


'''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


_lowercase    :    Tuple			     =  logging.get_logger(__name__)

_lowercase    :    Tuple			     =  {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Union[str, Any]							= "marian"
				__magic_name__       :   List[Any]							= ["past_key_values"]
				__magic_name__       :   List[Any]							= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[int]=58101   ,					lowerCAmelCase    :				Any=None   ,					lowerCAmelCase    :				Optional[int]=1024   ,					lowerCAmelCase    :				Dict=12   ,					lowerCAmelCase    :				Dict=4096   ,					lowerCAmelCase    :				str=16   ,					lowerCAmelCase    :				Optional[Any]=12   ,					lowerCAmelCase    :				Optional[Any]=4096   ,					lowerCAmelCase    :				List[Any]=16   ,					lowerCAmelCase    :				Optional[Any]=0.0   ,					lowerCAmelCase    :				List[Any]=0.0   ,					lowerCAmelCase    :				Dict=True   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				int="gelu"   ,					lowerCAmelCase    :				Union[str, Any]=1024   ,					lowerCAmelCase    :				Union[str, Any]=0.1   ,					lowerCAmelCase    :				Union[str, Any]=0.0   ,					lowerCAmelCase    :				Dict=0.0   ,					lowerCAmelCase    :				Dict=0.02   ,					lowerCAmelCase    :				Optional[Any]=58100   ,					lowerCAmelCase    :				List[str]=False   ,					lowerCAmelCase    :				Any=58100   ,					lowerCAmelCase    :				Dict=0   ,					lowerCAmelCase    :				Optional[Any]=0   ,					lowerCAmelCase    :				Optional[int]=True   ,					**lowerCAmelCase    :				Optional[Any]   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			decoder_vocab_size or vocab_size
										UpperCAmelCase         =			max_position_embeddings
										UpperCAmelCase         =			d_model
										UpperCAmelCase         =			encoder_ffn_dim
										UpperCAmelCase         =			encoder_layers
										UpperCAmelCase         =			encoder_attention_heads
										UpperCAmelCase         =			decoder_ffn_dim
										UpperCAmelCase         =			decoder_layers
										UpperCAmelCase         =			decoder_attention_heads
										UpperCAmelCase         =			dropout
										UpperCAmelCase         =			attention_dropout
										UpperCAmelCase         =			activation_dropout
										UpperCAmelCase         =			activation_function
										UpperCAmelCase         =			init_std
										UpperCAmelCase         =			encoder_layerdrop
										UpperCAmelCase         =			decoder_layerdrop
										UpperCAmelCase         =			use_cache
										UpperCAmelCase         =			encoder_layers
										UpperCAmelCase         =			scale_embedding  # scale factor will be sqrt(d_model) if True
										UpperCAmelCase         =			share_encoder_decoder_embeddings
										super().__init__(
										    pad_token_id=lowerCAmelCase   ,					eos_token_id=lowerCAmelCase   ,					is_encoder_decoder=lowerCAmelCase   ,					decoder_start_token_id=lowerCAmelCase   ,					forced_eos_token_id=lowerCAmelCase   ,					**lowerCAmelCase   ,					)







class  UpperCamelCase__(     lowerCAmelCase ):




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def   a__(	self    :				Optional[int]   )->  Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




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

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

																if self.use_past:
																						self.fill_with_past_key_values_(lowerCAmelCase   ,					direction='''inputs'''   )
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																UpperCAmelCase         =			OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]   )
																if self.use_past:
																						UpperCAmelCase       ,       UpperCAmelCase         =			self.num_layers
																						for i in range(lowerCAmelCase   ):
																												UpperCAmelCase         =			{0: '''batch''', 2: '''past_sequence + sequence'''}
																												UpperCAmelCase         =			{0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																UpperCAmelCase         =			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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def   a__(	self    :				Dict   )->  Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase         =			self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																else:
																						import torch
																UpperCAmelCase       ,       UpperCAmelCase         =			common_inputs['''input_ids'''].shape
																UpperCAmelCase         =			common_inputs['''decoder_input_ids'''].shape[1]
																UpperCAmelCase       ,       UpperCAmelCase         =			self.num_attention_heads
																UpperCAmelCase         =			(
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																UpperCAmelCase         =			decoder_seq_length + 3
																UpperCAmelCase         =			(
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																UpperCAmelCase         =			torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase   ,					lowerCAmelCase   )]   ,					dim=1   )

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

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




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

										"""simple docstring"""




										UpperCAmelCase         =			self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																else:
																						import torch
																UpperCAmelCase       ,       UpperCAmelCase         =			common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																UpperCAmelCase         =			seqlen + 2
																UpperCAmelCase       ,       UpperCAmelCase         =			self.num_layers
																UpperCAmelCase       ,       UpperCAmelCase         =			self.num_attention_heads
																UpperCAmelCase         =			(
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																UpperCAmelCase         =			common_inputs['''attention_mask'''].dtype
																UpperCAmelCase         =			torch.cat(
																    [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase   ,					lowerCAmelCase   ,					dtype=lowerCAmelCase   )]   ,					dim=1   )
																UpperCAmelCase         =			[
																    (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(lowerCAmelCase   )
																]
										return common_inputs




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

										"""simple docstring"""




										UpperCAmelCase         =			compute_effective_axis_dimension(
										    lowerCAmelCase   ,					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         =			tokenizer.num_special_tokens_to_add(lowerCAmelCase   )
										UpperCAmelCase         =			compute_effective_axis_dimension(
										    lowerCAmelCase   ,					fixed_dimension=OnnxConfig.default_fixed_sequence   ,					num_token_to_add=lowerCAmelCase   )

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




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

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase         =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    lowerCAmelCase   ,					batch_size=lowerCAmelCase   ,					seq_length=lowerCAmelCase   ,					is_pair=lowerCAmelCase   ,					framework=lowerCAmelCase   )

										else:
																UpperCAmelCase         =			self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase   ,					batch_size=lowerCAmelCase   ,					seq_length=lowerCAmelCase   ,					is_pair=lowerCAmelCase   ,					framework=lowerCAmelCase   )

										return common_inputs




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

										"""simple docstring"""




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





				@property
				def   a__(	self    :				int   )->  float:

										"""simple docstring"""




										return 1E-4









'''simple docstring'''



def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						assert column_title.isupper()
						UpperCAmelCase         =			0
						UpperCAmelCase         =			len(A  ) - 1
						UpperCAmelCase         =			0

						while index >= 0:
												UpperCAmelCase         =			(ord(column_title[index]  ) - 64) * pow(26   ,     A  )
												answer += value
												power += 1
												index -= 1

						return answer


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

	testmod()





