



'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(
			    """""".join(word[::-1]   ) if len(lowerCamelCase__   ) > 4 else word for word in sentence.split()   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(reverse_long_words('''Hey wollef sroirraw'''))




'''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							:Dict       = logging.get_logger(__name__)

lowerCamelCase							: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			):
	__SCREAMING_SNAKE_CASE       :		List[Any]		      = 'bart'
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ['past_key_values']
	__SCREAMING_SNAKE_CASE       :		Dict		      = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


	def __init__(self	,				lowercase=50265	,				lowercase=1024	,				lowercase=12	,				lowercase=4096	,				lowercase=16	,				lowercase=12	,				lowercase=4096	,				lowercase=16	,				lowercase=0.0	,				lowercase=0.0	,				lowercase="gelu"	,				lowercase=1024	,				lowercase=0.1	,				lowercase=0.0	,				lowercase=0.0	,				lowercase=0.02	,				lowercase=0.0	,				lowercase=False	,				lowercase=True	,				lowercase=3	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase=True	,				lowercase=2	,				lowercase=2	,				**lowercase	,				):
				A_     :						Optional[int]     =					vocab_size
				A_     :						Dict     =					max_position_embeddings
				A_     :						Dict     =					d_model
				A_     :						Any     =					encoder_ffn_dim
				A_     :						Dict     =					encoder_layers
				A_     :						Optional[int]     =					encoder_attention_heads
				A_     :						Tuple     =					decoder_ffn_dim
				A_     :						List[str]     =					decoder_layers
				A_     :						int     =					decoder_attention_heads
				A_     :						Dict     =					dropout
				A_     :						List[str]     =					attention_dropout
				A_     :						int     =					activation_dropout
				A_     :						Dict     =					activation_function
				A_     :						List[Any]     =					init_std
				A_     :						Dict     =					encoder_layerdrop
				A_     :						Tuple     =					decoder_layerdrop
				A_     :						Optional[int]     =					classifier_dropout
				A_     :						Union[str, Any]     =					use_cache
				A_     :						Dict     =					encoder_layers
				A_     :						Dict     =					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							):
							A_     :						List[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			):


	@property
	def    _a     (self							):
				if self.task in ["default", "seq2seq-lm"]:
							A_     :						List[Any]     =					OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
							    ]							)

							if self.use_past:
										A_     :						Union[str, Any]     =					{0: """batch"""}
										A_     :						List[Any]     =					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
							else:
										A_     :						Optional[int]     =					{0: """batch""", 1: """decoder_sequence"""}
										A_     :						List[str]     =					{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.
							A_     :						List[Any]     =					OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
							    ]							)
							if self.use_past:
										A_,   A_     :						Optional[int]     =					self.num_layers
										for i in range(lowercase							):
													A_     :						List[str]     =					{0: """batch""", 2: """past_sequence + sequence"""}
													A_     :						List[str]     =					{0: """batch""", 2: """past_sequence + sequence"""}
				else:
							A_     :						Union[str, 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    _a     (self							):
				if self.task in ["default", "seq2seq-lm"]:
							A_     :						int     =					super().outputs
				else:
							A_     :						int     =					super(lowercase	,				self							).outputs
							if self.use_past:
										A_,   A_     :						Union[str, Any]     =					self.num_layers
										for i in range(lowercase							):
													A_     :						List[str]     =					{0: """batch""", 2: """past_sequence + sequence"""}
													A_     :						Optional[int]     =					{0: """batch""", 2: """past_sequence + sequence"""}
				return common_outputs


	def    _a     (self	,				lowercase	,				lowercase = -1	,				lowercase = -1	,				lowercase = False	,				lowercase = None	,				):
				A_     :						Optional[int]     =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase							)

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

							A_     :						List[str]     =					torch.cat(
							    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase	,				lowercase							)]	,				dim=1							)

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

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


	def    _a     (self	,				lowercase	,				lowercase = -1	,				lowercase = -1	,				lowercase = False	,				lowercase = 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
				A_     :						List[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
				A_     :						Union[str, Any]     =					tokenizer.num_special_tokens_to_add(lowercase							)
				A_     :						List[Any]     =					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
				A_     :						str     =					[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
				A_     :						Tuple     =					dict(tokenizer(lowercase	,				return_tensors=lowercase							)							)
				return common_inputs


	def    _a     (self	,				lowercase	,				lowercase = -1	,				lowercase = -1	,				lowercase = False	,				lowercase = None	,				):
				if self.task in ["default", "seq2seq-lm"]:
							A_     :						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":
							A_     :						List[str]     =					self._generate_dummy_inputs_for_causal_lm(
							    lowercase	,				batch_size=lowercase	,				seq_length=lowercase	,				is_pair=lowercase	,				framework=lowercase							)
				else:
							A_     :						Optional[Any]     =					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    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
				if self.task in ["default", "seq2seq-lm"]:
							A_     :						Optional[Any]     =					super()._flatten_past_key_values_(lowercase	,				lowercase	,				lowercase	,				lowercase							)
				else:
							A_     :						List[Any]     =					super(lowercase	,				self							)._flatten_past_key_values_(
							    lowercase	,				lowercase	,				lowercase	,				lowercase							)