"""simple docstring"""







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


lowerCamelCase      : List[Any]       =logging.get_logger(__name__)

lowerCamelCase      : List[Any]       ={
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			"blenderbot"
    _UpperCAmelCase       =			["past_key_values"]
    _UpperCAmelCase       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(  self					,       __lowerCamelCase=8008					,       __lowerCamelCase=128					,       __lowerCamelCase=2					,       __lowerCamelCase=10240					,       __lowerCamelCase=32					,       __lowerCamelCase=24					,       __lowerCamelCase=10240					,       __lowerCamelCase=32					,       __lowerCamelCase=0.0					,       __lowerCamelCase=0.0					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase="gelu"					,       __lowerCamelCase=2560					,       __lowerCamelCase=0.1					,       __lowerCamelCase=0.0					,       __lowerCamelCase=0.0					,       __lowerCamelCase=0.02					,       __lowerCamelCase=1					,       __lowerCamelCase=False					,       __lowerCamelCase=0					,       __lowerCamelCase=1					,       __lowerCamelCase=2					,       __lowerCamelCase=3					,       __lowerCamelCase=2					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         __A					:					Tuple								=		vocab_size
         __A					:					List[str]								=		max_position_embeddings
         __A					:					Tuple								=		d_model
         __A					:					List[Any]								=		encoder_ffn_dim
         __A					:					str								=		encoder_layers
         __A					:					Optional[int]								=		encoder_attention_heads
         __A					:					Union[str, Any]								=		decoder_ffn_dim
         __A					:					Optional[Any]								=		decoder_layers
         __A					:					int								=		decoder_attention_heads
         __A					:					Union[str, Any]								=		dropout
         __A					:					str								=		attention_dropout
         __A					:					List[Any]								=		activation_dropout
         __A					:					Any								=		activation_function
         __A					:					List[Any]								=		init_std
         __A					:					Union[str, Any]								=		encoder_layerdrop
         __A					:					Union[str, Any]								=		decoder_layerdrop
         __A					:					Optional[int]								=		use_cache
         __A					:					List[str]								=		encoder_layers
         __A					:					Any								=		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					,       encoder_no_repeat_ngram_size=__lowerCamelCase					,       forced_eos_token_id=__lowerCamelCase					,       **__lowerCamelCase					,       )







class 							__snake_case(					A_	):


    '''simple docstring'''





    @property
    def 						_a							(  self	):




         '''simple docstring'''



         if self.task in ["default", "seq2seq-lm"]:
              __A					:					str								=		OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]	)
              if self.use_past:
                   __A					:					int								=		{0: 'batch'}
                   __A					:					Optional[int]								=		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   __A					:					Tuple								=		{0: 'batch', 1: 'decoder_sequence'}
                   __A					:					Optional[Any]								=		{0: 'batch', 1: 'decoder_sequence'}
              if self.use_past:
                   self.fill_with_past_key_values_(__lowerCamelCase					,       direction='inputs'	)
         elif self.task == "causal-lm":
              __A					:					int								=		OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]	)
              if self.use_past:
                   __A						,		__A					:					Dict								=		self.num_layers
                   for i in range(__lowerCamelCase	):
                        __A					:					List[str]								=		{0: 'batch', 2: 'past_sequence + sequence'}
                        __A					:					Any								=		{0: 'batch', 2: 'past_sequence + sequence'}
         else:
              __A					:					int								=		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
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def 						_a							(  self	):




         '''simple docstring'''



         if self.task in ["default", "seq2seq-lm"]:
              __A					:					Union[str, Any]								=		super().outputs
         else:
              __A					:					List[str]								=		super(__lowerCamelCase					,       self	).outputs
              if self.use_past:
                   __A						,		__A					:					List[str]								=		self.num_layers
                   for i in range(__lowerCamelCase	):
                        __A					:					Optional[Any]								=		{0: 'batch', 2: 'past_sequence + sequence'}
                        __A					:					str								=		{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = -1					,       __lowerCamelCase = -1					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       ):




         '''simple docstring'''



         __A					:					List[Any]								=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         # Generate decoder inputs
         __A					:					Union[str, Any]								=		seq_length if not self.use_past else 1
         __A					:					Union[str, Any]								=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         __A					:					int								=		{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         __A					:					Optional[int]								=		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
              __A						,		__A					:					Optional[Any]								=		common_inputs['input_ids'].shape
              __A					:					int								=		common_inputs['decoder_input_ids'].shape[1]
              __A						,		__A					:					List[Any]								=		self.num_attention_heads
              __A					:					int								=		(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              __A					:					Union[str, Any]								=		decoder_seq_length
              __A					:					Any								=		(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )
              __A					:					List[str]								=		torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(__lowerCamelCase					,       __lowerCamelCase	)]					,       dim=1	)
              __A					:					int								=		[]
              __A						,		__A					:					int								=		self.num_layers

              for _ in range(__lowerCamelCase	):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(__lowerCamelCase	),
                           torch.zeros(__lowerCamelCase	),
                           torch.zeros(__lowerCamelCase	),
                           torch.zeros(__lowerCamelCase	),
                       )	)
         return common_inputs
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = -1					,       __lowerCamelCase = -1					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       ):




         '''simple docstring'''



         __A					:					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
              __A						,		__A					:					List[Any]								=		common_inputs['input_ids'].shape
              __A					:					Union[str, Any]								=		seqlen
              __A						,		__A					:					Dict								=		self.num_layers
              __A						,		__A					:					Tuple								=		self.num_attention_heads
              __A					:					Dict								=		(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              __A					:					List[str]								=		common_inputs['attention_mask'].dtype
              __A					:					Optional[int]								=		torch.cat(
                  [common_inputs['attention_mask'], torch.ones(__lowerCamelCase					,       __lowerCamelCase					,       dtype=__lowerCamelCase	)]					,       dim=1	)
              __A					:					List[str]								=		[
                  (torch.zeros(__lowerCamelCase	), torch.zeros(__lowerCamelCase	)) for _ in range(__lowerCamelCase	)
              ]
         return common_inputs
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = -1					,       __lowerCamelCase = -1					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       ):




         '''simple docstring'''



         __A					:					Optional[int]								=		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
         __A					:					Union[str, Any]								=		tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
         __A					:					Union[str, Any]								=		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
         __A					:					Dict								=		[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
         __A					:					Optional[int]								=		dict(tokenizer(__lowerCamelCase					,       return_tensors=__lowerCamelCase	)	)
         return common_inputs
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = -1					,       __lowerCamelCase = -1					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       ):




         '''simple docstring'''



         if self.task in ["default", "seq2seq-lm"]:
              __A					:					str								=		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":
              __A					:					Dict								=		self._generate_dummy_inputs_for_causal_lm(
                  __lowerCamelCase					,       batch_size=__lowerCamelCase					,       seq_length=__lowerCamelCase					,       is_pair=__lowerCamelCase					,       framework=__lowerCamelCase	)
         else:
              __A					:					str								=		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 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



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

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if direction not in ["inputs", "outputs"]:
              raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'	)

         __A					:					List[Any]								=		'past_key_values' if direction == 'inputs' else 'present'
         __A						,		__A					:					Dict								=		self.num_layers

         __A					:					Any								=		'past_encoder_sequence'
         __A					:					Optional[Any]								=		'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

         for i in range(__lowerCamelCase	):
              __A					:					List[str]								=		{0: 'batch', 2: decoder_sequence}
              __A					:					Union[str, Any]								=		{0: 'batch', 2: decoder_sequence}
              __A					:					Union[str, Any]								=		{0: 'batch', 2: encoder_sequence}
              __A					:					Dict								=		{0: 'batch', 2: encoder_sequence}




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



