




import warnings
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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   'bart'
      UpperCAmelCase__      :	Dict    		=   ['past_key_values']
      UpperCAmelCase__      :	Union[str, Any]    		=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(				self: str    ,       UpperCamelCase_: Any=5_02_65    ,       UpperCamelCase_: Dict=10_24    ,       UpperCamelCase_: Optional[int]=12    ,       UpperCamelCase_: Dict=40_96    ,       UpperCamelCase_: List[str]=16    ,       UpperCamelCase_: Union[str, Any]=12    ,       UpperCamelCase_: Union[str, Any]=40_96    ,       UpperCamelCase_: List[Any]=16    ,       UpperCamelCase_: Tuple=0.0    ,       UpperCamelCase_: str=0.0    ,       UpperCamelCase_: Any="gelu"    ,       UpperCamelCase_: List[Any]=10_24    ,       UpperCamelCase_: Dict=0.1    ,       UpperCamelCase_: List[str]=0.0    ,       UpperCamelCase_: List[Any]=0.0    ,       UpperCamelCase_: str=0.02    ,       UpperCamelCase_: List[Any]=0.0    ,       UpperCamelCase_: Dict=False    ,       UpperCamelCase_: List[Any]=True    ,       UpperCamelCase_: Dict=3    ,       UpperCamelCase_: Optional[Any]=1    ,       UpperCamelCase_: Dict=0    ,       UpperCamelCase_: Optional[Any]=2    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: List[Any]=2    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						max_position_embeddings
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						encoder_ffn_dim
             __lowerCamelCase   =						encoder_layers
             __lowerCamelCase   =						encoder_attention_heads
             __lowerCamelCase   =						decoder_ffn_dim
             __lowerCamelCase   =						decoder_layers
             __lowerCamelCase   =						decoder_attention_heads
             __lowerCamelCase   =						dropout
             __lowerCamelCase   =						attention_dropout
             __lowerCamelCase   =						activation_dropout
             __lowerCamelCase   =						activation_function
             __lowerCamelCase   =						init_std
             __lowerCamelCase   =						encoder_layerdrop
             __lowerCamelCase   =						decoder_layerdrop
             __lowerCamelCase   =						classifier_dropout
             __lowerCamelCase   =						use_cache
             __lowerCamelCase   =						encoder_layers
             __lowerCamelCase   =						scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 num_labels=UpperCamelCase_    ,       pad_token_id=UpperCamelCase_    ,       bos_token_id=UpperCamelCase_    ,       eos_token_id=UpperCamelCase_    ,       is_encoder_decoder=UpperCamelCase_    ,       decoder_start_token_id=UpperCamelCase_    ,       forced_eos_token_id=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

             # ensure backward compatibility for BART CNN models
             if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						self.bos_token_id
                    warnings.warn(
                        F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                        """The config can simply be saved and uploaded again to be fixed."""   )





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						OrderedDict(
                        [
                            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                        ]   )

                    if self.use_past:
                           __lowerCamelCase   =						{0: """batch"""}
                           __lowerCamelCase   =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                    else:
                           __lowerCamelCase   =						{0: """batch""", 1: """decoder_sequence"""}
                           __lowerCamelCase   =						{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.
                    __lowerCamelCase   =						OrderedDict(
                        [
                            ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                            ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                        ]   )
                    if self.use_past:
                           __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
             else:
                    __lowerCamelCase   =						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   ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						super().outputs
             else:
                    __lowerCamelCase   =						super(UpperCamelCase_    ,       self   ).outputs
                    if self.use_past:
                           __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
             return common_outputs
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             __lowerCamelCase   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )

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

                    __lowerCamelCase   =						torch.cat(
                        [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCamelCase_    ,       UpperCamelCase_   )]    ,       dim=1   )

                    __lowerCamelCase   =						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                    __lowerCamelCase   =						min(UpperCamelCase_    ,       UpperCamelCase_   )
                    __lowerCamelCase   =						max(UpperCamelCase_    ,       UpperCamelCase_   ) - min_num_layers
                    __lowerCamelCase   =						"""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.
                    __lowerCamelCase   =						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    ,       ):
             __lowerCamelCase   =						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
                    __lowerCamelCase,   __lowerCamelCase   =						common_inputs["""input_ids"""].shape
                    # Not using the same length for past_key_values
                    __lowerCamelCase   =						seqlen + 2
                    __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                    __lowerCamelCase,   __lowerCamelCase   =						self.num_attention_heads
                    __lowerCamelCase   =						(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    __lowerCamelCase   =						common_inputs["""attention_mask"""].dtype
                    __lowerCamelCase   =						torch.cat(
                        [common_inputs["""attention_mask"""], torch.ones(UpperCamelCase_    ,       UpperCamelCase_    ,       dtype=UpperCamelCase_   )]    ,       dim=1   )
                    __lowerCamelCase   =						[
                        (torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   )) for _ in range(UpperCamelCase_   )
                    ]
             return common_inputs
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = 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
             __lowerCamelCase   =						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
             __lowerCamelCase   =						tokenizer.num_special_tokens_to_add(UpperCamelCase_   )
             __lowerCamelCase   =						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
             __lowerCamelCase   =						[""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
             __lowerCamelCase   =						dict(tokenizer(UpperCamelCase_    ,       return_tensors=UpperCamelCase_   )   )
             return common_inputs
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						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":
                    __lowerCamelCase   =						self._generate_dummy_inputs_for_causal_lm(
                        UpperCamelCase_    ,       batch_size=UpperCamelCase_    ,       seq_length=UpperCamelCase_    ,       is_pair=UpperCamelCase_    ,       framework=UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						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: Tuple    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Tuple   ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						super()._flatten_past_key_values_(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						super(UpperCamelCase_    ,       self   )._flatten_past_key_values_(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )







from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int | float]  ,			A__       :					int  ,			A__       :					int	):





       '''simple docstring'''



       if len(A__	) == 0:
              raise ValueError("""find_max() arg is an empty sequence"""	)
       if (
           left >= len(A__	)
           or left < -len(A__	)
           or right >= len(A__	)
           or right < -len(A__	)
       ):
              raise IndexError("""list index out of range"""	)
       if left == right:
              return nums[left]
       __lowerCamelCase   =						(left + right) >> 1  # the middle
       __lowerCamelCase   =						find_max(A__  ,			A__  ,			A__	)  # find max in range[left, mid]
       __lowerCamelCase   =						find_max(A__  ,			mid + 1  ,			A__	)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)

