





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							:					List[str]		    =		logging.get_logger(__name__)

_lowerCAmelCase							:					Optional[Any]		    =		{
    '''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     __magic_name__     (							snake_case_							):





     """simple docstring"""



     __UpperCamelCase    			=   '''marian'''
     __UpperCamelCase    			=   ['''past_key_values''']
     __UpperCamelCase    			=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



     def __init__(							self				:Optional[Any]	,					snake_case				:int=58_101	,					snake_case				:str=None	,					snake_case				:List[Any]=1_024	,					snake_case				:Optional[int]=12	,					snake_case				:Optional[Any]=4_096	,					snake_case				:Any=16	,					snake_case				:Any=12	,					snake_case				:Any=4_096	,					snake_case				:Optional[int]=16	,					snake_case				:Union[str, Any]=0.0	,					snake_case				:List[Any]=0.0	,					snake_case				:Any=True	,					snake_case				:Any=True	,					snake_case				:Any="gelu"	,					snake_case				:Union[str, Any]=1_024	,					snake_case				:Any=0.1	,					snake_case				:Union[str, Any]=0.0	,					snake_case				:int=0.0	,					snake_case				:Any=0.02	,					snake_case				:Union[str, Any]=58_100	,					snake_case				:Tuple=False	,					snake_case				:Optional[Any]=58_100	,					snake_case				:Dict=0	,					snake_case				:Union[str, Any]=0	,					snake_case				:Tuple=True	,					**snake_case				:Any	,					):





        '''simple docstring'''




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





     """simple docstring"""



     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




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

           if self.use_past:
              A_    : Any								=	{0: 'batch'}
              A_    : int								=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
              A_    : Optional[Any]								=	{0: 'batch', 1: 'decoder_sequence'}
              A_    : Tuple								=	{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.
           A_    : Any								=	OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]     )
           if self.use_past:
              A_    : Optional[int]								=	self.num_layers
              for i in range(snake_case     ):
                 A_    : Union[str, Any]								=	{0: 'batch', 2: 'past_sequence + sequence'}
                 A_    : str								=	{0: 'batch', 2: 'past_sequence + sequence'}
        else:
           A_    : List[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   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:int	,					snake_case				:PreTrainedTokenizer	,					snake_case				:int = -1	,					snake_case				:int = -1	,					snake_case				:bool = False	,					snake_case				:Optional[TensorType] = None	,					):





        '''simple docstring'''




        A_    : List[Any]								=	self._generate_dummy_inputs_for_encoder_and_decoder(
            snake_case	,					snake_case	,					snake_case	,					snake_case	,					snake_case     )

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

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

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





        '''simple docstring'''




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

           A_    : Any								=	common_inputs['attention_mask'].dtype
           A_    : Optional[Any]								=	torch.cat(
               [common_inputs["attention_mask"], torch.ones(snake_case	,					snake_case	,					dtype=snake_case     )]	,					dim=1     )
           A_    : Tuple								=	[
               (torch.zeros(snake_case     ), torch.zeros(snake_case     )) for _ in range(snake_case     )
           ]
        return common_inputs



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:PreTrainedTokenizer	,					snake_case				:int = -1	,					snake_case				:int = -1	,					snake_case				:bool = False	,					snake_case				:Optional[TensorType] = None	,					):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:PreTrainedTokenizer	,					snake_case				:int = -1	,					snake_case				:int = -1	,					snake_case				:bool = False	,					snake_case				:Optional[TensorType] = None	,					):





        '''simple docstring'''




        if self.task in ["default", "seq2seq-lm"]:
           A_    : 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:
           A_    : int								=	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   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Dict	,					snake_case				:Tuple	,					snake_case				:List[str]	,					snake_case				:Any     ):





        '''simple docstring'''




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





     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        return 1e-4

import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



