


'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


__snake_case          =  logging.get_logger(__name__)

__snake_case          =  {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'blenderbot-small'
		_a									=       ['past_key_values']
		_a									=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




		def __init__(	self					,     UpperCamelCase_=50265					,     UpperCamelCase_=512					,     UpperCamelCase_=8					,     UpperCamelCase_=2048					,     UpperCamelCase_=16					,     UpperCamelCase_=8					,     UpperCamelCase_=2048					,     UpperCamelCase_=16					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=512					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1					,     UpperCamelCase_=False					,     UpperCamelCase_=0					,     UpperCamelCase_=1					,     UpperCamelCase_=2					,     UpperCamelCase_=2					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				vocab_size
							UpperCamelCase__     :List[str]							      =				max_position_embeddings
							UpperCamelCase__     :Union[str, Any]							      =				d_model
							UpperCamelCase__     :Optional[int]							      =				encoder_ffn_dim
							UpperCamelCase__     :Optional[Any]							      =				encoder_layers
							UpperCamelCase__     :Optional[Any]							      =				encoder_attention_heads
							UpperCamelCase__     :List[str]							      =				decoder_ffn_dim
							UpperCamelCase__     :Optional[int]							      =				decoder_layers
							UpperCamelCase__     :Optional[Any]							      =				decoder_attention_heads
							UpperCamelCase__     :Any							      =				dropout
							UpperCamelCase__     :Union[str, Any]							      =				attention_dropout
							UpperCamelCase__     :Any							      =				activation_dropout
							UpperCamelCase__     :Any							      =				activation_function
							UpperCamelCase__     :Dict							      =				init_std
							UpperCamelCase__     :int							      =				encoder_layerdrop
							UpperCamelCase__     :Optional[Any]							      =				decoder_layerdrop
							UpperCamelCase__     :Any							      =				use_cache
							UpperCamelCase__     :Optional[Any]							      =				encoder_layers
							UpperCamelCase__     :str							      =				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_					,     forced_eos_token_id=UpperCamelCase_					,     **UpperCamelCase_					,     )



class      lowercase			(							A__      ):

		"""simple docstring"""

		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__     :Optional[Any]							      =				OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]							)

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

												if self.use_past:
																	self.fill_with_past_key_values_(UpperCamelCase_					,     direction='''inputs'''							)
							elif self.task == "causal-lm":
												# TODO: figure this case out.
												UpperCamelCase__     :str							      =				OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]							)
												if self.use_past:
																	UpperCamelCase__     :Optional[int]							      =				self.num_layers
																	for i in range(UpperCamelCase_							):
																						UpperCamelCase__     :Dict							      =				{0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__     :int							      =				{0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
												UpperCamelCase__     :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							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__     :Any							      =				super().outputs
							else:
												UpperCamelCase__     :Dict							      =				super(UpperCamelCase_					,     self							).outputs
												if self.use_past:
																	UpperCamelCase__     :Optional[Any]							      =				self.num_layers
																	for i in range(UpperCamelCase_							):
																						UpperCamelCase__     :Optional[int]							      =				{0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__     :Union[str, Any]							      =				{0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

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

												UpperCamelCase__     :int							      =				torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_					,     UpperCamelCase_							)]					,     dim=1							)

												UpperCamelCase__     :Union[str, Any]							      =				[]
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												UpperCamelCase__     :Dict							      =				self.num_layers
												UpperCamelCase__     :List[Any]							      =				min(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :Any							      =				max(UpperCamelCase_					,     UpperCamelCase_							) - min_num_layers
												UpperCamelCase__     :Dict							      =				'''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.
												UpperCamelCase__     :Optional[Any]							      =				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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     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
												UpperCamelCase__     :Any							      =				common_inputs['''input_ids'''].shape
												# Not using the same length for past_key_values
												UpperCamelCase__     :Dict							      =				seqlen + 2
												UpperCamelCase__     :str							      =				self.num_layers
												UpperCamelCase__     :Union[str, Any]							      =				self.num_attention_heads
												UpperCamelCase__     :str							      =				(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												UpperCamelCase__     :List[str]							      =				common_inputs['''attention_mask'''].dtype
												UpperCamelCase__     :Union[str, Any]							      =				torch.cat(
												    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_					,     UpperCamelCase_					,     dtype=UpperCamelCase_							)]					,     dim=1							)
												UpperCamelCase__     :Dict							      =				[
												    (torch.zeros(UpperCamelCase_							), torch.zeros(UpperCamelCase_							)) for _ in range(UpperCamelCase_							)
												]
							return common_inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				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
							UpperCamelCase__     :Union[str, Any]							      =				tokenizer.num_special_tokens_to_add(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				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
							UpperCamelCase__     :Dict							      =				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
							UpperCamelCase__     :Union[str, Any]							      =				dict(tokenizer(UpperCamelCase_					,     return_tensors=UpperCamelCase_							)							)
							return common_inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__     :Dict							      =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    UpperCamelCase_					,     batch_size=UpperCamelCase_					,     seq_length=UpperCamelCase_					,     is_pair=UpperCamelCase_					,     framework=UpperCamelCase_							)

							elif self.task == "causal-lm":
												UpperCamelCase__     :Optional[Any]							      =				self._generate_dummy_inputs_for_causal_lm(
												    UpperCamelCase_					,     batch_size=UpperCamelCase_					,     seq_length=UpperCamelCase_					,     is_pair=UpperCamelCase_					,     framework=UpperCamelCase_							)
							else:
												UpperCamelCase__     :Union[str, Any]							      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    UpperCamelCase_					,     batch_size=UpperCamelCase_					,     seq_length=UpperCamelCase_					,     is_pair=UpperCamelCase_					,     framework=UpperCamelCase_							)

							return common_inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__     :List[Any]							      =				super()._flatten_past_key_values_(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
							else:
												UpperCamelCase__     :str							      =				super(UpperCamelCase_					,     self							)._flatten_past_key_values_(
												    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)



'''simple docstring'''
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = ""					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__     :dict[str, RadixNode]							      =				{}

							# A node will be a leaf if the tree contains its word
							UpperCamelCase__     :List[Any]							      =				is_leaf

							UpperCamelCase__     :Dict							      =				prefix




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				0
							for q, w in zip(self.prefix					,     UpperCamelCase_							):
												if q != w:
																	break

												x += 1

							return self.prefix[:x], self.prefix[x:], word[x:]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							for word in words:
												self.insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.prefix == word:
												UpperCamelCase__     :Tuple							      =				True

							# Case 2: The node has no edges that have a prefix to the word
							# Solution: We create an edge from the current node to a new one
							# containing the word
							elif word[0] not in self.nodes:
												UpperCamelCase__     :Optional[Any]							      =				RadixNode(prefix=UpperCamelCase_					,     is_leaf=UpperCamelCase_							)

							else:
												UpperCamelCase__     :Optional[Any]							      =				self.nodes[word[0]]
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				incoming_node.match(
												    UpperCamelCase_							)

												# Case 3: The node prefix is equal to the matching
												# Solution: We insert remaining word on the next node
												if remaining_prefix == "":
																	self.nodes[matching_string[0]].insert(UpperCamelCase_							)

												# Case 4: The word is greater equal to the matching
												# Solution: Create a node in between both nodes, change
												# prefixes and add the new node for the remaining word
												else:
																	UpperCamelCase__     :str							      =				remaining_prefix

																	UpperCamelCase__     :Optional[Any]							      =				self.nodes[matching_string[0]]
																	UpperCamelCase__     :Dict							      =				RadixNode(UpperCamelCase_					,     UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				aux_node

																	if remaining_word == "":
																						UpperCamelCase__     :List[Any]							      =				True
																	else:
																						self.nodes[matching_string[0]].insert(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																	return False
												# This applies when the word and the prefix are equal
												elif remaining_word == "":
																	return incoming_node.is_leaf
												# We have word remaining so we check the next node
												else:
																	return incoming_node.find(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.nodes.get(word[0]					,     UpperCamelCase_							)
							if not incoming_node:
												return False
							else:
												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				incoming_node.match(
												    UpperCamelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																	return False
												# We have word remaining so we check the next node
												elif remaining_word != "":
																	return incoming_node.delete(UpperCamelCase_							)
												else:
																	# If it is not a leaf, we don't have to delete
																	if not incoming_node.is_leaf:
																						return False
																	else:
																						# We delete the nodes if no edges go from it
																						if len(incoming_node.nodes							) == 0:
																											del self.nodes[word[0]]
																											# We merge the current node with its only child
																											if len(self.nodes							) == 1 and not self.is_leaf:
																																UpperCamelCase__     :Tuple							      =				list(self.nodes.values()							)[0]
																																UpperCamelCase__     :Union[str, Any]							      =				merging_node.is_leaf
																																self.prefix += merging_node.prefix
																																UpperCamelCase__     :Dict							      =				merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																						elif len(incoming_node.nodes							) > 1:
																											UpperCamelCase__     :List[str]							      =				False
																						# If there is 1 edge, we merge it with its child
																						else:
																											UpperCamelCase__     :Optional[int]							      =				list(incoming_node.nodes.values()							)[0]
																											UpperCamelCase__     :int							      =				merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											UpperCamelCase__     :Optional[int]							      =				merging_node.nodes

																						return True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_ = 0							):

							'''simple docstring'''


							if self.prefix != "":
												print('''-''' * height					,     self.prefix					,     '''  (leaf)''' if self.is_leaf else ''''''							)

							for value in self.nodes.values():
												value.print_tree(height + 1							)



def     a   (    )						->			bool:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				'''banana bananas bandana band apple all beast'''.split()
					UpperCamelCase__     :Tuple							      =				RadixNode()
					root.insert_many(__a     )

					assert all(root.find(__a     ) for word in words     )
					assert not root.find('''bandanas'''     )
					assert not root.find('''apps'''     )
					root.delete('''all'''     )
					assert not root.find('''all'''     )
					root.delete('''banana'''     )
					assert not root.find('''banana'''     )
					assert root.find('''bananas'''     )

					return True


def     a   (    )						->			None:



					'''simple docstring'''






					assert test_trie()


def     a   (    )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				RadixNode()
					UpperCamelCase__     :List[Any]							      =				'''banana bananas bandanas bandana band apple all beast'''.split()
					root.insert_many(__a     )

					print('''Words:'''				,      __a     )
					print('''Tree:'''     )
					root.print_tree()


if __name__ == "__main__":
		main()