
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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""marian"""
	A__    :   List[str]				       =["""past_key_values"""]
	A__    :   str				       ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
	def __init__(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple=58101       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   Optional[Any]=1024       ,   UpperCAmelCase_							:   Dict=12       ,   UpperCAmelCase_							:   int=4096       ,   UpperCAmelCase_							:   List[Any]=16       ,   UpperCAmelCase_							:   Optional[int]=12       ,   UpperCAmelCase_							:   List[str]=4096       ,   UpperCAmelCase_							:   Tuple=16       ,   UpperCAmelCase_							:   str=0.0       ,   UpperCAmelCase_							:   str=0.0       ,   UpperCAmelCase_							:   int=True       ,   UpperCAmelCase_							:   str=True       ,   UpperCAmelCase_							:   List[Any]="gelu"       ,   UpperCAmelCase_							:   Union[str, Any]=1024       ,   UpperCAmelCase_							:   List[Any]=0.1       ,   UpperCAmelCase_							:   str=0.0       ,   UpperCAmelCase_							:   Dict=0.0       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   List[Any]=58100       ,   UpperCAmelCase_							:   List[Any]=False       ,   UpperCAmelCase_							:   str=58100       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   List[str]=0       ,   UpperCAmelCase_							:   int=True       ,   **UpperCAmelCase_							:   List[str]       ,   ):
						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= decoder_vocab_size or vocab_size
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= d_model
						SCREAMING_SNAKE_CASE__													= encoder_ffn_dim
						SCREAMING_SNAKE_CASE__													= encoder_layers
						SCREAMING_SNAKE_CASE__													= encoder_attention_heads
						SCREAMING_SNAKE_CASE__													= decoder_ffn_dim
						SCREAMING_SNAKE_CASE__													= decoder_layers
						SCREAMING_SNAKE_CASE__													= decoder_attention_heads
						SCREAMING_SNAKE_CASE__													= dropout
						SCREAMING_SNAKE_CASE__													= attention_dropout
						SCREAMING_SNAKE_CASE__													= activation_dropout
						SCREAMING_SNAKE_CASE__													= activation_function
						SCREAMING_SNAKE_CASE__													= init_std
						SCREAMING_SNAKE_CASE__													= encoder_layerdrop
						SCREAMING_SNAKE_CASE__													= decoder_layerdrop
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= encoder_layers
						SCREAMING_SNAKE_CASE__													= scale_embedding  # scale factor will be sqrt(d_model) if True
						SCREAMING_SNAKE_CASE__													= share_encoder_decoder_embeddings
						super().__init__(
						    pad_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   is_encoder_decoder=UpperCAmelCase_       ,   decoder_start_token_id=UpperCAmelCase_       ,   forced_eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def      A_				(			self							:   Tuple							):
						if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__													= OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]							)

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

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_       ,   direction='inputs'							)
						elif self.task == "causal-lm":
											# TODO: figure this case out.
											SCREAMING_SNAKE_CASE__													= OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]							)
											if self.use_past:
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.num_layers
																for i in range(UpperCAmelCase_							):
																					SCREAMING_SNAKE_CASE__													= {0: 'batch', 2: 'past_sequence + sequence'}
																					SCREAMING_SNAKE_CASE__													= {0: 'batch', 2: 'past_sequence + sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= 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							:   Union[str, Any]							):
						if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__													= super().outputs
						else:
											SCREAMING_SNAKE_CASE__													= super(UpperCAmelCase_       ,   self							).outputs
											if self.use_past:
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.num_layers
																for i in range(UpperCAmelCase_							):
																					SCREAMING_SNAKE_CASE__													= {0: 'batch', 2: 'past_sequence + sequence'}
																					SCREAMING_SNAKE_CASE__													= {0: 'batch', 2: 'past_sequence + sequence'}
						return common_outputs
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   PreTrainedTokenizer       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   Optional[TensorType] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._generate_dummy_inputs_for_encoder_and_decoder(
						    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

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

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

											SCREAMING_SNAKE_CASE__													= torch.cat(
											    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase_       ,   UpperCAmelCase_							)]       ,   dim=1							)

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

											for _ in range(UpperCAmelCase_							):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																    )							)
											# TODO: test this.
											SCREAMING_SNAKE_CASE__													= encoder_shape if remaining_side_name == 'encoder' else decoder_shape
											for _ in range(UpperCAmelCase_       ,   UpperCAmelCase_							):
																common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							))							)
						return common_inputs
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   PreTrainedTokenizer       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   Optional[TensorType] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._generate_dummy_inputs_for_encoder_and_decoder(
						    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

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

											SCREAMING_SNAKE_CASE__													= common_inputs['attention_mask'].dtype
											SCREAMING_SNAKE_CASE__													= torch.cat(
											    [common_inputs['attention_mask'], torch.ones(UpperCAmelCase_       ,   UpperCAmelCase_       ,   dtype=UpperCAmelCase_							)]       ,   dim=1							)
											SCREAMING_SNAKE_CASE__													= [
											    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(UpperCAmelCase_							)
											]
						return common_inputs
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   PreTrainedTokenizer       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   Optional[TensorType] = 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
						SCREAMING_SNAKE_CASE__													= compute_effective_axis_dimension(
						    UpperCAmelCase_       ,   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
						SCREAMING_SNAKE_CASE__													= tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= compute_effective_axis_dimension(
						    UpperCAmelCase_       ,   fixed_dimension=OnnxConfig.default_fixed_sequence       ,   num_token_to_add=UpperCAmelCase_							)

						# Generate dummy inputs according to compute batch and sequence
						SCREAMING_SNAKE_CASE__													= [' '.join([tokenizer.unk_token]							) * seq_length] * batch_size
						SCREAMING_SNAKE_CASE__													= dict(tokenizer(UpperCAmelCase_       ,   return_tensors=UpperCAmelCase_							)							)
						return common_inputs
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   PreTrainedTokenizer       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   int = -1       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   Optional[TensorType] = None       ,   ):
						if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__													= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    UpperCAmelCase_       ,   batch_size=UpperCAmelCase_       ,   seq_length=UpperCAmelCase_       ,   is_pair=UpperCAmelCase_       ,   framework=UpperCAmelCase_							)

						else:
											SCREAMING_SNAKE_CASE__													= self._generate_dummy_inputs_for_causal_lm(
											    UpperCAmelCase_       ,   batch_size=UpperCAmelCase_       ,   seq_length=UpperCAmelCase_       ,   is_pair=UpperCAmelCase_       ,   framework=UpperCAmelCase_							)

						return common_inputs
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   List[str]							):
						if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__													= super()._flatten_past_key_values_(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						else:
											SCREAMING_SNAKE_CASE__													= super(UpperCAmelCase_       ,   self							)._flatten_past_key_values_(
											    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)





	@property
	def      A_				(			self							:   List[Any]							):
						return 1e-4







def 							_lowercase							(     )		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= 1

					while len(UpperCamelCase_		) < 1e6:
										constant.append(str(UpperCamelCase_		)		)
										i += 1

					SCREAMING_SNAKE_CASE__													= ''.join(UpperCamelCase_		)

					return (
					    int(constant[0]		)
					    * int(constant[9]		)
					    * int(constant[99]		)
					    * int(constant[999]		)
					    * int(constant[9999]		)
					    * int(constant[99999]		)
					    * int(constant[999999]		)
					)


if __name__ == "__main__":
				print(solution())





