

import functools
import operator

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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 			A						(							__lowerCamelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'sew'


    def __init__(				self			:	Optional[Any],lowercase_			:	Union[str, Any]=3_2,lowercase_			:	str=7_6_8,lowercase_			:	Tuple=1_2,lowercase_			:	Any=1_2,lowercase_			:	Union[str, Any]=3_0_7_2,lowercase_			:	Optional[int]=2,lowercase_			:	str="gelu",lowercase_			:	List[str]=0.1,lowercase_			:	List[str]=0.1,lowercase_			:	Optional[Any]=0.1,lowercase_			:	Tuple=0.0,lowercase_			:	Any=0.1,lowercase_			:	Union[str, Any]=0.1,lowercase_			:	List[Any]=0.02,lowercase_			:	Optional[int]=1E-5,lowercase_			:	str="group",lowercase_			:	Optional[Any]="gelu",lowercase_			:	Dict=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2),lowercase_			:	Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),lowercase_			:	Optional[Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),lowercase_			:	Tuple=False,lowercase_			:	str=1_2_8,lowercase_			:	Any=1_6,lowercase_			:	Optional[int]=True,lowercase_			:	Union[str, Any]=0.05,lowercase_			:	Tuple=1_0,lowercase_			:	str=2,lowercase_			:	Any=0.0,lowercase_			:	Optional[int]=1_0,lowercase_			:	str=0,lowercase_			:	Union[str, Any]="mean",lowercase_			:	str=False,lowercase_			:	Optional[int]=False,lowercase_			:	Any=2_5_6,lowercase_			:	Tuple=0,lowercase_			:	Any=1,lowercase_			:	Tuple=2,**lowercase_			:	Tuple,)->   int:





        '''simple docstring'''







        super().__init__(**UpperCamelCase_,pad_token_id=UpperCamelCase_,bos_token_id=UpperCamelCase_,eos_token_id=UpperCamelCase_		)
        A__   				=       hidden_size
        A__   				=       feat_extract_norm
        A__   				=       feat_extract_activation
        A__   				=       list(UpperCamelCase_		)
        A__   				=       list(UpperCamelCase_		)
        A__   				=       list(UpperCamelCase_		)
        A__   				=       conv_bias
        A__   				=       num_conv_pos_embeddings
        A__   				=       num_conv_pos_embedding_groups
        A__   				=       len(self.conv_dim		)
        A__   				=       num_hidden_layers
        A__   				=       intermediate_size
        A__   				=       squeeze_factor
        A__   				=       hidden_act
        A__   				=       num_attention_heads
        A__   				=       hidden_dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       feat_proj_dropout
        A__   				=       final_dropout
        A__   				=       layerdrop
        A__   				=       layer_norm_eps
        A__   				=       initializer_range
        A__   				=       vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                'Configuration for convolutional layers is incorrect.'
                'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                F'but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'
                F'= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        A__   				=       apply_spec_augment
        A__   				=       mask_time_prob
        A__   				=       mask_time_length
        A__   				=       mask_time_min_masks
        A__   				=       mask_feature_prob
        A__   				=       mask_feature_length
        A__   				=       mask_feature_min_masks

        # ctc loss
        A__   				=       ctc_loss_reduction
        A__   				=       ctc_zero_infinity

        # sequence classification
        A__   				=       use_weighted_layer_sum
        A__   				=       classifier_proj_size


    @property
    def 						snake_case__					(				self			:	str		)->   Any:





        '''simple docstring'''







        return functools.reduce(operator.mul,self.conv_stride,1		)




import warnings
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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'bart'
    lowerCamelCase		     =							['past_key_values']
    lowerCamelCase		     =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(				self			:	Tuple,lowercase_			:	Optional[int]=5_0_2_6_5,lowercase_			:	List[str]=1_0_2_4,lowercase_			:	Any=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	str=1_6,lowercase_			:	int=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	Any=1_6,lowercase_			:	Any=0.0,lowercase_			:	str=0.0,lowercase_			:	Optional[Any]="gelu",lowercase_			:	List[str]=1_0_2_4,lowercase_			:	List[Any]=0.1,lowercase_			:	Union[str, Any]=0.0,lowercase_			:	Optional[int]=0.0,lowercase_			:	List[Any]=0.02,lowercase_			:	int=0.0,lowercase_			:	Optional[Any]=False,lowercase_			:	List[Any]=True,lowercase_			:	Union[str, Any]=3,lowercase_			:	int=1,lowercase_			:	int=0,lowercase_			:	List[str]=2,lowercase_			:	Optional[int]=True,lowercase_			:	Tuple=2,lowercase_			:	List[str]=2,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        A__   				=       vocab_size
        A__   				=       max_position_embeddings
        A__   				=       d_model
        A__   				=       encoder_ffn_dim
        A__   				=       encoder_layers
        A__   				=       encoder_attention_heads
        A__   				=       decoder_ffn_dim
        A__   				=       decoder_layers
        A__   				=       decoder_attention_heads
        A__   				=       dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       activation_function
        A__   				=       init_std
        A__   				=       encoder_layerdrop
        A__   				=       decoder_layerdrop
        A__   				=       classifier_dropout
        A__   				=       use_cache
        A__   				=       encoder_layers
        A__   				=       scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_		):
            A__   				=       self.bos_token_id
            warnings.warn(
                F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'		)




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





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





        '''simple docstring'''







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

            if self.use_past:
                A__   				=       {0: 'batch'}
                A__   				=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}
                A__   				=       {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.
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__   				=       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]]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__   				=       seq_length if not self.use_past else 1
        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__   				=       {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__   				=       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
            A__				,						A__   				=       common_inputs['input_ids'].shape
            A__   				=       common_inputs['decoder_input_ids'].shape[1]
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__   				=       decoder_seq_length + 3
            A__   				=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

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

            A__   				=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__				,						A__   				=       self.num_layers
            A__   				=       min(lowercase_,lowercase_		)
            A__   				=       max(lowercase_,lowercase_		) - min_num_layers
            A__   				=       '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.
            A__   				=       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 						snake_case__					(				self			:	List[str],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       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
            A__				,						A__   				=       common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__   				=       seqlen + 2
            A__				,						A__   				=       self.num_layers
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

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


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       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":
            A__   				=       self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__   				=       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 						snake_case__					(				self			:	int,lowercase_			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str		)->   str:





        '''simple docstring'''







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

