


"""simple docstring"""
from ..utils import DummyObject, requires_backends


__SCREAMING_SNAKE_CASE				:     List[Any]		=     None







def     _a					(   *_SCREAMING_SNAKE_CASE					,			**_SCREAMING_SNAKE_CASE					)     ->					Dict:
							requires_backends(_SCREAMING_SNAKE_CASE					,			["""sentencepiece""", """tokenizers"""]					)







"""simple docstring"""
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


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     str		=     {
    '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   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """bigbird_pegasus"""
							__lowercase:		Any				   =     ["""past_key_values"""]
							__lowercase:		Optional[Any]				   =     {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							}



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



														"""simple docstring"""



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

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

														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   __A  (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



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

																					if self.use_past:
																												snake_case_     							=		{0: """batch"""}
																												snake_case_     							=		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																					else:
																												snake_case_     							=		{0: """batch""", 1: """decoder_sequence"""}
																												snake_case_     							=		{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.
																					snake_case_     							=		OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																					    ]							)
																					if self.use_past:
																												snake_case_							,    snake_case_     							=		self.num_layers
																												for i in range(UpperCAmelCase_							):
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
														else:
																					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 		lowerCAmelCase							(					self				:   Dict							)       ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



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



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



														"""simple docstring"""



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

														# Generate decoder inputs
														snake_case_     							=		seq_length if not self.use_past else 1
														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														snake_case_     							=		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
																					snake_case_							,    snake_case_     							=		common_inputs["""input_ids"""].shape
																					snake_case_     							=		common_inputs["""decoder_input_ids"""].shape[1]
																					snake_case_							,    snake_case_     							=		self.num_attention_heads
																					snake_case_     							=		(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					snake_case_     							=		decoder_seq_length + 3
																					snake_case_     							=		(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

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

																					snake_case_     							=		[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					snake_case_							,    snake_case_     							=		self.num_layers
																					snake_case_     							=		min(UpperCAmelCase_		,							UpperCAmelCase_							)
																					snake_case_     							=		max(UpperCAmelCase_		,							UpperCAmelCase_							) - min_num_layers
																					snake_case_     							=		"""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.
																					snake_case_     							=		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				:   Union[str, Any]		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		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
																					snake_case_							,    snake_case_     							=		common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					snake_case_     							=		seqlen + 2
																					snake_case_							,    snake_case_     							=		self.num_layers
																					snake_case_							,    snake_case_     							=		self.num_attention_heads
																					snake_case_     							=		(
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					snake_case_     							=		common_inputs["""attention_mask"""].dtype
																					snake_case_     							=		torch.cat(
																					    [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_		,							dtype=UpperCAmelCase_							)]		,							dim=1							)
																					snake_case_     							=		[
																					    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(UpperCAmelCase_							)
																					]
														return common_inputs



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



														"""simple docstring"""



														snake_case_     							=		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
														snake_case_     							=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
														snake_case_     							=		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
														snake_case_     							=		[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
														snake_case_     							=		dict(tokenizer(UpperCAmelCase_		,							return_tensors=UpperCAmelCase_							)							)
														return common_inputs



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



														"""simple docstring"""



														if self.task in ["default", "seq2seq-lm"]:
																					snake_case_     							=		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":
																					snake_case_     							=		self._generate_dummy_inputs_for_causal_lm(
																					    UpperCAmelCase_		,							batch_size=UpperCAmelCase_		,							seq_length=UpperCAmelCase_		,							is_pair=UpperCAmelCase_		,							framework=UpperCAmelCase_							)
														else:
																					snake_case_     							=		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				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any							)       ->Tuple:



														"""simple docstring"""



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



