]     =     self.min_node.right
							__UpperCAmelCase	:							List[Any]     =     None
							__UpperCAmelCase	:							Optional[int]     =     bottom_of_new
							__UpperCAmelCase	:							Union[str, Any]     =     1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
									__UpperCAmelCase	:							str     =     size_of_new * 2 + 1
									__UpperCAmelCase	:							List[str]     =     bottom_of_new.left
									if bottom_of_new.val < min_of_new.val:
											__UpperCAmelCase	:							Union[str, Any]     =     bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
									__UpperCAmelCase	:							Optional[Any]     =     size_of_new
									__UpperCAmelCase	:							List[str]     =     bottom_of_new
									__UpperCAmelCase	:							List[Any]     =     min_of_new
									# print("Single root, multiple nodes case")
									return min_value
							# Remaining cases
							# Construct heap of right subtree
							__UpperCAmelCase	:							Union[str, Any]     =     BinomialHeap(
							    bottom_root=UpperCAmelCase_		,	min_node=UpperCAmelCase_		,	heap_size=UpperCAmelCase_		)

							# Update size
							__UpperCAmelCase	:							Any     =     self.size - 1 - size_of_new

							# Neighbour nodes
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node.left
							__UpperCAmelCase	:							List[str]     =     self.min_node.parent

							# Initialize new bottom_root and min_node
							__UpperCAmelCase	:							Optional[Any]     =     previous_node or next_node
							__UpperCAmelCase	:							Optional[Any]     =     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
									__UpperCAmelCase	:							List[str]     =     next_node

									# Update bottom_root and search for min_node below
									__UpperCAmelCase	:							List[str]     =     previous_node
									__UpperCAmelCase	:							int     =     previous_node
									while self.bottom_root.left:
											__UpperCAmelCase	:							Dict     =     self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root
							if next_node:
									__UpperCAmelCase	:							Tuple     =     previous_node

									# Search for new min_node above min_node
									__UpperCAmelCase	:							Optional[int]     =     next_node
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							List[Any]     =     i
											__UpperCAmelCase	:							List[Any]     =     i.parent
        # Merge heaps
							self.merge_heaps(UpperCAmelCase_		)

							return min_value
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     self.bottom_root
							while top_root.parent:
									__UpperCAmelCase	:							Optional[int]     =     top_root.parent
							# preorder
							__UpperCAmelCase	:							Union[str, Any]     =     []
							self.__traversal(UpperCAmelCase_		,	UpperCAmelCase_		)
							return heap_pre_order
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Dict=0		):







							"""simple docstring"""
							if curr_node:
									preorder.append((curr_node.val, level)		)
									self.__traversal(curr_node.left		,	UpperCAmelCase_		,	level + 1		)
									self.__traversal(curr_node.right		,	UpperCAmelCase_		,	level + 1		)
							else:
									preorder.append(("#", level)		)






					def __str__(   self       :      Optional[int]		):







							"""simple docstring"""
							if self.is_empty():
									return ""
							__UpperCAmelCase	:							Optional[Any]     =     self.pre_order()

							return "\n".join(("-" * level + str(UpperCAmelCase_		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()




