



'''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				:    Optional[int]     						=logging.get_logger(__name__)

_A				:    Optional[int]     						={
    '''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 		lowerCamelCase__      (						A     ):




	'''simple docstring'''



	A_						    =						"""marian"""
	A_						    =						["""past_key_values"""]
	A_						    =						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__(  self				:	Any     ,  UpperCamelCase_				:	Union[str, Any]=5_8101     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	str=1024     ,  UpperCamelCase_				:	Optional[int]=12     ,  UpperCamelCase_				:	str=4096     ,  UpperCamelCase_				:	Optional[int]=16     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	Union[str, Any]=4096     ,  UpperCamelCase_				:	Tuple=16     ,  UpperCamelCase_				:	Any=0.0     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	Tuple=True     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	str="gelu"     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.0     ,  UpperCamelCase_				:	List[str]=0.0     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	str=5_8100     ,  UpperCamelCase_				:	List[str]=False     ,  UpperCamelCase_				:	Union[str, Any]=5_8100     ,  UpperCamelCase_				:	int=0     ,  UpperCamelCase_				:	str=0     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[str]:



		'''simple docstring'''





		_lowercase					:       List[Any]       =		vocab_size
		_lowercase					:       Tuple       =		decoder_vocab_size or vocab_size
		_lowercase					:       Optional[int]       =		max_position_embeddings
		_lowercase					:       Tuple       =		d_model
		_lowercase					:       Optional[Any]       =		encoder_ffn_dim
		_lowercase					:       Optional[Any]       =		encoder_layers
		_lowercase					:       List[Any]       =		encoder_attention_heads
		_lowercase					:       int       =		decoder_ffn_dim
		_lowercase					:       List[Any]       =		decoder_layers
		_lowercase					:       List[Any]       =		decoder_attention_heads
		_lowercase					:       Tuple       =		dropout
		_lowercase					:       int       =		attention_dropout
		_lowercase					:       str       =		activation_dropout
		_lowercase					:       int       =		activation_function
		_lowercase					:       Any       =		init_std
		_lowercase					:       Dict       =		encoder_layerdrop
		_lowercase					:       Optional[Any]       =		decoder_layerdrop
		_lowercase					:       List[Any]       =		use_cache
		_lowercase					:       List[str]       =		encoder_layers
		_lowercase					:       int       =		scale_embedding  # scale factor will be sqrt(d_model) if True
		_lowercase					:       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 		lowerCamelCase__      (						A     ):




	'''simple docstring'''



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



		'''simple docstring'''





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

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



		'''simple docstring'''





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




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



		'''simple docstring'''





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

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

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

			_lowercase					:       Optional[Any]       =		[]
			# If the number of encoder and decoder layers are present in the model configuration, both are considered
			_lowercase					:       Dict       =		self.num_layers
			_lowercase					:       Dict       =		min(UpperCamelCase_     ,  UpperCamelCase_					)
			_lowercase					:       int       =		max(UpperCamelCase_     ,  UpperCamelCase_					) - min_num_layers
			_lowercase					:       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.
			_lowercase					:       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				:	Dict     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	int = -1     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	Optional[TensorType] = None     ,  )  ->       Mapping[str, Any]:



		'''simple docstring'''





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

			_lowercase					:       Union[str, Any]       =		common_inputs['attention_mask'].dtype
			_lowercase					:       Union[str, Any]       =		torch.cat(
			    [common_inputs['attention_mask'], torch.ones(UpperCamelCase_     ,  UpperCamelCase_     ,  dtype=UpperCamelCase_					)]     ,  dim=1					)
			_lowercase					:       Optional[Any]       =		[
			    (torch.zeros(UpperCamelCase_					), torch.zeros(UpperCamelCase_					)) for _ in range(UpperCamelCase_					)
			]
		return common_inputs




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



		'''simple docstring'''





		_lowercase					:       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
		_lowercase					:       int       =		tokenizer.num_special_tokens_to_add(UpperCamelCase_					)
		_lowercase					:       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
		_lowercase					:       Optional[Any]       =		[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
		_lowercase					:       List[str]       =		dict(tokenizer(UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)					)
		return common_inputs




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



		'''simple docstring'''





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

		else:
			_lowercase					:       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				:	List[str]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str					)  ->       Tuple:



		'''simple docstring'''





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




	@property
	def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       float:



		'''simple docstring'''





		return 1E-4





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			list:
	_lowercase					:       List[str]       =		word.split()

	def justify(_lowercase,				_lowercase,				_lowercase					) -> str:
		_lowercase					:       Dict       =		max_width - width
		_lowercase					:       Tuple       =		len(_lowercase					)
		if len(_lowercase					) == 1:
			# if there is only word in line
			# just insert overall_spaces_count for the remainder of line
			return line[0] + " " * overall_spaces_count
		else:
			_lowercase					:       Tuple       =		words_count - 1
			# num_spaces_between_words_list[i] : tells you to insert
			# num_spaces_between_words_list[i] spaces
			# after word on line[i]
			_lowercase					:       str       =		spaces_to_insert_between_words * [
			    overall_spaces_count // spaces_to_insert_between_words
			]
			_lowercase					:       Optional[int]       =		(
			    overall_spaces_count % spaces_to_insert_between_words
			)
			# distribute spaces via round robin to the left words
			for i in range(_lowercase					):
				num_spaces_between_words_list[i] += 1
			_lowercase					:       Union[str, Any]       =		[]
			for i in range(_lowercase					):
				# add the word
				aligned_words_list.append(line[i]					)
				# add the spaces to insert
				aligned_words_list.append(num_spaces_between_words_list[i] * ' '					)
			# just add the last word to the sentence
			aligned_words_list.append(line[-1]					)
			# join the aligned words list to form a justified line
			return "".join(_lowercase					)

	_lowercase					:       str       =		[]
	_lowercase					:       list[str]       =		[]
	_lowercase					:       Union[str, Any]       =		0
	for word in words:
		if width + len(_lowercase					) + len(_lowercase					) <= max_width:
			# keep adding words until we can fill out max_width
			# width = sum of length of all words (without overall_spaces_count)
			# len(word) = length of current word
			# len(line) = number of overall_spaces_count to insert between words
			line.append(_lowercase					)
			width += len(_lowercase					)
		else:
			# justify the line and add it to result
			answer.append(justify(_lowercase,				_lowercase,				_lowercase					)					)
			# reset new line and new width
			_lowercase  ,    _lowercase					:       Optional[Any]       =		[word], len(_lowercase					)
	_lowercase					:       Optional[int]       =		max_width - width - len(_lowercase					)
	answer.append(' '.join(_lowercase					) + (remaining_spaces + 1) * ' '					)
	return answer


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

		testmod()
