



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


a			:     Dict     = logging.get_logger(__name__)

a			:     Union[str, Any]     = {
    '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 							_a							(      _lowerCAmelCase				):
			A					=  '''mbart'''
			A					=  ['''past_key_values''']
			A					=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

			def __init__(self,      SCREAMING_SNAKE_CASE_=50265,      SCREAMING_SNAKE_CASE_=1024,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=4096,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=4096,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=1024,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0_2,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=2,      **SCREAMING_SNAKE_CASE_,      )					->      str:
						UpperCAmelCase_:		List[Any]       			=    vocab_size
						UpperCAmelCase_:		List[Any]       			=    max_position_embeddings
						UpperCAmelCase_:		Dict       			=    d_model
						UpperCAmelCase_:		Dict       			=    encoder_ffn_dim
						UpperCAmelCase_:		List[str]       			=    encoder_layers
						UpperCAmelCase_:		List[str]       			=    encoder_attention_heads
						UpperCAmelCase_:		int       			=    decoder_ffn_dim
						UpperCAmelCase_:		Optional[int]       			=    decoder_layers
						UpperCAmelCase_:		Any       			=    decoder_attention_heads
						UpperCAmelCase_:		List[str]       			=    dropout
						UpperCAmelCase_:		Union[str, Any]       			=    attention_dropout
						UpperCAmelCase_:		List[Any]       			=    activation_dropout
						UpperCAmelCase_:		Tuple       			=    activation_function
						UpperCAmelCase_:		Any       			=    init_std
						UpperCAmelCase_:		int       			=    encoder_layerdrop
						UpperCAmelCase_:		Tuple       			=    decoder_layerdrop
						UpperCAmelCase_:		List[Any]       			=    classifier_dropout
						UpperCAmelCase_:		Tuple       			=    use_cache
						UpperCAmelCase_:		Dict       			=    encoder_layers
						UpperCAmelCase_:		str       			=    scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      is_encoder_decoder=SCREAMING_SNAKE_CASE_,      forced_eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )







class 							_a							(      _lowerCAmelCase				):

			@property
			def     __snake_case					(self				)					->      Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase_:		int       			=    OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]				)

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      )					->      Mapping[str, Any]:
						UpperCAmelCase_:		Dict       			=    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
						UpperCAmelCase_:		Dict       			=    seq_length if not self.use_past else 1
						UpperCAmelCase_:		Union[str, Any]       			=    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_				)
						UpperCAmelCase_:		Tuple       			=    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
						UpperCAmelCase_:		str       			=    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
									UpperCAmelCase_:		List[Any]       			=    common_inputs["""input_ids"""].shape
									UpperCAmelCase_:		Union[str, Any]       			=    common_inputs["""decoder_input_ids"""].shape[1]
									UpperCAmelCase_:		Union[str, Any]       			=    self.num_attention_heads
									UpperCAmelCase_:		Dict       			=    (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									UpperCAmelCase_:		List[Any]       			=    decoder_seq_length + 3
									UpperCAmelCase_:		List[Any]       			=    (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									UpperCAmelCase_:		Dict       			=    torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)],      dim=1				)

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

									UpperCAmelCase_:		Dict       			=    common_inputs["""attention_mask"""].dtype
									UpperCAmelCase_:		int       			=    torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      dtype=SCREAMING_SNAKE_CASE_				)],      dim=1				)
									UpperCAmelCase_:		int       			=    [
									    (torch.zeros(SCREAMING_SNAKE_CASE_				), torch.zeros(SCREAMING_SNAKE_CASE_				)) for _ in range(SCREAMING_SNAKE_CASE_				)
									]
						return common_inputs

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      )					->      Mapping[str, Any]:
						# 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
						UpperCAmelCase_:		Optional[int]       			=    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
						UpperCAmelCase_:		Dict       			=    tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		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
						UpperCAmelCase_:		str       			=    [""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
						UpperCAmelCase_:		List[Any]       			=    dict(tokenizer(SCREAMING_SNAKE_CASE_,      return_tensors=SCREAMING_SNAKE_CASE_				)				)
						return common_inputs

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = -1,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      )					->      Mapping[str, Any]:
						if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase_:		List[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":
									UpperCAmelCase_:		Optional[int]       			=    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:
									UpperCAmelCase_:		Optional[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     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Any:
						if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase_:		List[str]       			=    super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
						else:
									UpperCAmelCase_:		Any       			=    super(SCREAMING_SNAKE_CASE_,      self				)._flatten_past_key_values_(
									    SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)











def    lowerCAmelCase_  (lowerCAmelCase__:		str     ):





			"""simple docstring"""


			return "".join(chr(ord(lowerCAmelCase__     ) + 3_2     ) if """A""" <= char <= """Z""" else char for char in word     )


if __name__ == "__main__":
	from doctest import testmod

	testmod()






