

'''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


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''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_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  int							=			'marian'
  SCREAMING_SNAKE_CASE    :  Tuple							=			['past_key_values']
  SCREAMING_SNAKE_CASE    :  List[Any]							=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






  def __init__(      self   :     Any					,lowercase__   :     Tuple=5_8_1_0_1					,lowercase__   :     Any=None					,lowercase__   :     List[Any]=1_0_2_4					,lowercase__   :     Tuple=1_2					,lowercase__   :     Optional[int]=4_0_9_6					,lowercase__   :     Any=1_6					,lowercase__   :     Dict=1_2					,lowercase__   :     int=4_0_9_6					,lowercase__   :     Tuple=1_6					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=0.0					,lowercase__   :     int=True					,lowercase__   :     str=True					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     Optional[int]=1_0_2_4					,lowercase__   :     Optional[Any]=0.1					,lowercase__   :     List[Any]=0.0					,lowercase__   :     List[str]=0.0					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=5_8_1_0_0					,lowercase__   :     List[Any]=False					,lowercase__   :     Dict=5_8_1_0_0					,lowercase__   :     List[str]=0					,lowercase__   :     Optional[Any]=0					,lowercase__   :     int=True					,**lowercase__   :     Any					,):
   __lowercase							  = vocab_size
   __lowercase							  = decoder_vocab_size or vocab_size
   __lowercase							  = max_position_embeddings
   __lowercase							  = d_model
   __lowercase							  = encoder_ffn_dim
   __lowercase							  = encoder_layers
   __lowercase							  = encoder_attention_heads
   __lowercase							  = decoder_ffn_dim
   __lowercase							  = decoder_layers
   __lowercase							  = decoder_attention_heads
   __lowercase							  = dropout
   __lowercase							  = attention_dropout
   __lowercase							  = activation_dropout
   __lowercase							  = activation_function
   __lowercase							  = init_std
   __lowercase							  = encoder_layerdrop
   __lowercase							  = decoder_layerdrop
   __lowercase							  = use_cache
   __lowercase							  = encoder_layers
   __lowercase							  = scale_embedding  # scale factor will be sqrt(d_model) if True
   __lowercase							  = 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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




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

    if self.use_past:
     __lowercase							  = {0: '''batch'''}
     __lowercase							  = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
     __lowercase							  = {0: '''batch''', 1: '''decoder_sequence'''}
     __lowercase							  = {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.
    __lowercase							  = OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
        ]			)
    if self.use_past:
     __lowercase  ,	__lowercase							  = self.num_layers
     for i in range(lowercase__			):
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
   else:
    __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = super().outputs
   else:
    __lowercase							  = super(lowercase__					,self			).outputs
    if self.use_past:
     __lowercase  ,	__lowercase							  = self.num_layers
     for i in range(lowercase__			):
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
   return common_outputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   __lowercase							  = self._generate_dummy_inputs_for_encoder_and_decoder(
       lowercase__					,lowercase__					,lowercase__					,lowercase__					,lowercase__			)

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

    __lowercase							  = torch.cat(
        [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase__					,lowercase__			)]					,dim=1			)

    __lowercase							  = []
    # If the number of encoder and decoder layers are present in the model configuration, both are considered
    __lowercase  ,	__lowercase							  = self.num_layers
    __lowercase							  = min(lowercase__					,lowercase__			)
    __lowercase							  = max(lowercase__					,lowercase__			) - min_num_layers
    __lowercase							  = '''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.
    __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   __lowercase							  = 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
    __lowercase  ,	__lowercase							  = common_inputs['''input_ids'''].shape
    # Not using the same length for past_key_values
    __lowercase							  = seqlen + 2
    __lowercase  ,	__lowercase							  = self.num_layers
    __lowercase  ,	__lowercase							  = self.num_attention_heads
    __lowercase							  = (
        batch,
        num_encoder_attention_heads,
        past_key_values_length,
        self._config.hidden_size // num_encoder_attention_heads,
    )

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     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
   __lowercase							  = 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
   __lowercase							  = tokenizer.num_special_tokens_to_add(lowercase__			)
   __lowercase							  = 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
   __lowercase							  = [''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
   __lowercase							  = dict(tokenizer(lowercase__					,return_tensors=lowercase__			)			)
   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
        lowercase__					,batch_size=lowercase__					,seq_length=lowercase__					,is_pair=lowercase__					,framework=lowercase__			)

   else:
    __lowercase							  = self._generate_dummy_inputs_for_causal_lm(
        lowercase__					,batch_size=lowercase__					,seq_length=lowercase__					,is_pair=lowercase__					,framework=lowercase__			)

   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     Optional[int]					,lowercase__   :     str			):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = super()._flatten_past_key_values_(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
   else:
    __lowercase							  = super(lowercase__					,self			)._flatten_past_key_values_(
        lowercase__					,lowercase__					,lowercase__					,lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   return 1e-4





"""simple docstring"""





snake_case__						:   str			 = [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

snake_case__						:   Optional[Any]			 = [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

snake_case__						:   Any			 = [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

snake_case__						:   Optional[Any]			 = [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

snake_case__						:   int			 = [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

snake_case__						:   Union[str, Any]			 = [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

snake_case__						:   List[Any]			 = [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

snake_case__						:   Optional[int]			 = [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]


