


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__snake_case									=		logging.get_logger(__name__)

__snake_case									=		{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class      __lowerCamelCase       (lowerCAmelCase__		):
       _lowercase											=			'''blenderbot-small'''
       _lowercase											=			['''past_key_values''']
       _lowercase											=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







       def __init__(       self:      List[str],A_:      Dict=5_0265,A_:      List[str]=512,A_:      Optional[int]=8,A_:      List[Any]=2048,A_:      Union[str, Any]=16,A_:      str=8,A_:      int=2048,A_:      str=16,A_:      List[Any]=0.0,A_:      str=0.0,A_:      List[str]=True,A_:      int=True,A_:      str="gelu",A_:      Any=512,A_:      int=0.1,A_:      Optional[Any]=0.0,A_:      Dict=0.0,A_:      str=0.0_2,A_:      Tuple=1,A_:      Tuple=False,A_:      Optional[Any]=0,A_:      Optional[int]=1,A_:      Optional[Any]=2,A_:      List[str]=2,**A_:      Optional[int],):






            '''simple docstring'''




            __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      =       use_cache
            __UpperCamelCase      =       encoder_layers
            __UpperCamelCase      =       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,decoder_start_token_id=__lowerCAmelCase,forced_eos_token_id=__lowerCAmelCase,**__lowerCAmelCase,)



class      __lowerCamelCase       (lowerCAmelCase__		):







       @property
       def 						snake_case_     (       self:      Dict ):






            '''simple docstring'''




            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_(__lowerCAmelCase,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(__lowerCAmelCase ):
                           __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 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




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







       def 						snake_case_     (       self:      Union[str, Any],A_:      List[str],A_:      Tuple = -1,A_:      int = -1,A_:      Optional[Any] = False,A_:      Optional[Any] = None,):






            '''simple docstring'''




            __UpperCamelCase      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase )

            # Generate decoder inputs
            __UpperCamelCase      =       seq_length if not self.use_past else 1
            __UpperCamelCase      =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase )
            __UpperCamelCase      =       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
            __UpperCamelCase      =       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
                 __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(__lowerCAmelCase,__lowerCAmelCase )],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(__lowerCAmelCase,__lowerCAmelCase )
                 __UpperCamelCase      =       max(__lowerCAmelCase,__lowerCAmelCase ) - min_num_layers
                 __UpperCamelCase      =       '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.
                 __UpperCamelCase      =       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 						snake_case_     (       self:      int,A_:      Optional[int],A_:      Union[str, Any] = -1,A_:      List[Any] = -1,A_:      str = False,A_:      List[Any] = None,):






            '''simple docstring'''




            __UpperCamelCase      =       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
                 __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(__lowerCAmelCase,__lowerCAmelCase,dtype=__lowerCAmelCase )],dim=1 )
                 __UpperCamelCase      =       [
                     (torch.zeros(__lowerCAmelCase ), torch.zeros(__lowerCAmelCase )) for _ in range(__lowerCAmelCase )
                 ]
            return common_inputs







       def 						snake_case_     (       self:      List[str],A_:      Optional[int],A_:      Union[str, Any] = -1,A_:      Any = -1,A_:      int = False,A_:      Optional[Any] = None,):






            '''simple docstring'''




            __UpperCamelCase      =       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
            __UpperCamelCase      =       tokenizer.num_special_tokens_to_add(__lowerCAmelCase )
            __UpperCamelCase      =       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
            __UpperCamelCase      =       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
            __UpperCamelCase      =       dict(tokenizer(__lowerCAmelCase,return_tensors=__lowerCAmelCase ) )
            return common_inputs







       def 						snake_case_     (       self:      Tuple,A_:      Optional[int],A_:      Optional[int] = -1,A_:      Union[str, Any] = -1,A_:      Any = False,A_:      List[Any] = None,):






            '''simple docstring'''




            if self.task in ["default", "seq2seq-lm"]:
                 __UpperCamelCase      =       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":
                 __UpperCamelCase      =       self._generate_dummy_inputs_for_causal_lm(
                     __lowerCAmelCase,batch_size=__lowerCAmelCase,seq_length=__lowerCAmelCase,is_pair=__lowerCAmelCase,framework=__lowerCAmelCase )
            else:
                 __UpperCamelCase      =       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 						snake_case_     (       self:      List[Any],A_:      List[str],A_:      Dict,A_:      List[str],A_:      Optional[Any] ):






            '''simple docstring'''




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



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase          =  {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

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




