"""simple docstring"""



import warnings
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


SCREAMING_SNAKE_CASE__:Optional[int]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:str			=	{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class 				snake_case__     (   _UpperCAmelCase   ):
 _snake_case   :			int    			=		"bart"
 _snake_case   :			List[str]    			=		["past_key_values"]
 _snake_case   :			List[Any]    			=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


 def __init__(				self  ,						lowerCamelCase=50265  ,						lowerCamelCase=1024  ,						lowerCamelCase=12  ,						lowerCamelCase=4096  ,						lowerCamelCase=16  ,						lowerCamelCase=12  ,						lowerCamelCase=4096  ,						lowerCamelCase=16  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=1024  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.02  ,						lowerCamelCase=0.0  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						lowerCamelCase=3  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						lowerCamelCase=2  ,						lowerCamelCase=True  ,						lowerCamelCase=2  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        __a     				=  vocab_size
        __a     				=  max_position_embeddings
        __a     				=  d_model
        __a     				=  encoder_ffn_dim
        __a     				=  encoder_layers
        __a     				=  encoder_attention_heads
        __a     				=  decoder_ffn_dim
        __a     				=  decoder_layers
        __a     				=  decoder_attention_heads
        __a     				=  dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  activation_function
        __a     				=  init_std
        __a     				=  encoder_layerdrop
        __a     				=  decoder_layerdrop
        __a     				=  classifier_dropout
        __a     				=  use_cache
        __a     				=  encoder_layers
        __a     				=  scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=_UpperCAmelCase  ,						pad_token_id=_UpperCAmelCase  ,						bos_token_id=_UpperCAmelCase  ,						eos_token_id=_UpperCAmelCase  ,						is_encoder_decoder=_UpperCAmelCase  ,						decoder_start_token_id=_UpperCAmelCase  ,						forced_eos_token_id=_UpperCAmelCase  ,						**_UpperCAmelCase  ,						)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"  ,						_UpperCAmelCase		):
               __a     				=  self.bos_token_id
               warnings.warn(
                   F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                   "The config can simply be saved and uploaded again to be fixed."		)






class 				snake_case__     (   _UpperCAmelCase   ):


 @property
 def 		a__ (				self		):
        if self.task in ["default", "seq2seq-lm"]:
               __a     				=  OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ]		)

               if self.use_past:
                      __a     				=  {0: '''batch'''}
                      __a     				=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                      __a     				=  {0: '''batch''', 1: '''decoder_sequence'''}
                      __a     				=  {0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                      self.fill_with_past_key_values_(_UpperCAmelCase  ,						direction="inputs"		)
        elif self.task == "causal-lm":
               # TODO: figure this case out.
               __a     				=  OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ]		)
               if self.use_past:
                      __a     				=  self.num_layers
                      for i in range(_UpperCAmelCase		):
                             __a     				=  {0: '''batch''', 2: '''past_sequence + sequence'''}
                             __a     				=  {0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
               __a     				=  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
 def 		a__ (				self		):
        if self.task in ["default", "seq2seq-lm"]:
               __a     				=  super().outputs
        else:
               __a     				=  super(_UpperCAmelCase  ,						self		).outputs
               if self.use_past:
                      __a     				=  self.num_layers
                      for i in range(_UpperCAmelCase		):
                             __a     				=  {0: '''batch''', 2: '''past_sequence + sequence'''}
                             __a     				=  {0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = -1  ,						lowerCamelCase = -1  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						):
        __a     				=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase		)

        # Generate decoder inputs
        __a     				=  seq_length if not self.use_past else 1
        __a     				=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase		)
        __a     				=  {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        __a     				=  dict(**_UpperCAmelCase  ,						**_UpperCAmelCase		)

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

               __a     				=  torch.cat(
                   [common_inputs["decoder_attention_mask"], torch.ones(_UpperCAmelCase  ,						_UpperCAmelCase		)]  ,						dim=1		)

               __a     				=  []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               __a     				=  self.num_layers
               __a     				=  min(_UpperCAmelCase  ,						_UpperCAmelCase		)
               __a     				=  max(_UpperCAmelCase  ,						_UpperCAmelCase		) - min_num_layers
               __a     				=  '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = -1  ,						lowerCamelCase = -1  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						):
        __a     				=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase		)

        if self.use_past:
               if not is_torch_available():
                      raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
               else:
                      import torch
               __a     				=  common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               __a     				=  seqlen + 2
               __a     				=  self.num_layers
               __a     				=  self.num_attention_heads
               __a     				=  (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = -1  ,						lowerCamelCase = -1  ,						lowerCamelCase = False  ,						lowerCamelCase = 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
        __a     				=  compute_effective_axis_dimension(
            _UpperCAmelCase  ,						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     				=  tokenizer.num_special_tokens_to_add(_UpperCAmelCase		)
        __a     				=  compute_effective_axis_dimension(
            _UpperCAmelCase  ,						fixed_dimension=OnnxConfig.default_fixed_sequence  ,						num_token_to_add=_UpperCAmelCase		)

        # Generate dummy inputs according to compute batch and sequence
        __a     				=  [''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
        __a     				=  dict(tokenizer(_UpperCAmelCase  ,						return_tensors=_UpperCAmelCase		)		)
        return common_inputs


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = -1  ,						lowerCamelCase = -1  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						):
        if self.task in ["default", "seq2seq-lm"]:
               __a     				=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   _UpperCAmelCase  ,						batch_size=_UpperCAmelCase  ,						seq_length=_UpperCAmelCase  ,						is_pair=_UpperCAmelCase  ,						framework=_UpperCAmelCase		)

        elif self.task == "causal-lm":
               __a     				=  self._generate_dummy_inputs_for_causal_lm(
                   _UpperCAmelCase  ,						batch_size=_UpperCAmelCase  ,						seq_length=_UpperCAmelCase  ,						is_pair=_UpperCAmelCase  ,						framework=_UpperCAmelCase		)
        else:
               __a     				=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   _UpperCAmelCase  ,						batch_size=_UpperCAmelCase  ,						seq_length=_UpperCAmelCase  ,						is_pair=_UpperCAmelCase  ,						framework=_UpperCAmelCase		)

        return common_inputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        if self.task in ["default", "seq2seq-lm"]:
               __a     				=  super()._flatten_past_key_values_(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase		)
        else:
               __a     				=  super(_UpperCAmelCase  ,						self		)._flatten_past_key_values_(
                   _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase		)

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
