


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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "marian"
     __SCREAMING_SNAKE_CASE  			= ["past_key_values"]
     __SCREAMING_SNAKE_CASE  			= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


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







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


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

                   if self.use_past:
                          _A      :       Tuple				    = {0: "batch"}
                          _A      :       int				    = {0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                          _A      :       Dict				    = {0: "batch", 1: "decoder_sequence"}
                          _A      :       List[Any]				    = {0: "batch", 1: "decoder_sequence"}

                   if self.use_past:
                          self.fill_with_past_key_values_(__lowerCamelCase ,						direction="inputs")
            elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   _A      :       List[str]				    = OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ])
                   if self.use_past:
                          _A    ,			_A      :       int				    = self.num_layers
                          for i in range(__lowerCamelCase):
                                 _A      :       List[Any]				    = {0: "batch", 2: "past_sequence + sequence"}
                                 _A      :       str				    = {0: "batch", 2: "past_sequence + sequence"}
            else:
                   _A      :       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)			->				Mapping[str, Mapping[int, str]]:
            if self.task in ["default", "seq2seq-lm"]:
                   _A      :       Union[str, Any]				    = super().outputs
            else:
                   _A      :       List[Any]				    = super(__lowerCamelCase ,						self).outputs
                   if self.use_past:
                          _A    ,			_A      :       Union[str, Any]				    = self.num_layers
                          for i in range(__lowerCamelCase):
                                 _A      :       List[Any]				    = {0: "batch", 2: "past_sequence + sequence"}
                                 _A      :       Union[str, Any]				    = {0: "batch", 2: "past_sequence + sequence"}
            return common_outputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = -1 ,						__lowerCamelCase = -1 ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						)			->				Mapping[str, Any]:
            _A      :       Any				    = self._generate_dummy_inputs_for_encoder_and_decoder(
                __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

            # Generate decoder inputs
            _A      :       Dict				    = seq_length if not self.use_past else 1
            _A      :       str				    = self._generate_dummy_inputs_for_encoder_and_decoder(
                __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)
            _A      :       Tuple				    = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
            _A      :       List[str]				    = dict(**__lowerCamelCase ,						**__lowerCamelCase)

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

                   _A      :       Dict				    = torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase ,						__lowerCamelCase)] ,						dim=1)

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

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = -1 ,						__lowerCamelCase = -1 ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						)			->				Mapping[str, Any]:
            _A      :       Dict				    = self._generate_dummy_inputs_for_encoder_and_decoder(
                __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

            if self.use_past:
                   if not is_torch_available():
                          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
                   else:
                          import torch
                   _A    ,			_A      :       Optional[int]				    = common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   _A      :       Dict				    = seqlen + 2
                   _A    ,			_A      :       Any				    = self.num_layers
                   _A    ,			_A      :       Dict				    = 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      :       Union[str, Any]				    = common_inputs["attention_mask"].dtype
                   _A      :       Any				    = torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase ,						__lowerCamelCase ,						dtype=__lowerCamelCase)] ,						dim=1)
                   _A      :       List[Any]				    = [
                       (torch.zeros(__lowerCamelCase), torch.zeros(__lowerCamelCase)) for _ in range(__lowerCamelCase)
                   ]
            return common_inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = -1 ,						__lowerCamelCase = -1 ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						)			->				Mapping[str, Any]:
            # 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
            _A      :       Union[str, Any]				    = compute_effective_axis_dimension(
                __lowerCamelCase ,						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      :       Optional[int]				    = tokenizer.num_special_tokens_to_add(__lowerCamelCase)
            _A      :       Dict				    = compute_effective_axis_dimension(
                __lowerCamelCase ,						fixed_dimension=OnnxConfig.default_fixed_sequence ,						num_token_to_add=__lowerCamelCase)

            # Generate dummy inputs according to compute batch and sequence
            _A      :       List[Any]				    = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
            _A      :       Dict				    = dict(tokenizer(__lowerCamelCase ,						return_tensors=__lowerCamelCase))
            return common_inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = -1 ,						__lowerCamelCase = -1 ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						)			->				Mapping[str, Any]:
            if self.task in ["default", "seq2seq-lm"]:
                   _A      :       Union[str, Any]				    = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       __lowerCamelCase ,						batch_size=__lowerCamelCase ,						seq_length=__lowerCamelCase ,						is_pair=__lowerCamelCase ,						framework=__lowerCamelCase)

            else:
                   _A      :       Any				    = self._generate_dummy_inputs_for_causal_lm(
                       __lowerCamelCase ,						batch_size=__lowerCamelCase ,						seq_length=__lowerCamelCase ,						is_pair=__lowerCamelCase ,						framework=__lowerCamelCase)

            return common_inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            if self.task in ["default", "seq2seq-lm"]:
                   _A      :       str				    = super()._flatten_past_key_values_(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)
            else:
                   _A      :       Tuple				    = super(__lowerCamelCase ,						self)._flatten_past_key_values_(
                       __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				float:
            return 1e-4




def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
