
"""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


lowerCamelCase__   					=		logging.get_logger(__name__)

lowerCamelCase__   					=		{
    """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  A__       (	_lowerCamelCase):
		A_						:       Dict				   =	'bart'
		A_						:       Dict				   =	['past_key_values']
		A_						:       Union[str, Any]				   =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

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

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

			# ensure backward compatibility for BART CNN models
			if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Any									=      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  A__       (	_lowerCamelCase):

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

				if self.use_past:
					__lowerCAmelCase			:						Dict									=      {0: 'batch'}
					__lowerCAmelCase			:						Optional[Any]									=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
				else:
					__lowerCAmelCase			:						List[str]									=      {0: 'batch', 1: 'decoder_sequence'}
					__lowerCAmelCase			:						str									=      {0: 'batch', 1: 'decoder_sequence'}

				if self.use_past:
					self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       ,		direction='inputs' )
			elif self.task == "causal-lm":
				# TODO: figure this case out.
				__lowerCAmelCase			:						int									=      OrderedDict(
				    [
				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
				    ] )
				if self.use_past:
					__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self.num_layers
					for i in range(_SCREAMING_SNAKE_CASE ):
						__lowerCAmelCase			:						Dict									=      {0: 'batch', 2: 'past_sequence + sequence'}
						__lowerCAmelCase			:						Tuple									=      {0: 'batch', 2: 'past_sequence + sequence'}
			else:
				__lowerCAmelCase			:						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 							__lowerCamelCase							(      self ):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase			:						List[str]									=      super().outputs
			else:
				__lowerCAmelCase			:						Optional[Any]									=      super(_SCREAMING_SNAKE_CASE       ,		self ).outputs
				if self.use_past:
					__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.num_layers
					for i in range(_SCREAMING_SNAKE_CASE ):
						__lowerCAmelCase			:						List[Any]									=      {0: 'batch', 2: 'past_sequence + sequence'}
						__lowerCAmelCase			:						List[str]									=      {0: 'batch', 2: 'past_sequence + sequence'}
			return common_outputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						str									=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			# Generate decoder inputs
			__lowerCAmelCase			:						Optional[int]									=      seq_length if not self.use_past else 1
			__lowerCAmelCase			:						Optional[int]									=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
			__lowerCAmelCase			:						List[Any]									=      dict(**_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			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			:						Dict									=      common_inputs['input_ids'].shape
				__lowerCAmelCase			:						Optional[int]									=      common_inputs['decoder_input_ids'].shape[1]
				__lowerCAmelCase			,   __lowerCAmelCase			:						List[Any]									=      self.num_attention_heads
				__lowerCAmelCase			:						Optional[Any]									=      (
				    batch,
				    num_encoder_attention_heads,
				    encoder_seq_length,
				    self._config.hidden_size // num_encoder_attention_heads,
				)
				__lowerCAmelCase			:						Any									=      decoder_seq_length + 3
				__lowerCAmelCase			:						List[str]									=      (
				    batch,
				    num_decoder_attention_heads,
				    decoder_past_length,
				    self._config.hidden_size // num_decoder_attention_heads,
				)

				__lowerCAmelCase			:						Any									=      torch.cat(
				    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )]       ,		dim=1 )

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

				for _ in range(_SCREAMING_SNAKE_CASE ):
					common_inputs["past_key_values"].append(
					    (
					        torch.zeros(_SCREAMING_SNAKE_CASE ),
					        torch.zeros(_SCREAMING_SNAKE_CASE ),
					        torch.zeros(_SCREAMING_SNAKE_CASE ),
					        torch.zeros(_SCREAMING_SNAKE_CASE ),
					    ) )
				# TODO: test this.
				__lowerCAmelCase			:						int									=      encoder_shape if remaining_side_name == 'encoder' else decoder_shape
				for _ in range(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) )
			return common_inputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						Tuple									=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			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			:						List[str]									=      common_inputs['input_ids'].shape
				# Not using the same length for past_key_values
				__lowerCAmelCase			:						int									=      seqlen + 2
				__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      self.num_layers
				__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.num_attention_heads
				__lowerCAmelCase			:						Optional[Any]									=      (
				    batch,
				    num_encoder_attention_heads,
				    past_key_values_length,
				    self._config.hidden_size // num_encoder_attention_heads,
				)

				__lowerCAmelCase			:						List[str]									=      common_inputs['attention_mask'].dtype
				__lowerCAmelCase			:						Any									=      torch.cat(
				    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		dtype=_SCREAMING_SNAKE_CASE )]       ,		dim=1 )
				__lowerCAmelCase			:						int									=      [
				    (torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) for _ in range(_SCREAMING_SNAKE_CASE )
				]
			return common_inputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = 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			:						Tuple									=      compute_effective_axis_dimension(
			    _SCREAMING_SNAKE_CASE       ,		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			:						List[Any]									=      tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      compute_effective_axis_dimension(
			    _SCREAMING_SNAKE_CASE       ,		fixed_dimension=OnnxConfig.default_fixed_sequence       ,		num_token_to_add=_SCREAMING_SNAKE_CASE )

			# Generate dummy inputs according to compute batch and sequence
			__lowerCAmelCase			:						int									=      [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
			__lowerCAmelCase			:						List[str]									=      dict(tokenizer(_SCREAMING_SNAKE_CASE       ,		return_tensors=_SCREAMING_SNAKE_CASE ) )
			return common_inputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = -1       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase			:						Union[str, Any]									=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
				    _SCREAMING_SNAKE_CASE       ,		batch_size=_SCREAMING_SNAKE_CASE       ,		seq_length=_SCREAMING_SNAKE_CASE       ,		is_pair=_SCREAMING_SNAKE_CASE       ,		framework=_SCREAMING_SNAKE_CASE )

			elif self.task == "causal-lm":
				__lowerCAmelCase			:						List[Any]									=      self._generate_dummy_inputs_for_causal_lm(
				    _SCREAMING_SNAKE_CASE       ,		batch_size=_SCREAMING_SNAKE_CASE       ,		seq_length=_SCREAMING_SNAKE_CASE       ,		is_pair=_SCREAMING_SNAKE_CASE       ,		framework=_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						int									=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _SCREAMING_SNAKE_CASE       ,		batch_size=_SCREAMING_SNAKE_CASE       ,		seq_length=_SCREAMING_SNAKE_CASE       ,		is_pair=_SCREAMING_SNAKE_CASE       ,		framework=_SCREAMING_SNAKE_CASE )

			return common_inputs







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase			:						Tuple									=      super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						Union[str, Any]									=      super(_SCREAMING_SNAKE_CASE       ,		self )._flatten_past_key_values_(
				    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )




"""simple docstring"""







import random

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float    ,   lowerCAmelCase__      :				bool = False						)						->   dict:

  """simple docstring"""




  lowerCAmelCase_   :				dict								= {i: [] for i in range(lowerCAmelCase__						)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowerCAmelCase__						)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowerCAmelCase__						):
    for j in range(i + 1    ,   lowerCAmelCase__						):
      if random.random() < probability:
        graph[i].append(lowerCAmelCase__						)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowerCAmelCase__						)
  return graph

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   dict:

  """simple docstring"""




  return {
      i: [j for j in range(lowerCAmelCase__						) if i != j] for i in range(lowerCAmelCase__						)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()





