'''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						:    Union[str, Any]     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    int     					=					{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "mbart"
   __lowerCAmelCase    						=      ["past_key_values"]
   __lowerCAmelCase    						=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
   def __init__(			self			: Optional[int],   UpperCamelCase__			: List[Any]=5_02_65,   UpperCamelCase__			: Optional[Any]=10_24,   UpperCamelCase__			: str=12,   UpperCamelCase__			: List[Any]=40_96,   UpperCamelCase__			: Optional[int]=16,   UpperCamelCase__			: int=12,   UpperCamelCase__			: List[str]=40_96,   UpperCamelCase__			: Optional[int]=16,   UpperCamelCase__			: Any=0.0,   UpperCamelCase__			: Tuple=0.0,   UpperCamelCase__			: int=True,   UpperCamelCase__			: int=True,   UpperCamelCase__			: List[Any]="gelu",   UpperCamelCase__			: List[Any]=10_24,   UpperCamelCase__			: List[str]=0.1,   UpperCamelCase__			: List[str]=0.0,   UpperCamelCase__			: List[str]=0.0,   UpperCamelCase__			: Union[str, Any]=0.02,   UpperCamelCase__			: Optional[Any]=0.0,   UpperCamelCase__			: Optional[Any]=False,   UpperCamelCase__			: Optional[Any]=1,   UpperCamelCase__			: Tuple=0,   UpperCamelCase__			: Union[str, Any]=2,   UpperCamelCase__			: List[Any]=2,   **UpperCamelCase__			: List[Any],   )     -> List[str]:
        _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__(
            pad_token_id=UpperCamelCase__,   bos_token_id=UpperCamelCase__,   eos_token_id=UpperCamelCase__,   is_encoder_decoder=UpperCamelCase__,   forced_eos_token_id=UpperCamelCase__,   **UpperCamelCase__,   )







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   @property
   def   __UpperCAmelCase      (			self			: str    )     -> Mapping[str, Mapping[int, str]]:
        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		,					_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   __UpperCAmelCase      (			self			: int    )     -> Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             _A								=     super().outputs
        else:
             _A								=     super(UpperCamelCase__,   self    ).outputs
             if self.use_past:
                  _A		,					_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   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: PreTrainedTokenizer,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[TensorType] = None,   )     -> Mapping[str, Any]:
        _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		,					_A								=     common_inputs['input_ids'].shape
             _A								=     common_inputs['decoder_input_ids'].shape[1]
             _A		,					_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		,					_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   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: PreTrainedTokenizer,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[TensorType] = None,   )     -> Mapping[str, Any]:
        _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		,					_A								=     common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _A								=     seqlen + 2
             _A		,					_A								=     self.num_layers
             _A		,					_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   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: PreTrainedTokenizer,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[TensorType] = 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								=     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   __UpperCAmelCase      (			self			: Optional[Any],   UpperCamelCase__			: PreTrainedTokenizer,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: int = -1,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[TensorType] = None,   )     -> Mapping[str, Any]:
        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   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Any    )     -> Tuple:
        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'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase						:    Dict     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



