


"""simple docstring"""

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__    (		_a     ):
      a			:  str           =		"""bart"""
      a			:  Any           =		["""past_key_values"""]
      a			:  int           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
      def __init__(		self						:						Tuple   ,							A_						:						Tuple=5_0_2_6_5   ,							A_						:						Tuple=1_0_2_4   ,							A_						:						Optional[Any]=1_2   ,							A_						:						List[str]=4_0_9_6   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						Any=1_2   ,							A_						:						Union[str, Any]=4_0_9_6   ,							A_						:						str=1_6   ,							A_						:						List[Any]=0.0   ,							A_						:						Any=0.0   ,							A_						:						Dict="gelu"   ,							A_						:						List[Any]=1_0_2_4   ,							A_						:						int=0.1   ,							A_						:						List[str]=0.0   ,							A_						:						List[Any]=0.0   ,							A_						:						Optional[Any]=0.02   ,							A_						:						Tuple=0.0   ,							A_						:						List[Any]=False   ,							A_						:						Optional[Any]=True   ,							A_						:						Union[str, Any]=3   ,							A_						:						List[Any]=1   ,							A_						:						int=0   ,							A_						:						Any=2   ,							A_						:						Tuple=True   ,							A_						:						List[Any]=2   ,							A_						:						Optional[int]=2   ,							**A_						:						Optional[int]   ,							):






       '''simple docstring'''


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

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

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""   ,							A_				):
        __lowercase						=						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__    (		_a     ):
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


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

        if self.use_past:
         __lowercase						=						{0: """batch"""}
         __lowercase						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
         __lowercase						=						{0: """batch""", 1: """decoder_sequence"""}
         __lowercase						=						{0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
         self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowercase						=						OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]				)
        if self.use_past:
         __lowercase				,		__lowercase						=						self.num_layers
         for i in range(A_				):
          __lowercase						=						{0: """batch""", 2: """past_sequence + sequence"""}
          __lowercase						=						{0: """batch""", 2: """past_sequence + sequence"""}
       else:
        __lowercase						=						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						:						List[str]				):






       '''simple docstring'''


       if self.task in ["default", "seq2seq-lm"]:
        __lowercase						=						super().outputs
       else:
        __lowercase						=						super(A_   ,							self				).outputs
        if self.use_past:
         __lowercase				,		__lowercase						=						self.num_layers
         for i in range(A_				):
          __lowercase						=						{0: """batch""", 2: """past_sequence + sequence"""}
          __lowercase						=						{0: """batch""", 2: """past_sequence + sequence"""}
       return common_outputs
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_   ,							A_   ,							A_   ,							A_   ,							A_				)

       # Generate decoder inputs
       __lowercase						=						seq_length if not self.use_past else 1
       __lowercase						=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_   ,							A_   ,							A_   ,							A_   ,							A_				)
       __lowercase						=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       __lowercase						=						dict(**A_   ,							**A_				)

       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch
        __lowercase				,		__lowercase						=						common_inputs["""input_ids"""].shape
        __lowercase						=						common_inputs["""decoder_input_ids"""].shape[1]
        __lowercase				,		__lowercase						=						self.num_attention_heads
        __lowercase						=						(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowercase						=						decoder_seq_length + 3
        __lowercase						=						(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowercase						=						torch.cat(
            [common_inputs["""decoder_attention_mask"""], torch.ones(A_   ,							A_				)]   ,							dim=1				)

        __lowercase						=						[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowercase				,		__lowercase						=						self.num_layers
        __lowercase						=						min(A_   ,							A_				)
        __lowercase						=						max(A_   ,							A_				) - min_num_layers
        __lowercase						=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

        for _ in range(A_				):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_				),
                 torch.zeros(A_				),
                 torch.zeros(A_				),
                 torch.zeros(A_				),
             )				)
        # TODO: test this.
        __lowercase						=						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
        for _ in range(A_   ,							A_				):
         common_inputs["past_key_values"].append((torch.zeros(A_				), torch.zeros(A_				))				)
       return common_inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_   ,							A_   ,							A_   ,							A_   ,							A_				)

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

        __lowercase						=						common_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)
        __lowercase						=						[
            (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(A_				)
        ]
       return common_inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						compute_effective_axis_dimension(
           A_   ,							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
       __lowercase						=						tokenizer.num_special_tokens_to_add(A_				)
       __lowercase						=						compute_effective_axis_dimension(
           A_   ,							fixed_dimension=OnnxConfig.default_fixed_sequence   ,							num_token_to_add=A_				)

       # Generate dummy inputs according to compute batch and sequence
       __lowercase						=						[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
       __lowercase						=						dict(tokenizer(A_   ,							return_tensors=A_				)				)
       return common_inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       if self.task in ["default", "seq2seq-lm"]:
        __lowercase						=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       elif self.task == "causal-lm":
        __lowercase						=						self._generate_dummy_inputs_for_causal_lm(
            A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)
       else:
        __lowercase						=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       return common_inputs






      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Tuple   ,							A_						:						Dict   ,							A_						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


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




"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			int			):






 """simple docstring"""







 assert (
     isinstance(UpperCamelCase__					, UpperCamelCase__			) and number_of_steps > 0
 ), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
 if number_of_steps == 1:
  return 1
 __lowercase				,		__lowercase						=						1, 1
 for _ in range(number_of_steps - 1			):
  __lowercase				,		__lowercase						=						current + previous, current
 return current


if __name__ == "__main__":
       import doctest

       doctest.testmod()
