




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			:      str      			=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:      Tuple      			={
    """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 	A	(						a_						):
     """simple docstring"""

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




     def __init__(			self     :      Optional[int]		,     A__     :      List[str]=5_0_2_6_5		,     A__     :      Any=1_0_2_4		,     A__     :      str=1_2		,     A__     :      List[str]=4_0_9_6		,     A__     :      int=1_6		,     A__     :      Optional[int]=1_2		,     A__     :      int=4_0_9_6		,     A__     :      List[str]=1_6		,     A__     :      Union[str, Any]=0.0		,     A__     :      Any=0.0		,     A__     :      Optional[int]=True		,     A__     :      Union[str, Any]=True		,     A__     :      Union[str, Any]="gelu"		,     A__     :      List[str]=1_0_2_4		,     A__     :      Union[str, Any]=0.1		,     A__     :      List[Any]=0.0		,     A__     :      str=0.0		,     A__     :      Tuple=0.02		,     A__     :      Tuple=0.0		,     A__     :      Union[str, Any]=False		,     A__     :      Tuple=1		,     A__     :      str=0		,     A__     :      str=2		,     A__     :      Any=2		,     **A__     :      List[Any]		,     ) ->				Optional[int]:
           lowerCamelCase__					     = vocab_size
           lowerCamelCase__					     = max_position_embeddings
           lowerCamelCase__					     = d_model
           lowerCamelCase__					     = encoder_ffn_dim
           lowerCamelCase__					     = encoder_layers
           lowerCamelCase__					     = encoder_attention_heads
           lowerCamelCase__					     = decoder_ffn_dim
           lowerCamelCase__					     = decoder_layers
           lowerCamelCase__					     = decoder_attention_heads
           lowerCamelCase__					     = dropout
           lowerCamelCase__					     = attention_dropout
           lowerCamelCase__					     = activation_dropout
           lowerCamelCase__					     = activation_function
           lowerCamelCase__					     = init_std
           lowerCamelCase__					     = encoder_layerdrop
           lowerCamelCase__					     = decoder_layerdrop
           lowerCamelCase__					     = classifier_dropout
           lowerCamelCase__					     = use_cache
           lowerCamelCase__					     = encoder_layers
           lowerCamelCase__					     = scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=A__		,     bos_token_id=A__		,     eos_token_id=A__		,     is_encoder_decoder=A__		,     forced_eos_token_id=A__		,     **A__		,     )






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

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







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

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

                 if self.use_past:
                       self.fill_with_past_key_values_(A__		,     direction='inputs'	)
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 lowerCamelCase__					     = OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]	)
                 if self.use_past:
                       lowerCamelCase__					     = self.num_layers
                       for i in range(A__	):
                             lowerCamelCase__					     = {0: """batch""", 2: """past_sequence + sequence"""}
                             lowerCamelCase__					     = {0: """batch""", 2: """past_sequence + sequence"""}
           else:
                 lowerCamelCase__					     = 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        __snake_case      (			self     :      Optional[Any]	) ->				Mapping[str, Mapping[int, str]]:







           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = super().outputs
           else:
                 lowerCamelCase__					     = super(A__		,     self	).outputs
                 if self.use_past:
                       lowerCamelCase__					     = self.num_layers
                       for i in range(A__	):
                             lowerCamelCase__					     = {0: """batch""", 2: """past_sequence + sequence"""}
                             lowerCamelCase__					     = {0: """batch""", 2: """past_sequence + sequence"""}
           return common_outputs




     def        __snake_case      (			self     :      Dict		,     A__     :      PreTrainedTokenizer		,     A__     :      int = -1		,     A__     :      int = -1		,     A__     :      bool = False		,     A__     :      Optional[TensorType] = None		,     ) ->				Mapping[str, Any]:
           lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               A__		,     A__		,     A__		,     A__		,     A__	)

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

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

                 lowerCamelCase__					     = torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(A__		,     A__	)]		,     dim=1	)

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

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




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      PreTrainedTokenizer		,     A__     :      int = -1		,     A__     :      int = -1		,     A__     :      bool = False		,     A__     :      Optional[TensorType] = None		,     ) ->				Mapping[str, Any]:
           lowerCamelCase__					     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               A__		,     A__		,     A__		,     A__		,     A__	)

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

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




     def        __snake_case      (			self     :      Dict		,     A__     :      PreTrainedTokenizer		,     A__     :      int = -1		,     A__     :      int = -1		,     A__     :      bool = False		,     A__     :      Optional[TensorType] = None		,     ) ->				Mapping[str, Any]:
           lowerCamelCase__					     = compute_effective_axis_dimension(
               A__		,     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__					     = tokenizer.num_special_tokens_to_add(A__	)
           lowerCamelCase__					     = compute_effective_axis_dimension(
               A__		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=A__	)

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




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







           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     A__		,     batch_size=A__		,     seq_length=A__		,     is_pair=A__		,     framework=A__	)

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

           return common_inputs




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







           if self.task in ["default", "seq2seq-lm"]:
                 lowerCamelCase__					     = super()._flatten_past_key_values_(A__		,     A__		,     A__		,     A__	)
           else:
                 lowerCamelCase__					     = super(A__		,     self	)._flatten_past_key_values_(
                     A__		,     A__		,     A__		,     A__	)


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    )


