

'''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


_lowerCamelCase	 =			logging.get_logger(__name__)

_lowerCamelCase	 =			{
    """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						(__SCREAMING_SNAKE_CASE):
			__A   :     int          ="bigbird_pegasus"
			__A   :     Tuple          =["past_key_values"]
			__A   :     Tuple          ={
			    "num_attention_heads": "encoder_attention_heads",
			    "hidden_size": "d_model",
			    "attention_probs_dropout_prob": "attention_dropout",
			}






			def __init__(   self       ,_snake_case=9_61_03       ,_snake_case=40_96       ,_snake_case=16       ,_snake_case=40_96       ,_snake_case=16       ,_snake_case=16       ,_snake_case=40_96       ,_snake_case=16       ,_snake_case=0.0       ,_snake_case=0.0       ,_snake_case=True       ,_snake_case=True       ,_snake_case="gelu_new"       ,_snake_case=10_24       ,_snake_case=0.1       ,_snake_case=0.0       ,_snake_case=0.0       ,_snake_case=0.02       ,_snake_case=2       ,_snake_case=0.0       ,_snake_case=True       ,_snake_case=0       ,_snake_case=2       ,_snake_case=1       ,_snake_case="block_sparse"       ,_snake_case=64       ,_snake_case=3       ,_snake_case=False       ,**_snake_case       ,):
				UpperCAmelCase_			:	Any      =   vocab_size
				UpperCAmelCase_			:	Tuple      =   max_position_embeddings
				UpperCAmelCase_			:	Tuple      =   d_model
				UpperCAmelCase_			:	Optional[int]      =   encoder_ffn_dim
				UpperCAmelCase_			:	Union[str, Any]      =   encoder_layers
				UpperCAmelCase_			:	Tuple      =   encoder_attention_heads
				UpperCAmelCase_			:	str      =   decoder_ffn_dim
				UpperCAmelCase_			:	List[Any]      =   decoder_layers
				UpperCAmelCase_			:	Union[str, Any]      =   decoder_attention_heads
				UpperCAmelCase_			:	Tuple      =   dropout
				UpperCAmelCase_			:	Tuple      =   attention_dropout
				UpperCAmelCase_			:	Optional[Any]      =   activation_dropout
				UpperCAmelCase_			:	Optional[int]      =   activation_function
				UpperCAmelCase_			:	Tuple      =   init_std
				UpperCAmelCase_			:	Dict      =   encoder_layerdrop
				UpperCAmelCase_			:	Union[str, Any]      =   decoder_layerdrop
				UpperCAmelCase_			:	int      =   classifier_dropout
				UpperCAmelCase_			:	List[str]      =   use_cache
				UpperCAmelCase_			:	Tuple      =   encoder_layers
				UpperCAmelCase_			:	str      =   scale_embedding  # scale factor will be sqrt(d_model) if True

				# extra config
				UpperCAmelCase_			:	List[str]      =   attention_type
				UpperCAmelCase_			:	Dict      =   block_size
				UpperCAmelCase_			:	Tuple      =   num_random_blocks
				UpperCAmelCase_			:	List[Any]      =   use_bias

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




class  _snake_case						(__SCREAMING_SNAKE_CASE):






			@property
			def 						UpperCamelCase__				(   self			):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCAmelCase_			:	Union[str, Any]      =   OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]			)

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

					if self.use_past:
						self.fill_with_past_key_values_(_snake_case       ,direction="inputs"			)
				elif self.task == "causal-lm":
					# TODO: figure this case out.
					UpperCAmelCase_			:	Union[str, Any]      =   OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]			)
					if self.use_past:
						UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   self.num_layers
						for i in range(_snake_case			):
							UpperCAmelCase_			:	Tuple      =   {0: "batch", 2: "past_sequence + sequence"}
							UpperCAmelCase_			:	List[str]      =   {0: "batch", 2: "past_sequence + sequence"}
				else:
					UpperCAmelCase_			:	Union[str, Any]      =   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 						UpperCamelCase__				(   self			):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCAmelCase_			:	List[str]      =   super().outputs
				else:
					UpperCAmelCase_			:	str      =   super(_snake_case       ,self			).outputs
					if self.use_past:
						UpperCAmelCase_ , UpperCAmelCase_			:	str      =   self.num_layers
						for i in range(_snake_case			):
							UpperCAmelCase_			:	Union[str, Any]      =   {0: "batch", 2: "past_sequence + sequence"}
							UpperCAmelCase_			:	Tuple      =   {0: "batch", 2: "past_sequence + sequence"}
				return common_outputs






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case = -1       ,_snake_case = -1       ,_snake_case = False       ,_snake_case = None       ,):
				UpperCAmelCase_			:	Union[str, Any]      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case			)

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

				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
					else:
						import torch
					UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   common_inputs["input_ids"].shape
					UpperCAmelCase_			:	int      =   common_inputs["decoder_input_ids"].shape[1]
					UpperCAmelCase_ , 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_			:	Union[str, Any]      =   decoder_seq_length + 3
					UpperCAmelCase_			:	Any      =   (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

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

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

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






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case = -1       ,_snake_case = -1       ,_snake_case = False       ,_snake_case = None       ,):
				UpperCAmelCase_			:	List[str]      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case			)

				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
					else:
						import torch
					UpperCAmelCase_ , UpperCAmelCase_			:	Tuple      =   common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					UpperCAmelCase_			:	int      =   seqlen + 2
					UpperCAmelCase_ , UpperCAmelCase_			:	int      =   self.num_layers
					UpperCAmelCase_ , UpperCAmelCase_			:	int      =   self.num_attention_heads
					UpperCAmelCase_			:	int      =   (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					UpperCAmelCase_			:	Union[str, Any]      =   common_inputs["attention_mask"].dtype
					UpperCAmelCase_			:	List[Any]      =   torch.cat(
					    [common_inputs["attention_mask"], torch.ones(_snake_case       ,_snake_case       ,dtype=_snake_case			)]       ,dim=1			)
					UpperCAmelCase_			:	Optional[int]      =   [
					    (torch.zeros(_snake_case			), torch.zeros(_snake_case			)) for _ in range(_snake_case			)
					]
				return common_inputs






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case = -1       ,_snake_case = -1       ,_snake_case = False       ,_snake_case = None       ,):
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCAmelCase_			:	Dict      =   compute_effective_axis_dimension(
				    _snake_case       ,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_			:	Any      =   tokenizer.num_special_tokens_to_add(_snake_case			)
				UpperCAmelCase_			:	List[Any]      =   compute_effective_axis_dimension(
				    _snake_case       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_snake_case			)

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






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case = -1       ,_snake_case = -1       ,_snake_case = False       ,_snake_case = None       ,):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCAmelCase_			:	int      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    _snake_case       ,batch_size=_snake_case       ,seq_length=_snake_case       ,is_pair=_snake_case       ,framework=_snake_case			)

				elif self.task == "causal-lm":
					UpperCAmelCase_			:	Optional[int]      =   self._generate_dummy_inputs_for_causal_lm(
					    _snake_case       ,batch_size=_snake_case       ,seq_length=_snake_case       ,is_pair=_snake_case       ,framework=_snake_case			)
				else:
					UpperCAmelCase_			:	str      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    _snake_case       ,batch_size=_snake_case       ,seq_length=_snake_case       ,is_pair=_snake_case       ,framework=_snake_case			)

				return common_inputs






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case			):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCAmelCase_			:	int      =   super()._flatten_past_key_values_(_snake_case       ,_snake_case       ,_snake_case       ,_snake_case			)
				else:
					UpperCAmelCase_			:	Tuple      =   super(_snake_case       ,self			)._flatten_past_key_values_(
					    _snake_case       ,_snake_case       ,_snake_case       ,_snake_case			)





from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




