






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

UpperCAmelCase_   : Any   			= {
    """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__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "bart"
      __UpperCamelCase	 =  ["past_key_values"]
      __UpperCamelCase	 =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
      def __init__(						self     :  int						,	lowercase_     :  Any=50265						,	lowercase_     :  List[str]=1024						,	lowercase_     :  List[Any]=12						,	lowercase_     :  str=4096						,	lowercase_     :  int=16						,	lowercase_     :  Optional[Any]=12						,	lowercase_     :  List[str]=4096						,	lowercase_     :  Optional[Any]=16						,	lowercase_     :  Optional[int]=0.0						,	lowercase_     :  Union[str, Any]=0.0						,	lowercase_     :  Optional[Any]="gelu"						,	lowercase_     :  int=1024						,	lowercase_     :  Any=0.1						,	lowercase_     :  Dict=0.0						,	lowercase_     :  Any=0.0						,	lowercase_     :  Union[str, Any]=0.02						,	lowercase_     :  Tuple=0.0						,	lowercase_     :  Tuple=False						,	lowercase_     :  str=True						,	lowercase_     :  Optional[int]=3						,	lowercase_     :  Optional[int]=1						,	lowercase_     :  Dict=0						,	lowercase_     :  Tuple=2						,	lowercase_     :  Union[str, Any]=True						,	lowercase_     :  Dict=2						,	lowercase_     :  Dict=2						,	**lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		d_model
          SCREAMING_SNAKE_CASE_      :  int    						=		encoder_ffn_dim
          SCREAMING_SNAKE_CASE_      :  Any    						=		encoder_layers
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		encoder_attention_heads
          SCREAMING_SNAKE_CASE_      :  str    						=		decoder_ffn_dim
          SCREAMING_SNAKE_CASE_      :  str    						=		decoder_layers
          SCREAMING_SNAKE_CASE_      :  int    						=		decoder_attention_heads
          SCREAMING_SNAKE_CASE_      :  Any    						=		dropout
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		attention_dropout
          SCREAMING_SNAKE_CASE_      :  int    						=		activation_dropout
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		activation_function
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		init_std
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		encoder_layerdrop
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		decoder_layerdrop
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		classifier_dropout
          SCREAMING_SNAKE_CASE_      :  str    						=		use_cache
          SCREAMING_SNAKE_CASE_      :  Dict    						=		encoder_layers
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		scale_embedding  # scale factor will be sqrt(d_model) if True

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

          # ensure backward compatibility for BART CNN models
          if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''						,	lowercase_):
              SCREAMING_SNAKE_CASE_      :  str    						=		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__				(			UpperCAmelCase__ ):






      '''simple docstring'''

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




          '''simple docstring'''


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

              if self.use_past:
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{0: '''batch'''}
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
              else:
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		{0: '''batch''', 1: '''decoder_sequence'''}
                  SCREAMING_SNAKE_CASE_      :  Any    						=		{0: '''batch''', 1: '''decoder_sequence'''}

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




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_)

          # Generate decoder inputs
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		seq_length if not self.use_past else 1
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
          SCREAMING_SNAKE_CASE_      :  Dict    						=		dict(**lowercase_						,	**lowercase_)

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

              SCREAMING_SNAKE_CASE_      :  str    						=		torch.cat(
                  [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_						,	lowercase_)]						,	dim=1)

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.num_layers
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		min(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		max(lowercase_						,	lowercase_) - min_num_layers
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_)

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

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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		compute_effective_axis_dimension(
              lowercase_						,	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
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		tokenizer.num_special_tokens_to_add(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		compute_effective_axis_dimension(
              lowercase_						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=lowercase_)

          # Generate dummy inputs according to compute batch and sequence
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
          SCREAMING_SNAKE_CASE_      :  Dict    						=		dict(tokenizer(lowercase_						,	return_tensors=lowercase_))
          return common_inputs
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  PreTrainedTokenizer						,	lowercase_     :  int = -1						,	lowercase_     :  int = -1						,	lowercase_     :  bool = False						,	lowercase_     :  Optional[TensorType] = None						,	):




          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase_						,	batch_size=lowercase_						,	seq_length=lowercase_						,	is_pair=lowercase_						,	framework=lowercase_)

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

          return common_inputs



      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  str						,	lowercase_     :  int						,	lowercase_     :  Tuple						,	lowercase_     :  str):




          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		super()._flatten_past_key_values_(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		super(lowercase_						,	self)._flatten_past_key_values_(
                  lowercase_						,	lowercase_						,	lowercase_						,	lowercase_)










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


