

"""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_		(	__snake_case    ):
							_UpperCAmelCase   :  Any										=				'marian'
							_UpperCAmelCase   :  Optional[int]										=				['past_key_values']
							_UpperCAmelCase   :  List[Any]										=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=5_8_1_0_1       ,SCREAMING_SNAKE_CASE__		:      int=None       ,SCREAMING_SNAKE_CASE__		:      Dict=1_0_2_4       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_2       ,SCREAMING_SNAKE_CASE__		:      List[str]=4_0_9_6       ,SCREAMING_SNAKE_CASE__		:      int=1_6       ,SCREAMING_SNAKE_CASE__		:      str=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=4_0_9_6       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=0.0       ,SCREAMING_SNAKE_CASE__		:      int=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=True       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      int="gelu"       ,SCREAMING_SNAKE_CASE__		:      str=1_0_2_4       ,SCREAMING_SNAKE_CASE__		:      int=0.1       ,SCREAMING_SNAKE_CASE__		:      Dict=0.0       ,SCREAMING_SNAKE_CASE__		:      int=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.02       ,SCREAMING_SNAKE_CASE__		:      Tuple=5_8_1_0_0       ,SCREAMING_SNAKE_CASE__		:      str=False       ,SCREAMING_SNAKE_CASE__		:      int=5_8_1_0_0       ,SCREAMING_SNAKE_CASE__		:      str=0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True       ,**SCREAMING_SNAKE_CASE__		:      Any       ,):
								__lowerCamelCase    :    int 	=			vocab_size
								__lowerCamelCase    :    Any 	=			decoder_vocab_size or vocab_size
								__lowerCamelCase    :    List[Any] 	=			max_position_embeddings
								__lowerCamelCase    :    List[Any] 	=			d_model
								__lowerCamelCase    :    str 	=			encoder_ffn_dim
								__lowerCamelCase    :    List[Any] 	=			encoder_layers
								__lowerCamelCase    :    Optional[int] 	=			encoder_attention_heads
								__lowerCamelCase    :    int 	=			decoder_ffn_dim
								__lowerCamelCase    :    Tuple 	=			decoder_layers
								__lowerCamelCase    :    Dict 	=			decoder_attention_heads
								__lowerCamelCase    :    Union[str, Any] 	=			dropout
								__lowerCamelCase    :    Optional[Any] 	=			attention_dropout
								__lowerCamelCase    :    Optional[int] 	=			activation_dropout
								__lowerCamelCase    :    Tuple 	=			activation_function
								__lowerCamelCase    :    str 	=			init_std
								__lowerCamelCase    :    Union[str, Any] 	=			encoder_layerdrop
								__lowerCamelCase    :    List[str] 	=			decoder_layerdrop
								__lowerCamelCase    :    Dict 	=			use_cache
								__lowerCamelCase    :    Optional[Any] 	=			encoder_layers
								__lowerCamelCase    :    Dict 	=			scale_embedding  # scale factor will be sqrt(d_model) if True
								__lowerCamelCase    :    Union[str, Any] 	=			share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,decoder_start_token_id=SCREAMING_SNAKE_CASE__       ,forced_eos_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)






class 	A_		(	__snake_case    ):
							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
							def    lowerCAmelCase					(		self		:      Optional[int]):
								if self.task in ["default", "seq2seq-lm"]:
									__lowerCamelCase    :    str 	=			OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
									    ])

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

									if self.use_past:
										self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__       ,direction='inputs')
								elif self.task == "causal-lm":
									# TODO: figure this case out.
									__lowerCamelCase    :    Optional[Any] 	=			OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
									    ])
									if self.use_past:
										__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			self.num_layers
										for i in range(SCREAMING_SNAKE_CASE__):
											__lowerCamelCase    :    Tuple 	=			{0: 'batch', 2: 'past_sequence + sequence'}
											__lowerCamelCase    :    Union[str, Any] 	=			{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    lowerCAmelCase					(		self		:      List[str]):
								if self.task in ["default", "seq2seq-lm"]:
									__lowerCamelCase    :    Optional[Any] 	=			super().outputs
								else:
									__lowerCamelCase    :    Dict 	=			super(SCREAMING_SNAKE_CASE__       ,self).outputs
									if self.use_past:
										__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			self.num_layers
										for i in range(SCREAMING_SNAKE_CASE__):
											__lowerCamelCase    :    Union[str, Any] 	=			{0: 'batch', 2: 'past_sequence + sequence'}
											__lowerCamelCase    :    Union[str, Any] 	=			{0: 'batch', 2: 'past_sequence + sequence'}
								return common_outputs
							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = -1       ,SCREAMING_SNAKE_CASE__		:      Tuple = -1       ,SCREAMING_SNAKE_CASE__		:      List[Any] = False       ,SCREAMING_SNAKE_CASE__		:      List[str] = None       ,):
								__lowerCamelCase    :    str 	=			self._generate_dummy_inputs_for_encoder_and_decoder(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase    :    Optional[Any] 	=			torch.cat(
									    [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)]       ,dim=1)

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

									for _ in range(SCREAMING_SNAKE_CASE__):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(SCREAMING_SNAKE_CASE__),
										        torch.zeros(SCREAMING_SNAKE_CASE__),
										        torch.zeros(SCREAMING_SNAKE_CASE__),
										        torch.zeros(SCREAMING_SNAKE_CASE__),
										    ))
									# TODO: test this.
									__lowerCamelCase    :    Union[str, Any] 	=			encoder_shape if remaining_side_name == 'encoder' else decoder_shape
									for _ in range(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
										common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__), torch.zeros(SCREAMING_SNAKE_CASE__)))
								return common_inputs
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = -1       ,SCREAMING_SNAKE_CASE__		:      str = -1       ,SCREAMING_SNAKE_CASE__		:      int = False       ,SCREAMING_SNAKE_CASE__		:      str = None       ,):
								__lowerCamelCase    :    str 	=			self._generate_dummy_inputs_for_encoder_and_decoder(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_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
									__lowerCamelCase	,			__lowerCamelCase    :    int 	=			common_inputs['input_ids'].shape
									# Not using the same length for past_key_values
									__lowerCamelCase    :    Any 	=			seqlen + 2
									__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.num_layers
									__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			self.num_attention_heads
									__lowerCamelCase    :    str 	=			(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									__lowerCamelCase    :    Optional[Any] 	=			common_inputs['attention_mask'].dtype
									__lowerCamelCase    :    Any 	=			torch.cat(
									    [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,dtype=SCREAMING_SNAKE_CASE__)]       ,dim=1)
									__lowerCamelCase    :    str 	=			[
									    (torch.zeros(SCREAMING_SNAKE_CASE__), torch.zeros(SCREAMING_SNAKE_CASE__)) for _ in range(SCREAMING_SNAKE_CASE__)
									]
								return common_inputs
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[str] = -1       ,SCREAMING_SNAKE_CASE__		:      Dict = -1       ,SCREAMING_SNAKE_CASE__		:      List[Any] = False       ,SCREAMING_SNAKE_CASE__		:      int = 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
								__lowerCamelCase    :    Optional[int] 	=			compute_effective_axis_dimension(
								    SCREAMING_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
								__lowerCamelCase    :    str 	=			tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			compute_effective_axis_dimension(
								    SCREAMING_SNAKE_CASE__       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=SCREAMING_SNAKE_CASE__)

								# Generate dummy inputs according to compute batch and sequence
								__lowerCamelCase    :    str 	=			[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
								__lowerCamelCase    :    int 	=			dict(tokenizer(SCREAMING_SNAKE_CASE__       ,return_tensors=SCREAMING_SNAKE_CASE__))
								return common_inputs
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = -1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = -1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = False       ,SCREAMING_SNAKE_CASE__		:      List[Any] = None       ,):
								if self.task in ["default", "seq2seq-lm"]:
									__lowerCamelCase    :    Optional[int] 	=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    SCREAMING_SNAKE_CASE__       ,batch_size=SCREAMING_SNAKE_CASE__       ,seq_length=SCREAMING_SNAKE_CASE__       ,is_pair=SCREAMING_SNAKE_CASE__       ,framework=SCREAMING_SNAKE_CASE__)

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

								return common_inputs
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Any):
								if self.task in ["default", "seq2seq-lm"]:
									__lowerCamelCase    :    Optional[int] 	=			super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase    :    Optional[int] 	=			super(SCREAMING_SNAKE_CASE__       ,self)._flatten_past_key_values_(
									    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)


							@property
							def    lowerCAmelCase					(		self		:      int):
								return 1E-4







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a		=open  # noqa: we just need to have a builtin inside this module to test it properly




