



"""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


__UpperCAmelCase   :   int        =       logging.get_logger(__name__)

__UpperCAmelCase   :   Dict        =       {
    '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				(snake_case__	):


					'''simple docstring'''


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







					def __init__(   self     ,							a=5_81_01     ,							a=None     ,							a=10_24     ,							a=12     ,							a=40_96     ,							a=16     ,							a=12     ,							a=40_96     ,							a=16     ,							a=0.0     ,							a=0.0     ,							a=True     ,							a=True     ,							a="gelu"     ,							a=10_24     ,							a=0.1     ,							a=0.0     ,							a=0.0     ,							a=0.02     ,							a=5_81_00     ,							a=False     ,							a=5_81_00     ,							a=0     ,							a=0     ,							a=True     ,							**a     ,							):




										"""simple docstring"""





										snake_case_    :List[Any]										=    vocab_size
										snake_case_    :Optional[int]										=    decoder_vocab_size or vocab_size
										snake_case_    :Tuple										=    max_position_embeddings
										snake_case_    :List[Any]										=    d_model
										snake_case_    :Any										=    encoder_ffn_dim
										snake_case_    :str										=    encoder_layers
										snake_case_    :List[str]										=    encoder_attention_heads
										snake_case_    :str										=    decoder_ffn_dim
										snake_case_    :Tuple										=    decoder_layers
										snake_case_    :Any										=    decoder_attention_heads
										snake_case_    :List[Any]										=    dropout
										snake_case_    :Any										=    attention_dropout
										snake_case_    :Union[str, Any]										=    activation_dropout
										snake_case_    :Optional[int]										=    activation_function
										snake_case_    :Dict										=    init_std
										snake_case_    :List[Any]										=    encoder_layerdrop
										snake_case_    :Optional[Any]										=    decoder_layerdrop
										snake_case_    :Any										=    use_cache
										snake_case_    :Any										=    encoder_layers
										snake_case_    :Optional[int]										=    scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case_    :int										=    share_encoder_decoder_embeddings
										super().__init__(
										    pad_token_id=__lowercase     ,							eos_token_id=__lowercase     ,							is_encoder_decoder=__lowercase     ,							decoder_start_token_id=__lowercase     ,							forced_eos_token_id=__lowercase     ,							**__lowercase     ,							)



class 						__lowerCAmelCase				(snake_case__	):


					'''simple docstring'''


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 		_a			(   self ):




										"""simple docstring"""





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

															if self.use_past:
																				snake_case_    :Optional[Any]										=    {0: '''batch'''}
																				snake_case_    :Any										=    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
															else:
																				snake_case_    :Optional[Any]										=    {0: '''batch''', 1: '''decoder_sequence'''}
																				snake_case_    :List[Any]										=    {0: '''batch''', 1: '''decoder_sequence'''}

															if self.use_past:
																				self.fill_with_past_key_values_(__lowercase     ,							direction="inputs" )
										elif self.task == "causal-lm":
															# TODO: figure this case out.
															snake_case_    :Any										=    OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ] )
															if self.use_past:
																				snake_case_    :str										=    self.num_layers
																				for i in range(__lowercase ):
																									snake_case_    :Optional[int]										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
																									snake_case_    :List[str]										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
															snake_case_    :List[str]										=    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 ):




										"""simple docstring"""





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







					def 		_a			(   self     ,							a     ,							a = -1     ,							a = -1     ,							a = False     ,							a = None     ,							):




										"""simple docstring"""





										snake_case_    :List[str]										=    self._generate_dummy_inputs_for_encoder_and_decoder(
										    __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase )

										# Generate decoder inputs
										snake_case_    :Optional[Any]										=    seq_length if not self.use_past else 1
										snake_case_    :List[Any]										=    self._generate_dummy_inputs_for_encoder_and_decoder(
										    __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase )
										snake_case_    :Dict										=    {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										snake_case_    :str										=    dict(**__lowercase     ,							**__lowercase )

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

															snake_case_    :Optional[int]										=    torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(__lowercase     ,							__lowercase )]     ,							dim=1 )

															snake_case_    :Any										=    []
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															snake_case_    :str										=    self.num_layers
															snake_case_    :List[Any]										=    min(__lowercase     ,							__lowercase )
															snake_case_    :int										=    max(__lowercase     ,							__lowercase ) - min_num_layers
															snake_case_    :Any										=    '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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







					def 		_a			(   self     ,							a     ,							a = -1     ,							a = -1     ,							a = False     ,							a = None     ,							):




										"""simple docstring"""





										snake_case_    :List[str]										=    self._generate_dummy_inputs_for_encoder_and_decoder(
										    __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase )

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

															snake_case_    :Any										=    common_inputs['''attention_mask'''].dtype
															snake_case_    :Optional[int]										=    torch.cat(
															    [common_inputs["attention_mask"], torch.ones(__lowercase     ,							__lowercase     ,							dtype=__lowercase )]     ,							dim=1 )
															snake_case_    :List[str]										=    [
															    (torch.zeros(__lowercase ), torch.zeros(__lowercase )) for _ in range(__lowercase )
															]
										return common_inputs







					def 		_a			(   self     ,							a     ,							a = -1     ,							a = -1     ,							a = False     ,							a = None     ,							):




										"""simple docstring"""





										snake_case_    :Union[str, Any]										=    compute_effective_axis_dimension(
										    __lowercase     ,							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
										snake_case_    :Optional[int]										=    tokenizer.num_special_tokens_to_add(__lowercase )
										snake_case_    :Optional[int]										=    compute_effective_axis_dimension(
										    __lowercase     ,							fixed_dimension=OnnxConfig.default_fixed_sequence     ,							num_token_to_add=__lowercase )

										# Generate dummy inputs according to compute batch and sequence
										snake_case_    :Any										=    [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
										snake_case_    :Union[str, Any]										=    dict(tokenizer(__lowercase     ,							return_tensors=__lowercase ) )
										return common_inputs







					def 		_a			(   self     ,							a     ,							a = -1     ,							a = -1     ,							a = False     ,							a = None     ,							):




										"""simple docstring"""





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

										else:
															snake_case_    :List[Any]										=    self._generate_dummy_inputs_for_causal_lm(
															    __lowercase     ,							batch_size=__lowercase     ,							seq_length=__lowercase     ,							is_pair=__lowercase     ,							framework=__lowercase )

										return common_inputs







					def 		_a			(   self     ,							a     ,							a     ,							a     ,							a ):




										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
															snake_case_    :str										=    super()._flatten_past_key_values_(__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase )
										else:
															snake_case_    :Optional[Any]										=    super(__lowercase     ,							self )._flatten_past_key_values_(
															    __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase )







					@property
					def 		_a			(   self ):




										"""simple docstring"""





										return 1e-4








"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str] ):


							'''simple docstring'''







							create_state_space_tree(SCREAMING_SNAKE_CASE     ,    []     ,    0     ,    [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				list[int]     ,    ):


							'''simple docstring'''







							if index == len(SCREAMING_SNAKE_CASE ):
														print(SCREAMING_SNAKE_CASE )
														return

							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														if not index_used[i]:
																					current_sequence.append(sequence[i] )
																					__lowerCamelCase   :   List[Any]       =True
																					create_state_space_tree(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    index + 1     ,    SCREAMING_SNAKE_CASE )
																					current_sequence.pop()
																					__lowerCamelCase   :   List[Any]       =False
_UpperCamelCase					      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

_UpperCamelCase					      =   ["A", "B", "C"]
generate_all_permutations(sequence_a)




