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_				:		Dict       						=       logging.get_logger(__name__)

lowerCamelCase_				:		Optional[int]       						=       {
    """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		):
			A__				:   Dict								=			'bigbird_pegasus'
			A__				:   List[Any]								=			['past_key_values']
			A__				:   Tuple								=			{
			    'num_attention_heads': 'encoder_attention_heads',
			    'hidden_size': 'd_model',
			    'attention_probs_dropout_prob': 'attention_dropout',
			}



			def __init__(						self      ,						UpperCAmelCase=9_6_1_0_3      ,						UpperCAmelCase=4_0_9_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=4_0_9_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=4_0_9_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase="gelu_new"      ,						UpperCAmelCase=1_0_2_4      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=2      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=True      ,						UpperCAmelCase=0      ,						UpperCAmelCase=2      ,						UpperCAmelCase=1      ,						UpperCAmelCase="block_sparse"      ,						UpperCAmelCase=6_4      ,						UpperCAmelCase=3      ,						UpperCAmelCase=False      ,						**UpperCAmelCase      ,						) ->    str:
								__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							=   classifier_dropout
								__a							=   use_cache
								__a							=   encoder_layers
								__a							=   scale_embedding  # scale factor will be sqrt(d_model) if True

								# extra config
								__a							=   attention_type
								__a							=   block_size
								__a							=   num_random_blocks
								__a							=   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		):



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
													__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'    )
								elif self.task == "causal-lm":
													# TODO: figure this case out.
													__a							=   OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]    )
													if self.use_past:
																		__a					,      __a							=   self.num_layers
																		for i in range(UpperCAmelCase    ):
																							__a							=   {0: 'batch', 2: 'past_sequence + sequence'}
																							__a							=   {0: 'batch', 2: 'past_sequence + sequence'}
								else:
													__a							=   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 __SCREAMING_SNAKE_CASE							(						self    ) ->    Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
													__a							=   super().outputs
								else:
													__a							=   super(UpperCAmelCase      ,						self    ).outputs
													if self.use_past:
																		__a					,      __a							=   self.num_layers
																		for i in range(UpperCAmelCase    ):
																							__a							=   {0: 'batch', 2: 'past_sequence + sequence'}
																							__a							=   {0: 'batch', 2: 'past_sequence + sequence'}
								return common_outputs



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = False      ,						UpperCAmelCase = None      ,						) ->    Mapping[str, Any]:
								__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					,      __a							=   common_inputs['input_ids'].shape
													__a							=   common_inputs['decoder_input_ids'].shape[1]
													__a					,      __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					,      __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



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = False      ,						UpperCAmelCase = None      ,						) ->    Mapping[str, Any]:
								__a							=   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
													__a					,      __a							=   common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													__a							=   seqlen + 2
													__a					,      __a							=   self.num_layers
													__a					,      __a							=   self.num_attention_heads
													__a							=   (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													__a							=   common_inputs['attention_mask'].dtype
													__a							=   torch.cat(
													    [common_inputs['attention_mask'], torch.ones(UpperCAmelCase      ,						UpperCAmelCase      ,						dtype=UpperCAmelCase    )]      ,						dim=1    )
													__a							=   [
													    (torch.zeros(UpperCAmelCase    ), torch.zeros(UpperCAmelCase    )) for _ in range(UpperCAmelCase    )
													]
								return common_inputs



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = False      ,						UpperCAmelCase = None      ,						) ->    Mapping[str, Any]:
								# 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
								__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 __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = -1      ,						UpperCAmelCase = False      ,						UpperCAmelCase = None      ,						) ->    Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
													__a							=   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":
													__a							=   self._generate_dummy_inputs_for_causal_lm(
													    UpperCAmelCase      ,						batch_size=UpperCAmelCase      ,						seq_length=UpperCAmelCase      ,						is_pair=UpperCAmelCase      ,						framework=UpperCAmelCase    )
								else:
													__a							=   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 __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								if self.task in ["default", "seq2seq-lm"]:
													__a							=   super()._flatten_past_key_values_(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								else:
													__a							=   super(UpperCAmelCase      ,						self    )._flatten_past_key_values_(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )





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						)




