


'''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_		:   Dict							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   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     lowercase__  (						_snake_case					):





							'''simple docstring'''


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






							def __init__(    self    ,					__snake_case=5_8101    ,					__snake_case=None    ,					__snake_case=1024    ,					__snake_case=12    ,					__snake_case=4096    ,					__snake_case=16    ,					__snake_case=12    ,					__snake_case=4096    ,					__snake_case=16    ,					__snake_case=0.0    ,					__snake_case=0.0    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case="gelu"    ,					__snake_case=1024    ,					__snake_case=0.1    ,					__snake_case=0.0    ,					__snake_case=0.0    ,					__snake_case=0.02    ,					__snake_case=5_8100    ,					__snake_case=False    ,					__snake_case=5_8100    ,					__snake_case=0    ,					__snake_case=0    ,					__snake_case=True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       int     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       List[str]     		=      decoder_vocab_size or vocab_size
											_SCREAMING_SNAKE_CASE :       str     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      d_model
											_SCREAMING_SNAKE_CASE :       Any     		=      encoder_ffn_dim
											_SCREAMING_SNAKE_CASE :       Dict     		=      encoder_layers
											_SCREAMING_SNAKE_CASE :       Tuple     		=      encoder_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      decoder_ffn_dim
											_SCREAMING_SNAKE_CASE :       int     		=      decoder_layers
											_SCREAMING_SNAKE_CASE :       List[str]     		=      decoder_attention_heads
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      dropout
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      attention_dropout
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      activation_dropout
											_SCREAMING_SNAKE_CASE :       Any     		=      activation_function
											_SCREAMING_SNAKE_CASE :       int     		=      init_std
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      encoder_layerdrop
											_SCREAMING_SNAKE_CASE :       Any     		=      decoder_layerdrop
											_SCREAMING_SNAKE_CASE :       Tuple     		=      use_cache
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      encoder_layers
											_SCREAMING_SNAKE_CASE :       List[str]     		=      scale_embedding  # scale factor will be sqrt(d_model) if True
											_SCREAMING_SNAKE_CASE :       int     		=      share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					is_encoder_decoder=__snake_case    ,					decoder_start_token_id=__snake_case    ,					forced_eos_token_id=__snake_case    ,					**__snake_case    ,					)





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


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

															if self.use_past:
																			_SCREAMING_SNAKE_CASE :       Any     		=      {0: """batch"""}
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
															else:
																			_SCREAMING_SNAKE_CASE :       Any     		=      {0: """batch""", 1: """decoder_sequence"""}
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {0: """batch""", 1: """decoder_sequence"""}

															if self.use_past:
																			self.fill_with_past_key_values_(__snake_case    ,					direction="""inputs"""  )
											elif self.task == "causal-lm":
															# TODO: figure this case out.
															_SCREAMING_SNAKE_CASE :       str     		=      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 :       Any     		=      self.num_layers
																			for i in range(__snake_case  ):
																							_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 2: """past_sequence + sequence"""}
																							_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 2: """past_sequence + sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       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 UpperCAmelCase_	(    self  ):
											if self.task in ["default", "seq2seq-lm"]:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      super().outputs
											else:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      super(__snake_case    ,					self  ).outputs
															if self.use_past:
																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      self.num_layers
																			for i in range(__snake_case  ):
																							_SCREAMING_SNAKE_CASE :       Any     		=      {0: """batch""", 2: """past_sequence + sequence"""}
																							_SCREAMING_SNAKE_CASE :       List[Any]     		=      {0: """batch""", 2: """past_sequence + sequence"""}
											return common_outputs






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = -1    ,					__snake_case = -1    ,					__snake_case = False    ,					__snake_case = None    ,					):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self._generate_dummy_inputs_for_encoder_and_decoder(
											    __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )

											# Generate decoder inputs
											_SCREAMING_SNAKE_CASE :       List[str]     		=      seq_length if not self.use_past else 1
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self._generate_dummy_inputs_for_encoder_and_decoder(
											    __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											_SCREAMING_SNAKE_CASE :       Tuple     		=      dict(**__snake_case    ,					**__snake_case  )

											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 :       int     		=      common_inputs["""input_ids"""].shape
															_SCREAMING_SNAKE_CASE :       Any     		=      common_inputs["""decoder_input_ids"""].shape[1]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.num_attention_heads
															_SCREAMING_SNAKE_CASE :       Any     		=      (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															_SCREAMING_SNAKE_CASE :       Any     		=      decoder_seq_length + 3
															_SCREAMING_SNAKE_CASE :       Dict     		=      (
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(__snake_case    ,					__snake_case  )]    ,					dim=1  )

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

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






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = -1    ,					__snake_case = -1    ,					__snake_case = False    ,					__snake_case = None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      self._generate_dummy_inputs_for_encoder_and_decoder(
											    __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )

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

															_SCREAMING_SNAKE_CASE :       int     		=      common_inputs["""attention_mask"""].dtype
															_SCREAMING_SNAKE_CASE :       int     		=      torch.cat(
															    [common_inputs["""attention_mask"""], torch.ones(__snake_case    ,					__snake_case    ,					dtype=__snake_case  )]    ,					dim=1  )
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      [
															    (torch.zeros(__snake_case  ), torch.zeros(__snake_case  )) for _ in range(__snake_case  )
															]
											return common_inputs






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = -1    ,					__snake_case = -1    ,					__snake_case = False    ,					__snake_case = 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 :       List[Any]     		=      compute_effective_axis_dimension(
											    __snake_case    ,					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 :       Any     		=      tokenizer.num_special_tokens_to_add(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      compute_effective_axis_dimension(
											    __snake_case    ,					fixed_dimension=OnnxConfig.default_fixed_sequence    ,					num_token_to_add=__snake_case  )

											# Generate dummy inputs according to compute batch and sequence
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
											_SCREAMING_SNAKE_CASE :       str     		=      dict(tokenizer(__snake_case    ,					return_tensors=__snake_case  )  )
											return common_inputs






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = -1    ,					__snake_case = -1    ,					__snake_case = False    ,					__snake_case = None    ,					):
											if self.task in ["default", "seq2seq-lm"]:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    __snake_case    ,					batch_size=__snake_case    ,					seq_length=__snake_case    ,					is_pair=__snake_case    ,					framework=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      self._generate_dummy_inputs_for_causal_lm(
															    __snake_case    ,					batch_size=__snake_case    ,					seq_length=__snake_case    ,					is_pair=__snake_case    ,					framework=__snake_case  )

											return common_inputs






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											if self.task in ["default", "seq2seq-lm"]:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      super()._flatten_past_key_values_(__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )
											else:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      super(__snake_case    ,					self  )._flatten_past_key_values_(
															    __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )






							@property
							def UpperCAmelCase_	(    self  ):
											return 1e-4








'''simple docstring'''


UpperCAmelCase_		:   Dict							   =		[
    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,
]

UpperCAmelCase_		:   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,
]

UpperCAmelCase_		:   Tuple							   =		[
    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,
]

UpperCAmelCase_		:   Dict							   =		[
    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,
]

UpperCAmelCase_		:   Tuple							   =		[
    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,
]

UpperCAmelCase_		:   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,
]

UpperCAmelCase_		:   Tuple							   =		[
    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,
]

UpperCAmelCase_		:   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,
]




