
"""simple docstring"""




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							:  Union[str, Any] 				=		logging.get_logger(__name__)

__lowerCAmelCase							:  str 				=		{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[Any]         =		'mbart'
	snake_case__							:		Any         =		['past_key_values']
	snake_case__							:		int         =		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


	def __init__(  self :List[Any]       ,    __magic_name__ :Optional[int]=50265       ,    __magic_name__ :Union[str, Any]=1024       ,    __magic_name__ :str=12       ,    __magic_name__ :List[Any]=4096       ,    __magic_name__ :Tuple=16       ,    __magic_name__ :Union[str, Any]=12       ,    __magic_name__ :Union[str, Any]=4096       ,    __magic_name__ :Tuple=16       ,    __magic_name__ :Any=0.0       ,    __magic_name__ :Optional[Any]=0.0       ,    __magic_name__ :Optional[Any]=True       ,    __magic_name__ :Optional[int]=True       ,    __magic_name__ :Tuple="gelu"       ,    __magic_name__ :int=1024       ,    __magic_name__ :Any=0.1       ,    __magic_name__ :Any=0.0       ,    __magic_name__ :int=0.0       ,    __magic_name__ :Any=0.02       ,    __magic_name__ :int=0.0       ,    __magic_name__ :List[str]=False       ,    __magic_name__ :int=1       ,    __magic_name__ :List[str]=0       ,    __magic_name__ :List[Any]=2       ,    __magic_name__ :Optional[int]=2       ,    **__magic_name__ :Dict       ,    )			->   Optional[Any]:

			'''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__(
			    pad_token_id=__magic_name__       ,    bos_token_id=__magic_name__       ,    eos_token_id=__magic_name__       ,    is_encoder_decoder=__magic_name__       ,    forced_eos_token_id=__magic_name__       ,    **__magic_name__       ,    )

class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	@property
	def    _UpperCamelCase							(  self :Dict			)			->   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_(__magic_name__       ,    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(__magic_name__			):
									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    _UpperCamelCase							(  self :Tuple			)			->   Mapping[str, Mapping[int, str]]:

			'''simple docstring'''






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


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

			'''simple docstring'''






			a__   		=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__			)

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

			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(__magic_name__       ,    __magic_name__			)]       ,    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(__magic_name__       ,    __magic_name__			)
					a__   		=     max(__magic_name__       ,    __magic_name__			) - min_num_layers
					a__   		=     '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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


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

			'''simple docstring'''






			a__   		=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__			)

			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(__magic_name__       ,    __magic_name__       ,    dtype=__magic_name__			)]       ,    dim=1			)
					a__   		=     [
					    (torch.zeros(__magic_name__			), torch.zeros(__magic_name__			)) for _ in range(__magic_name__			)
					]
			return common_inputs


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

			'''simple docstring'''






			a__   		=     compute_effective_axis_dimension(
			    __magic_name__       ,    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(__magic_name__			)
			a__   		=     compute_effective_axis_dimension(
			    __magic_name__       ,    fixed_dimension=OnnxConfig.default_fixed_sequence       ,    num_token_to_add=__magic_name__			)

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


	def    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :PreTrainedTokenizer       ,    __magic_name__ :int = -1       ,    __magic_name__ :int = -1       ,    __magic_name__ :bool = False       ,    __magic_name__ :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(
					    __magic_name__       ,    batch_size=__magic_name__       ,    seq_length=__magic_name__       ,    is_pair=__magic_name__       ,    framework=__magic_name__			)

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

			return common_inputs






	def    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :Dict       ,    __magic_name__ :Tuple       ,    __magic_name__ :Optional[Any]       ,    __magic_name__ :List[Any]			)			->   Any:

			'''simple docstring'''






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






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




