
'''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


A			      =      logging.get_logger(__name__)

A			      =      {
    '''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     a_		(   a__):
					_lowerCAmelCase			     =	'''marian'''
					_lowerCAmelCase			     =	['''past_key_values''']
					_lowerCAmelCase			     =	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
					def __init__(     self,    A=5_8101,    A=None,    A=1024,    A=12,    A=4096,    A=16,    A=12,    A=4096,    A=16,    A=0.0,    A=0.0,    A=True,    A=True,    A="gelu",    A=1024,    A=0.1,    A=0.0,    A=0.0,    A=0.02,    A=5_8100,    A=False,    A=5_8100,    A=0,    A=0,    A=True,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= vocab_size
									lowerCamelCase					:			Tuple									= decoder_vocab_size or vocab_size
									lowerCamelCase					:			Optional[int]									= max_position_embeddings
									lowerCamelCase					:			Tuple									= d_model
									lowerCamelCase					:			Optional[Any]									= encoder_ffn_dim
									lowerCamelCase					:			Optional[Any]									= encoder_layers
									lowerCamelCase					:			List[Any]									= encoder_attention_heads
									lowerCamelCase					:			int									= decoder_ffn_dim
									lowerCamelCase					:			List[Any]									= decoder_layers
									lowerCamelCase					:			List[Any]									= decoder_attention_heads
									lowerCamelCase					:			Tuple									= dropout
									lowerCamelCase					:			int									= attention_dropout
									lowerCamelCase					:			str									= activation_dropout
									lowerCamelCase					:			int									= activation_function
									lowerCamelCase					:			Any									= init_std
									lowerCamelCase					:			Dict									= encoder_layerdrop
									lowerCamelCase					:			Optional[Any]									= decoder_layerdrop
									lowerCamelCase					:			List[Any]									= use_cache
									lowerCamelCase					:			List[str]									= encoder_layers
									lowerCamelCase					:			int									= scale_embedding  # scale factor will be sqrt(d_model) if True
									lowerCamelCase					:			int									= 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     a_		(   a__):
					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

													if self.use_past:
																	lowerCamelCase					:			Tuple									= {0: 'batch'}
																	lowerCamelCase					:			List[str]									= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	lowerCamelCase					:			Tuple									= {0: 'batch', 1: 'decoder_sequence'}
																	lowerCamelCase					:			int									= {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.
													lowerCamelCase					:			Union[str, Any]									= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]      )
													if self.use_past:
																	lowerCamelCase					:			List[Any]									= self.num_layers
																	for i in range(UpperCamelCase_      ):
																					lowerCamelCase					:			str									= {0: 'batch', 2: 'past_sequence + sequence'}
																					lowerCamelCase					:			Dict									= {0: 'batch', 2: 'past_sequence + sequence'}
									else:
													lowerCamelCase					:			Optional[Any]									= 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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase					:			List[str]									= super().outputs
									else:
													lowerCamelCase					:			int									= super(UpperCamelCase_,    self      ).outputs
													if self.use_past:
																	lowerCamelCase					:			str									= self.num_layers
																	for i in range(UpperCamelCase_      ):
																					lowerCamelCase					:			Any									= {0: 'batch', 2: 'past_sequence + sequence'}
																					lowerCamelCase					:			Any									= {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
					def     UpperCAmelCase_							(     self,    A,    A = -1,    A = -1,    A = False,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self._generate_dummy_inputs_for_encoder_and_decoder(
									    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )

									# Generate decoder inputs
									lowerCamelCase					:			Any									= seq_length if not self.use_past else 1
									lowerCamelCase					:			List[str]									= self._generate_dummy_inputs_for_encoder_and_decoder(
									    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )
									lowerCamelCase					:			Optional[int]									= {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									lowerCamelCase					:			str									= 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
													lowerCamelCase					:			Optional[Any]									= common_inputs['input_ids'].shape
													lowerCamelCase					:			List[str]									= common_inputs['decoder_input_ids'].shape[1]
													lowerCamelCase					:			Tuple									= self.num_attention_heads
													lowerCamelCase					:			List[str]									= (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													lowerCamelCase					:			List[Any]									= decoder_seq_length + 3
													lowerCamelCase					:			Optional[Any]									= (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													lowerCamelCase					:			Any									= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_,    UpperCamelCase_      )],    dim=1      )

													lowerCamelCase					:			Optional[Any]									= []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													lowerCamelCase					:			Dict									= self.num_layers
													lowerCamelCase					:			Dict									= min(UpperCamelCase_,    UpperCamelCase_      )
													lowerCamelCase					:			int									= max(UpperCamelCase_,    UpperCamelCase_      ) - min_num_layers
													lowerCamelCase					:			Union[str, Any]									= '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.
													lowerCamelCase					:			Dict									= 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     UpperCAmelCase_							(     self,    A,    A = -1,    A = -1,    A = False,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			int									= 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
													lowerCamelCase					:			Any									= common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													lowerCamelCase					:			Any									= seqlen + 2
													lowerCamelCase					:			Union[str, Any]									= self.num_layers
													lowerCamelCase					:			List[Any]									= self.num_attention_heads
													lowerCamelCase					:			Dict									= (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													lowerCamelCase					:			Union[str, Any]									= common_inputs['attention_mask'].dtype
													lowerCamelCase					:			Union[str, Any]									= torch.cat(
													    [common_inputs['attention_mask'], torch.ones(UpperCamelCase_,    UpperCamelCase_,    dtype=UpperCamelCase_      )],    dim=1      )
													lowerCamelCase					:			Optional[Any]									= [
													    (torch.zeros(UpperCamelCase_      ), torch.zeros(UpperCamelCase_      )) for _ in range(UpperCamelCase_      )
													]
									return common_inputs
					def     UpperCAmelCase_							(     self,    A,    A = -1,    A = -1,    A = False,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= 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
									lowerCamelCase					:			int									= tokenizer.num_special_tokens_to_add(UpperCamelCase_      )
									lowerCamelCase					:			int									= 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
									lowerCamelCase					:			Optional[Any]									= [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
									lowerCamelCase					:			List[str]									= dict(tokenizer(UpperCamelCase_,    return_tensors=UpperCamelCase_      )      )
									return common_inputs
					def     UpperCAmelCase_							(     self,    A,    A = -1,    A = -1,    A = False,    A = None,    ):

									"""simple docstring"""


									if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase					:			Optional[Any]									= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    UpperCamelCase_,    batch_size=UpperCamelCase_,    seq_length=UpperCamelCase_,    is_pair=UpperCamelCase_,    framework=UpperCamelCase_      )

									else:
													lowerCamelCase					:			Dict									= self._generate_dummy_inputs_for_causal_lm(
													    UpperCamelCase_,    batch_size=UpperCamelCase_,    seq_length=UpperCamelCase_,    is_pair=UpperCamelCase_,    framework=UpperCamelCase_      )

									return common_inputs
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase					:			Tuple									= super()._flatten_past_key_values_(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )
									else:
													lowerCamelCase					:			int									= super(UpperCamelCase_,    self      )._flatten_past_key_values_(
													    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4


'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							list[int]):
				if not nums:  # Makes sure that the list is not empty
								raise ValueError('List is empty')

				lowerCamelCase					:			int									= sum(UpperCAmelCase__) / len(UpperCAmelCase__)  # Calculate the average
				return sum(abs(x - average) for x in nums) / len(UpperCAmelCase__)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
