

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    '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 					_UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='bart'
			lowerCamelCase__           =['past_key_values']
			lowerCamelCase__           ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(			self							: Dict      ,      a							: Optional[Any]=5_0265      ,      a							: str=1024      ,      a							: List[Any]=12      ,      a							: Optional[Any]=4096      ,      a							: Any=16      ,      a							: int=12      ,      a							: Union[str, Any]=4096      ,      a							: Any=16      ,      a							: Union[str, Any]=0.0      ,      a							: str=0.0      ,      a							: str="gelu"      ,      a							: Any=1024      ,      a							: Optional[Any]=0.1      ,      a							: Dict=0.0      ,      a							: Optional[Any]=0.0      ,      a							: int=0.02      ,      a							: List[Any]=0.0      ,      a							: Optional[int]=False      ,      a							: Optional[Any]=True      ,      a							: Tuple=3      ,      a							: Optional[Any]=1      ,      a							: str=0      ,      a							: Union[str, Any]=2      ,      a							: List[str]=True      ,      a							: Union[str, Any]=2      ,      a							: Dict=2      ,      **a							: Optional[Any]      ,      )							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      str				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       d_model
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_ffn_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_layers
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_attention_heads
				SCREAMING_SNAKE_CASE   :      str				     =       decoder_ffn_dim
				SCREAMING_SNAKE_CASE   :      Dict				     =       decoder_layers
				SCREAMING_SNAKE_CASE   :      List[str]				     =       decoder_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       dropout
				SCREAMING_SNAKE_CASE   :      List[str]				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      Any				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      Dict				     =       activation_function
				SCREAMING_SNAKE_CASE   :      Any				     =       init_std
				SCREAMING_SNAKE_CASE   :      Dict				     =       encoder_layerdrop
				SCREAMING_SNAKE_CASE   :      Dict				     =       decoder_layerdrop
				SCREAMING_SNAKE_CASE   :      List[str]				     =       classifier_dropout
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_cache
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       encoder_layers
				SCREAMING_SNAKE_CASE   :      str				     =       scale_embedding  # scale factor will be sqrt(d_model) if True

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

				# ensure backward compatibility for BART CNN models
				if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"      ,      a	):
					SCREAMING_SNAKE_CASE   :      str				     =       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 					_UpperCamelCase							(							__A   ):
			'''simple docstring'''







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

				"""simple docstring"""

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

					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Dict				     =       {0: "batch"}
						SCREAMING_SNAKE_CASE   :      str				     =       {0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       {0: "batch", 1: "decoder_sequence"}
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       {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.
					SCREAMING_SNAKE_CASE   :      List[str]				     =       OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]	)
					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Dict				     =       self.num_layers
						for i in range(a	):
							SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE   :      Tuple				     =       {0: "batch", 2: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      str				     =       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							: Tuple	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       super().outputs
				else:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       super(a      ,      self	).outputs
					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.num_layers
						for i in range(a	):
							SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE   :      List[str]				     =       {0: "batch", 2: "past_sequence + sequence"}
				return common_outputs
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a      ,      a      ,      a      ,      a      ,      a	)

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

					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(a      ,      a	)]      ,      dim=1	)

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.num_layers
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       max(a      ,      a	) - min_num_layers
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "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.
					SCREAMING_SNAKE_CASE   :      List[str]				     =       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 							__UpperCamelCase (			self							: str      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       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
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       seqlen + 2
					SCREAMING_SNAKE_CASE   :      Any				     =       self.num_layers
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.num_attention_heads
					SCREAMING_SNAKE_CASE   :      str				     =       (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      Dict				     =       torch.cat(
					    [common_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)
					SCREAMING_SNAKE_CASE   :      Any				     =       [
					    (torch.zeros(a	), torch.zeros(a	)) for _ in range(a	)
					]
				return common_inputs
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       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
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.num_special_tokens_to_add(a	)
				SCREAMING_SNAKE_CASE   :      int				     =       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
				SCREAMING_SNAKE_CASE   :      str				     =       [" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       dict(tokenizer(a      ,      return_tensors=a	)	)
				return common_inputs
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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":
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self._generate_dummy_inputs_for_causal_lm(
					    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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 							__UpperCamelCase (			self							: List[str]      ,      a							: str      ,      a							: Union[str, Any]      ,      a							: List[str]      ,      a							: Optional[int]	)							->       Dict:

				"""simple docstring"""

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


import functools
import operator

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='wavlm'
			def __init__(			self							: Optional[int]      ,      a							: Optional[Any]=32      ,      a							: int=768      ,      a							: Tuple=12      ,      a							: List[str]=12      ,      a							: str=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.1      ,      a							: int=0.1      ,      a							: str=0.1      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.1      ,      a							: Any=0.1      ,      a							: List[str]=0.02      ,      a							: List[Any]=1e-5      ,      a							: Any="group"      ,      a							: Optional[int]="gelu"      ,      a							: List[str]=(512, 512, 512, 512, 512, 512, 512)      ,      a							: Any=(5, 2, 2, 2, 2, 2, 2)      ,      a							: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)      ,      a							: Optional[Any]=False      ,      a							: Dict=128      ,      a							: Optional[Any]=16      ,      a							: Optional[Any]=320      ,      a							: str=800      ,      a							: Optional[int]=False      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.05      ,      a							: Any=10      ,      a							: Optional[int]=2      ,      a							: Dict=0.0      ,      a							: str=10      ,      a							: Tuple=320      ,      a							: Optional[int]=2      ,      a							: int=0.1      ,      a							: List[str]=100      ,      a							: Tuple=256      ,      a							: str=256      ,      a							: Tuple=0.1      ,      a							: str="mean"      ,      a							: int=False      ,      a							: int=False      ,      a							: Optional[Any]=256      ,      a							: Any=(512, 512, 512, 512, 1500)      ,      a							: Tuple=(5, 3, 3, 1, 1)      ,      a							: str=(1, 2, 3, 1, 1)      ,      a							: Optional[Any]=512      ,      a							: Optional[Any]=80      ,      a							: Tuple=0      ,      a							: Any=1      ,      a							: Optional[Any]=2      ,      a							: int=False      ,      a							: Dict=3      ,      a							: Any=2      ,      a							: List[Any]=3      ,      a							: int=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(**a      ,      pad_token_id=a      ,      bos_token_id=a      ,      eos_token_id=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       feat_extract_norm
				SCREAMING_SNAKE_CASE   :      Any				     =       feat_extract_activation
				SCREAMING_SNAKE_CASE   :      Any				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       conv_bias
				SCREAMING_SNAKE_CASE   :      str				     =       num_buckets
				SCREAMING_SNAKE_CASE   :      str				     =       max_bucket_distance
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_conv_pos_embeddings
				SCREAMING_SNAKE_CASE   :      Any				     =       num_conv_pos_embedding_groups
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(self.conv_dim	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      List[str]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      List[str]				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      int				     =       feat_proj_dropout
				SCREAMING_SNAKE_CASE   :      Any				     =       final_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layerdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_norm_eps
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_ctc_classes
				SCREAMING_SNAKE_CASE   :      Tuple				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       do_stable_layer_norm
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_weighted_layer_sum
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_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)`, but is `len(config.conv_dim) ="
					    F" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"
					    F" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				SCREAMING_SNAKE_CASE   :      int				     =       apply_spec_augment
				SCREAMING_SNAKE_CASE   :      int				     =       mask_time_prob
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mask_time_length
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       mask_time_min_masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       mask_feature_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mask_feature_length

				# parameters for pretraining with codevector quantized representations
				SCREAMING_SNAKE_CASE   :      str				     =       num_codevectors_per_group
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_codevector_groups
				SCREAMING_SNAKE_CASE   :      Tuple				     =       contrastive_logits_temperature
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_negatives
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       codevector_dim
				SCREAMING_SNAKE_CASE   :      int				     =       proj_codevector_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       diversity_loss_weight

				# ctc loss
				SCREAMING_SNAKE_CASE   :      Any				     =       ctc_loss_reduction
				SCREAMING_SNAKE_CASE   :      Dict				     =       ctc_zero_infinity

				# adapter
				SCREAMING_SNAKE_CASE   :      Any				     =       add_adapter
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       adapter_kernel_size
				SCREAMING_SNAKE_CASE   :      Any				     =       adapter_stride
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_adapter_layers
				SCREAMING_SNAKE_CASE   :      int				     =       output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       xvector_output_dim






			@property
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

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