
"""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_					:						Optional[int]				= logging.get_logger(__name__)

UpperCamelCase_					:						Dict				= {
    '''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				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"bart"
			snake_case	      =				["past_key_values"]
			snake_case	      =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Optional[Any]=50_265       ,   _snake_case						:					Optional[int]=1_024       ,   _snake_case						:					List[Any]=12       ,   _snake_case						:					Optional[Any]=4_096       ,   _snake_case						:					Dict=16       ,   _snake_case						:					Tuple=12       ,   _snake_case						:					Dict=4_096       ,   _snake_case						:					Tuple=16       ,   _snake_case						:					Union[str, Any]=0.0       ,   _snake_case						:					Optional[int]=0.0       ,   _snake_case						:					str="gelu"       ,   _snake_case						:					Union[str, Any]=1_024       ,   _snake_case						:					Tuple=0.1       ,   _snake_case						:					Any=0.0       ,   _snake_case						:					Union[str, Any]=0.0       ,   _snake_case						:					Optional[Any]=0.0_2       ,   _snake_case						:					Optional[Any]=0.0       ,   _snake_case						:					Union[str, Any]=False       ,   _snake_case						:					int=True       ,   _snake_case						:					List[Any]=3       ,   _snake_case						:					Dict=1       ,   _snake_case						:					Any=0       ,   _snake_case						:					Any=2       ,   _snake_case						:					int=True       ,   _snake_case						:					Any=2       ,   _snake_case						:					int=2       ,   **_snake_case						:					Union[str, Any]       ,   )      -> int:







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

								super().__init__(
								    num_labels=_snake_case       ,   pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case       ,   is_encoder_decoder=_snake_case       ,   decoder_start_token_id=_snake_case       ,   forced_eos_token_id=_snake_case       ,   **_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"       ,   _snake_case	):
													A_				 =						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				(					_lowercase      ):




			"""simple docstring"""







			@property
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Mapping[str, Mapping[int, str]]:







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

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

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







								"""simple docstring"""
								if self.task in ["default", "seq2seq-lm"]:
													A_				 =						super().outputs
								else:
													A_				 =						super(_snake_case       ,   self	).outputs
													if self.use_past:
																		A_ ,    A_				 =						self.num_layers
																		for i in range(_snake_case	):
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
								return common_outputs




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)

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

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

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

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




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   _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
													A_ ,    A_				 =						common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													A_				 =						seqlen + 2
													A_ ,    A_				 =						self.num_layers
													A_ ,    A_				 =						self.num_attention_heads
													A_				 =						(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													A_				 =						common_inputs["attention_mask"].dtype
													A_				 =						torch.cat(
													    [common_inputs["attention_mask"], torch.ones(_snake_case       ,   _snake_case       ,   dtype=_snake_case	)]       ,   dim=1	)
													A_				 =						[
													    (torch.zeros(_snake_case	), torch.zeros(_snake_case	)) for _ in range(_snake_case	)
													]
								return common_inputs




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								# 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_				 =						compute_effective_axis_dimension(
								    _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
								A_				 =						tokenizer.num_special_tokens_to_add(_snake_case	)
								A_				 =						compute_effective_axis_dimension(
								    _snake_case       ,   fixed_dimension=OnnxConfig.default_fixed_sequence       ,   num_token_to_add=_snake_case	)

								# Generate dummy inputs according to compute batch and sequence
								A_				 =						[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
								A_				 =						dict(tokenizer(_snake_case       ,   return_tensors=_snake_case	)	)
								return common_inputs




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







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

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

								return common_inputs






			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Tuple       ,   _snake_case						:					Any       ,   _snake_case						:					Union[str, Any]	)      -> List[str]:







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






"""simple docstring"""





import torch
def 							A_     ():

					'''simple docstring'''





					if torch.cuda.is_available():
										A_				 =						torch.cuda.device_count()
					else:
										A_				 =						0
					print(f'Successfully ran on {num_gpus} GPUs'				)


if __name__ == "__main__":
		main()




