

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      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """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 SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Tuple						      ="mbart"
  lowerCamelCase    :			str						      =["past_key_values"]
  lowerCamelCase    :			Optional[int]						      ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







  def __init__(			self :    Optional[int]       ,							lowerCAmelCase :    List[str]=5_02_65       ,							lowerCAmelCase :    Any=10_24       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    List[str]=40_96       ,							lowerCAmelCase :    int=16       ,							lowerCAmelCase :    Optional[int]=12       ,							lowerCAmelCase :    int=40_96       ,							lowerCAmelCase :    List[str]=16       ,							lowerCAmelCase :    Union[str, Any]=0.0       ,							lowerCAmelCase :    Any=0.0       ,							lowerCAmelCase :    Optional[int]=True       ,							lowerCAmelCase :    Union[str, Any]=True       ,							lowerCAmelCase :    Union[str, Any]="gelu"       ,							lowerCAmelCase :    List[str]=10_24       ,							lowerCAmelCase :    Union[str, Any]=0.1       ,							lowerCAmelCase :    List[Any]=0.0       ,							lowerCAmelCase :    str=0.0       ,							lowerCAmelCase :    Tuple=0.02       ,							lowerCAmelCase :    Tuple=0.0       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    Tuple=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    str=2       ,							lowerCAmelCase :    Any=2       ,							**lowerCAmelCase :    List[Any]       ,							)							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						vocab_size
     __lowerCAmelCase  :			List[str]					    =						max_position_embeddings
     __lowerCAmelCase  :			int					    =						d_model
     __lowerCAmelCase  :			Any					    =						encoder_ffn_dim
     __lowerCAmelCase  :			Dict					    =						encoder_layers
     __lowerCAmelCase  :			Any					    =						encoder_attention_heads
     __lowerCAmelCase  :			Tuple					    =						decoder_ffn_dim
     __lowerCAmelCase  :			Dict					    =						decoder_layers
     __lowerCAmelCase  :			Optional[Any]					    =						decoder_attention_heads
     __lowerCAmelCase  :			Optional[int]					    =						dropout
     __lowerCAmelCase  :			Optional[int]					    =						attention_dropout
     __lowerCAmelCase  :			str					    =						activation_dropout
     __lowerCAmelCase  :			Optional[int]					    =						activation_function
     __lowerCAmelCase  :			List[str]					    =						init_std
     __lowerCAmelCase  :			List[str]					    =						encoder_layerdrop
     __lowerCAmelCase  :			Tuple					    =						decoder_layerdrop
     __lowerCAmelCase  :			Any					    =						classifier_dropout
     __lowerCAmelCase  :			Any					    =						use_cache
     __lowerCAmelCase  :			Tuple					    =						encoder_layers
     __lowerCAmelCase  :			Dict					    =						scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							is_encoder_decoder=lowerCAmelCase       ,							forced_eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase       ,							)






class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  @property
  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	Mapping[str, Mapping[int, str]]:






     """simple docstring"""







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

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






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PreTrainedTokenizer       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional[TensorType] = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )

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

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

        __lowerCAmelCase  :			Optional[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  :			List[Any]					    =						min(lowerCAmelCase       ,							lowerCAmelCase    )
        __lowerCAmelCase  :			List[Any]					    =						max(lowerCAmelCase       ,							lowerCAmelCase    ) - min_num_layers
        __lowerCAmelCase  :			str					    =						"""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.
        __lowerCAmelCase  :			Optional[Any]					    =						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        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]       ,							lowerCAmelCase :    PreTrainedTokenizer       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional[TensorType] = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         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
        __lowerCAmelCase    ,__lowerCAmelCase  :			Any					    =						common_inputs["""input_ids"""].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase  :			Dict					    =						seqlen + 2
        __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						self.num_layers
        __lowerCAmelCase    ,__lowerCAmelCase  :			Dict					    =						self.num_attention_heads
        __lowerCAmelCase  :			List[Any]					    =						(
            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  :			Optional[Any]					    =						torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase       ,							lowerCAmelCase       ,							dtype=lowerCAmelCase    )]       ,							dim=1    )
        __lowerCAmelCase  :			int					    =						[
            (torch.zeros(lowerCAmelCase    ), torch.zeros(lowerCAmelCase    )) for _ in range(lowerCAmelCase    )
        ]
     return common_inputs







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PreTrainedTokenizer       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional[TensorType] = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			List[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
     __lowerCAmelCase  :			Optional[int]					    =						tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
     __lowerCAmelCase  :			Tuple					    =						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
     __lowerCAmelCase  :			int					    =						[""" """.join([tokenizer.unk_token]    ) * seq_length] * batch_size
     __lowerCAmelCase  :			List[Any]					    =						dict(tokenizer(lowerCAmelCase       ,							return_tensors=lowerCAmelCase    )    )
     return common_inputs







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PreTrainedTokenizer       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional[TensorType] = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :			Optional[int]					    =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							seq_length=lowerCAmelCase       ,							is_pair=lowerCAmelCase       ,							framework=lowerCAmelCase    )

     elif self.task == "causal-lm":
        __lowerCAmelCase  :			List[str]					    =						self._generate_dummy_inputs_for_causal_lm(
            lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							seq_length=lowerCAmelCase       ,							is_pair=lowerCAmelCase       ,							framework=lowerCAmelCase    )
     else:
        __lowerCAmelCase  :			Dict					    =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							seq_length=lowerCAmelCase       ,							is_pair=lowerCAmelCase       ,							framework=lowerCAmelCase    )

     return common_inputs







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							lowerCAmelCase :    str       ,							lowerCAmelCase :    List[str]       ,							lowerCAmelCase :    int       ,							lowerCAmelCase :    Optional[Any]    )							->	Tuple:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :			Optional[Any]					    =						super()._flatten_past_key_values_(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )
     else:
        __lowerCAmelCase  :			int					    =						super(lowerCAmelCase       ,							self    )._flatten_past_key_values_(
            lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


