



"""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       :      List[Any]							=							logging.get_logger(__name__)

_lowerCAmelCase       :      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 			UpperCAmelCase_						(       lowerCAmelCase__		):
      __SCREAMING_SNAKE_CASE		:     Tuple				  =				"""bigbird_pegasus"""
      __SCREAMING_SNAKE_CASE		:     int				  =				["""past_key_values"""]
      __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				{
          """num_attention_heads""": """encoder_attention_heads""",
          """hidden_size""": """d_model""",
          """attention_probs_dropout_prob""": """attention_dropout""",
      }



      def __init__(  self  :			List[Any]  ,			A  :			Union[str, Any]=9_6_1_0_3  ,			A  :			Tuple=4_0_9_6  ,			A  :			Optional[int]=1_6  ,			A  :			Any=4_0_9_6  ,			A  :			int=1_6  ,			A  :			Optional[Any]=1_6  ,			A  :			List[str]=4_0_9_6  ,			A  :			List[str]=1_6  ,			A  :			int=0.0  ,			A  :			str=0.0  ,			A  :			Optional[Any]=True  ,			A  :			Union[str, Any]=True  ,			A  :			Any="gelu_new"  ,			A  :			str=1_0_2_4  ,			A  :			Tuple=0.1  ,			A  :			int=0.0  ,			A  :			str=0.0  ,			A  :			Optional[Any]=0.02  ,			A  :			List[str]=2  ,			A  :			Optional[Any]=0.0  ,			A  :			List[Any]=True  ,			A  :			Optional[int]=0  ,			A  :			List[str]=2  ,			A  :			Dict=1  ,			A  :			Optional[Any]="block_sparse"  ,			A  :			int=6_4  ,			A  :			List[Any]=3  ,			A  :			Optional[Any]=False  ,			**A  :			Dict  ,			):
           _UpperCAmelCase    :			Any              =					vocab_size
           _UpperCAmelCase    :			str              =					max_position_embeddings
           _UpperCAmelCase    :			Optional[int]              =					d_model
           _UpperCAmelCase    :			List[str]              =					encoder_ffn_dim
           _UpperCAmelCase    :			List[Any]              =					encoder_layers
           _UpperCAmelCase    :			Optional[int]              =					encoder_attention_heads
           _UpperCAmelCase    :			Union[str, Any]              =					decoder_ffn_dim
           _UpperCAmelCase    :			Tuple              =					decoder_layers
           _UpperCAmelCase    :			Any              =					decoder_attention_heads
           _UpperCAmelCase    :			Union[str, Any]              =					dropout
           _UpperCAmelCase    :			List[Any]              =					attention_dropout
           _UpperCAmelCase    :			List[Any]              =					activation_dropout
           _UpperCAmelCase    :			Any              =					activation_function
           _UpperCAmelCase    :			Dict              =					init_std
           _UpperCAmelCase    :			List[Any]              =					encoder_layerdrop
           _UpperCAmelCase    :			Dict              =					decoder_layerdrop
           _UpperCAmelCase    :			List[Any]              =					classifier_dropout
           _UpperCAmelCase    :			Dict              =					use_cache
           _UpperCAmelCase    :			Optional[Any]              =					encoder_layers
           _UpperCAmelCase    :			Union[str, Any]              =					scale_embedding  # scale factor will be sqrt(d_model) if True

           # extra config
           _UpperCAmelCase    :			Any              =					attention_type
           _UpperCAmelCase    :			str              =					block_size
           _UpperCAmelCase    :			Any              =					num_random_blocks
           _UpperCAmelCase    :			Tuple              =					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 			UpperCAmelCase_						(       lowerCAmelCase__		):



      @property
      def 		snake_case_      (  self  :			List[Any]		):






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

                if self.use_past:
                     _UpperCAmelCase    :			List[str]              =					{0: "batch"}
                     _UpperCAmelCase    :			Optional[int]              =					{0: "batch", 1: "past_decoder_sequence + sequence"}
                else:
                     _UpperCAmelCase    :			List[str]              =					{0: "batch", 1: "decoder_sequence"}
                     _UpperCAmelCase    :			Union[str, Any]              =					{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.
                _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    :			List[str]              =					self.num_layers
                     for i in range(lowerCAmelCase_		):
                          _UpperCAmelCase    :			int              =					{0: "batch", 2: "past_sequence + sequence"}
                          _UpperCAmelCase    :			Union[str, Any]              =					{0: "batch", 2: "past_sequence + sequence"}
           else:
                _UpperCAmelCase    :			Optional[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 		snake_case_      (  self  :			Dict		):






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



      def 		snake_case_      (  self  :			List[Any]  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):
           _UpperCAmelCase    :			Union[str, Any]              =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_		)

           # Generate decoder inputs
           _UpperCAmelCase    :			Dict              =					seq_length if not self.use_past else 1
           _UpperCAmelCase    :			List[str]              =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_		)
           _UpperCAmelCase    :			Tuple              =					{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
           _UpperCAmelCase    :			Optional[Any]              =					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
                _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					common_inputs["input_ids"].shape
                _UpperCAmelCase    :			Dict              =					common_inputs["decoder_input_ids"].shape[1]
                _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					self.num_attention_heads
                _UpperCAmelCase    :			Optional[int]              =					(
                    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    :			List[Any]              =					(
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )

                _UpperCAmelCase    :			Tuple              =					torch.cat(
                    [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase_  ,			lowerCAmelCase_		)]  ,			dim=1		)

                _UpperCAmelCase    :			List[str]              =					[]
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                _UpperCAmelCase       , _UpperCAmelCase    :			int              =					self.num_layers
                _UpperCAmelCase    :			Any              =					min(lowerCAmelCase_  ,			lowerCAmelCase_		)
                _UpperCAmelCase    :			Any              =					max(lowerCAmelCase_  ,			lowerCAmelCase_		) - min_num_layers
                _UpperCAmelCase    :			Dict              =					"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.
                _UpperCAmelCase    :			Optional[Any]              =					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 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):
           _UpperCAmelCase    :			int              =					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
                _UpperCAmelCase       , _UpperCAmelCase    :			int              =					common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                _UpperCAmelCase    :			Union[str, Any]              =					seqlen + 2
                _UpperCAmelCase       , _UpperCAmelCase    :			Union[str, Any]              =					self.num_layers
                _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					self.num_attention_heads
                _UpperCAmelCase    :			Optional[int]              =					(
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                _UpperCAmelCase    :			Any              =					common_inputs["attention_mask"].dtype
                _UpperCAmelCase    :			Dict              =					torch.cat(
                    [common_inputs["attention_mask"], torch.ones(lowerCAmelCase_  ,			lowerCAmelCase_  ,			dtype=lowerCAmelCase_		)]  ,			dim=1		)
                _UpperCAmelCase    :			Union[str, Any]              =					[
                    (torch.zeros(lowerCAmelCase_		), torch.zeros(lowerCAmelCase_		)) for _ in range(lowerCAmelCase_		)
                ]
           return common_inputs



      def 		snake_case_      (  self  :			Dict  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):
           _UpperCAmelCase    :			Dict              =					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
           _UpperCAmelCase    :			Dict              =					tokenizer.num_special_tokens_to_add(lowerCAmelCase_		)
           _UpperCAmelCase    :			int              =					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
           _UpperCAmelCase    :			Any              =					[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
           _UpperCAmelCase    :			Union[str, Any]              =					dict(tokenizer(lowerCAmelCase_  ,			return_tensors=lowerCAmelCase_		)		)
           return common_inputs



      def 		snake_case_      (  self  :			List[str]  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):






           if self.task in ["default", "seq2seq-lm"]:
                _UpperCAmelCase    :			Tuple              =					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":
                _UpperCAmelCase    :			Optional[int]              =					self._generate_dummy_inputs_for_causal_lm(
                    lowerCAmelCase_  ,			batch_size=lowerCAmelCase_  ,			seq_length=lowerCAmelCase_  ,			is_pair=lowerCAmelCase_  ,			framework=lowerCAmelCase_		)
           else:
                _UpperCAmelCase    :			Dict              =					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 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Dict  ,			A  :			Optional[Any]  ,			A  :			str  ,			A  :			List[Any]		):






           if self.task in ["default", "seq2seq-lm"]:
                _UpperCAmelCase    :			List[str]              =					super()._flatten_past_key_values_(lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_		)
           else:
                _UpperCAmelCase    :			Optional[int]              =					super(lowerCAmelCase_  ,			self		)._flatten_past_key_values_(
                    lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_		)





"""simple docstring"""




import sys

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:





     '''simple docstring'''

     if not numbers:
          return 0
     if target == 0 and target in numbers:
          return 0
     if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
         isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
          raise ValueError("numbers must be an iterable of integers"       )

     _UpperCAmelCase    :			List[Any]              =					0
     _UpperCAmelCase    :			str              =					sys.maxsize

     while right < len(SCREAMING_SNAKE_CASE__       ):
          curr_sum += numbers[right]
          while curr_sum >= target and left <= right:
               _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
               curr_sum -= numbers[left]
               left += 1
          right += 1

     return 0 if min_len == sys.maxsize else min_len
