

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						:List[Any]      =			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 		__magic_name__ (				__snake_case							):
     UpperCamelCase_     :Union[str, Any]									=	'mbart'
     UpperCamelCase_     :int									=	['past_key_values']
     UpperCamelCase_     :List[Any]									=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






     def __init__(							self						,		_lowercase=50_265						,		_lowercase=1_024						,		_lowercase=12						,		_lowercase=4_096						,		_lowercase=16						,		_lowercase=12						,		_lowercase=4_096						,		_lowercase=16						,		_lowercase=0.0						,		_lowercase=0.0						,		_lowercase=True						,		_lowercase=True						,		_lowercase="gelu"						,		_lowercase=1_024						,		_lowercase=0.1						,		_lowercase=0.0						,		_lowercase=0.0						,		_lowercase=0.02						,		_lowercase=0.0						,		_lowercase=False						,		_lowercase=1						,		_lowercase=0						,		_lowercase=2						,		_lowercase=2						,		**_lowercase						,		)->			Tuple:
         UpperCamelCase_          =		vocab_size
         UpperCamelCase_          =		max_position_embeddings
         UpperCamelCase_          =		d_model
         UpperCamelCase_          =		encoder_ffn_dim
         UpperCamelCase_          =		encoder_layers
         UpperCamelCase_          =		encoder_attention_heads
         UpperCamelCase_          =		decoder_ffn_dim
         UpperCamelCase_          =		decoder_layers
         UpperCamelCase_          =		decoder_attention_heads
         UpperCamelCase_          =		dropout
         UpperCamelCase_          =		attention_dropout
         UpperCamelCase_          =		activation_dropout
         UpperCamelCase_          =		activation_function
         UpperCamelCase_          =		init_std
         UpperCamelCase_          =		encoder_layerdrop
         UpperCamelCase_          =		decoder_layerdrop
         UpperCamelCase_          =		classifier_dropout
         UpperCamelCase_          =		use_cache
         UpperCamelCase_          =		encoder_layers
         UpperCamelCase_          =		scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=_lowercase						,		bos_token_id=_lowercase						,		eos_token_id=_lowercase						,		is_encoder_decoder=_lowercase						,		forced_eos_token_id=_lowercase						,		**_lowercase						,		)






class 		__magic_name__ (				__snake_case							):






     @property
     def      UpperCAmelCase_							(							self     )->			Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_          =		OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]     )

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

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = -1						,		_lowercase = -1						,		_lowercase = False						,		_lowercase = None						,		)->			Mapping[str, Any]:
         UpperCamelCase_          =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )

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

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

             UpperCamelCase_          =		torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_lowercase						,		_lowercase     )]						,		dim=1     )

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

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = -1						,		_lowercase = -1						,		_lowercase = False						,		_lowercase = None						,		)->			Mapping[str, Any]:
         UpperCamelCase_          =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )

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

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = -1						,		_lowercase = -1						,		_lowercase = False						,		_lowercase = 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
         UpperCamelCase_          =		compute_effective_axis_dimension(
             _lowercase						,		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
         UpperCamelCase_          =		tokenizer.num_special_tokens_to_add(_lowercase     )
         UpperCamelCase_          =		compute_effective_axis_dimension(
             _lowercase						,		fixed_dimension=OnnxConfig.default_fixed_sequence						,		num_token_to_add=_lowercase     )

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






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

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

         return common_inputs






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			Dict:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_          =		super()._flatten_past_key_values_(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )
         else:
             UpperCamelCase_          =		super(_lowercase						,		self     )._flatten_past_key_values_(
                 _lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)