


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


A_														=      logging.get_logger(__name__)

A_														=      {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			UpperCAmelCase       :       Tuple       = """bigbird_pegasus"""
			UpperCAmelCase       :       int       = ["""past_key_values"""]
			UpperCAmelCase       :       Union[str, Any]       = {
			    """num_attention_heads""": """encoder_attention_heads""",
			    """hidden_size""": """d_model""",
			    """attention_probs_dropout_prob""": """attention_dropout""",
			}

			def __init__(	self     :     List[Any]							,							lowerCAmelCase_     :     Union[str, Any]=96_103							,							lowerCAmelCase_     :     Tuple=4_096							,							lowerCAmelCase_     :     Optional[int]=16							,							lowerCAmelCase_     :     Any=4_096							,							lowerCAmelCase_     :     int=16							,							lowerCAmelCase_     :     Optional[Any]=16							,							lowerCAmelCase_     :     List[str]=4_096							,							lowerCAmelCase_     :     List[str]=16							,							lowerCAmelCase_     :     int=0.0							,							lowerCAmelCase_     :     str=0.0							,							lowerCAmelCase_     :     Optional[Any]=True							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     Any="gelu_new"							,							lowerCAmelCase_     :     str=1_024							,							lowerCAmelCase_     :     Tuple=0.1							,							lowerCAmelCase_     :     int=0.0							,							lowerCAmelCase_     :     str=0.0							,							lowerCAmelCase_     :     Optional[Any]=0.02							,							lowerCAmelCase_     :     List[str]=2							,							lowerCAmelCase_     :     Optional[Any]=0.0							,							lowerCAmelCase_     :     List[Any]=True							,							lowerCAmelCase_     :     Optional[int]=0							,							lowerCAmelCase_     :     List[str]=2							,							lowerCAmelCase_     :     Dict=1							,							lowerCAmelCase_     :     Optional[Any]="block_sparse"							,							lowerCAmelCase_     :     int=64							,							lowerCAmelCase_     :     List[Any]=3							,							lowerCAmelCase_     :     Optional[Any]=False							,							**lowerCAmelCase_     :     Dict							,							)					->					List[Any]:




								"""simple docstring"""






								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

								# extra config
								SCREAMING_SNAKE_CASE_	      =       attention_type
								SCREAMING_SNAKE_CASE_	      =       block_size
								SCREAMING_SNAKE_CASE_	      =       num_random_blocks
								SCREAMING_SNAKE_CASE_	      =       use_bias

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

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			@property
			def _lowercase (	self     :     List[Any]					)					->					Mapping[str, Mapping[int, str]]:




								"""simple docstring"""






								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_(lowerCAmelCase_							,							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(lowerCAmelCase_					):
																							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 _lowercase (	self     :     Dict					)					->					Mapping[str, Mapping[int, str]]:




								"""simple docstring"""






								if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE_	      =       super().outputs
								else:
													SCREAMING_SNAKE_CASE_	      =       super(lowerCAmelCase_							,							self					).outputs
													if self.use_past:
																		SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.num_layers
																		for i in range(lowerCAmelCase_					):
																							SCREAMING_SNAKE_CASE_	      =       {0: '''batch''', 2: '''past_sequence + sequence'''}
																							SCREAMING_SNAKE_CASE_	      =       {0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs

			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     PreTrainedTokenizer							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     bool = False							,							lowerCAmelCase_     :     Optional[TensorType] = None							,							)					->					Mapping[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)

								# 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(
								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								SCREAMING_SNAKE_CASE_	      =       dict(**lowerCAmelCase_							,							**lowerCAmelCase_					)

								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(lowerCAmelCase_							,							lowerCAmelCase_					)]							,							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(lowerCAmelCase_							,							lowerCAmelCase_					)
													SCREAMING_SNAKE_CASE_	      =       max(lowerCAmelCase_							,							lowerCAmelCase_					) - min_num_layers
													SCREAMING_SNAKE_CASE_	      =       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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

			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     PreTrainedTokenizer							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     bool = False							,							lowerCAmelCase_     :     Optional[TensorType] = None							,							)					->					Mapping[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)

								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(lowerCAmelCase_							,							lowerCAmelCase_							,							dtype=lowerCAmelCase_					)]							,							dim=1					)
													SCREAMING_SNAKE_CASE_	      =       [
													    (torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					)) for _ in range(lowerCAmelCase_					)
													]
								return common_inputs

			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     PreTrainedTokenizer							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     bool = False							,							lowerCAmelCase_     :     Optional[TensorType] = None							,							)					->					Mapping[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       compute_effective_axis_dimension(
								    lowerCAmelCase_							,							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(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       compute_effective_axis_dimension(
								    lowerCAmelCase_							,							fixed_dimension=OnnxConfig.default_fixed_sequence							,							num_token_to_add=lowerCAmelCase_					)

								# 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(lowerCAmelCase_							,							return_tensors=lowerCAmelCase_					)					)
								return common_inputs

			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     PreTrainedTokenizer							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     int = -1							,							lowerCAmelCase_     :     bool = False							,							lowerCAmelCase_     :     Optional[TensorType] = None							,							)					->					Mapping[str, Any]:




								"""simple docstring"""






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

								elif self.task == "causal-lm":
													SCREAMING_SNAKE_CASE_	      =       self._generate_dummy_inputs_for_causal_lm(
													    lowerCAmelCase_							,							batch_size=lowerCAmelCase_							,							seq_length=lowerCAmelCase_							,							is_pair=lowerCAmelCase_							,							framework=lowerCAmelCase_					)
								else:
													SCREAMING_SNAKE_CASE_	      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase_							,							batch_size=lowerCAmelCase_							,							seq_length=lowerCAmelCase_							,							is_pair=lowerCAmelCase_							,							framework=lowerCAmelCase_					)

								return common_inputs




			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Dict							,							lowerCAmelCase_     :     Optional[Any]							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     List[Any]					)					->					Optional[Any]:




								"""simple docstring"""






								if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE_	      =       super()._flatten_past_key_values_(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
								else:
													SCREAMING_SNAKE_CASE_	      =       super(lowerCAmelCase_							,							self					)._flatten_past_key_values_(
													    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)






from __future__ import annotations

class 						snake_case			:
			'''simple docstring'''

			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       value
								SCREAMING_SNAKE_CASE_	      =       None
								SCREAMING_SNAKE_CASE_	      =       None

class 						snake_case			:
			'''simple docstring'''

			UpperCAmelCase       :       int

			def __init__(	self     :     Union[str, Any]					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       0

			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								if node is None:
													return

								if path_sum == self.target:
													self.paths += 1

								if node.left:
													self.depth_first_search(node.left							,							path_sum + node.left.value					)
								if node.right:
													self.depth_first_search(node.right							,							path_sum + node.right.value					)




			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:




								"""simple docstring"""






								if node is None:
													return 0
								if target is not None:
													SCREAMING_SNAKE_CASE_	      =       target

								self.depth_first_search(lowerCAmelCase_							,							node.value					)
								self.path_sum(node.left					)
								self.path_sum(node.right					)

								return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()


