

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

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}






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







			lowerCamelCase__           ="bloom"
			lowerCamelCase__           =["past_key_values"]
			lowerCamelCase__           ={
			    "num_hidden_layers": "n_layer",
			    "num_attention_heads": "n_head",
			}
			def __init__(			self							: Any      ,      a							: Union[str, Any]=25_0880      ,      a							: Optional[Any]=64      ,      a							: int=2      ,      a							: Any=8      ,      a							: Optional[int]=1e-5      ,      a							: str=0.02      ,      a							: List[str]=True      ,      a							: Dict=1      ,      a							: Tuple=2      ,      a							: Optional[Any]=False      ,      a							: List[Any]=0.0      ,      a							: List[str]=0.0      ,      a							: int=1      ,      a							: Dict=False      ,      **a							: Tuple      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				# Backward compatibility with n_embed kwarg
				SCREAMING_SNAKE_CASE   :      Tuple				     =       kwargs.pop("n_embed"      ,      lowerCamelCase__	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_size if n_embed is None else n_embed
				SCREAMING_SNAKE_CASE   :      int				     =       n_layer
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       n_head
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      Tuple				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Any				     =       use_cache
				SCREAMING_SNAKE_CASE   :      List[str]				     =       pretraining_tp
				SCREAMING_SNAKE_CASE   :      str				     =       apply_residual_connection_post_layernorm
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       attention_dropout

				SCREAMING_SNAKE_CASE   :      str				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      Tuple				     =       eos_token_id
				SCREAMING_SNAKE_CASE   :      List[str]				     =       slow_but_exact

				super().__init__(bos_token_id=lowerCamelCase__      ,      eos_token_id=lowerCamelCase__      ,      **lowerCamelCase__	)






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







			lowerCamelCase__           =version.parse('1.12'   )
			def __init__(			self							: int      ,      a							: PretrainedConfig      ,      a							: str = "default"      ,      a							: List[PatchingSpec] = None      ,      a							: bool = False      ,      )							->       Tuple:

				"""simple docstring"""

				super().__init__(lowerCamelCase__      ,      task=lowerCamelCase__      ,      patching_specs=lowerCamelCase__      ,      use_past=lowerCamelCase__	)
				if not getattr(self._config      ,      "pad_token_id"      ,      lowerCamelCase__	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      List[str]				     =       0
			@property
			def 							__UpperCamelCase (			self							: int	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
					self.fill_with_past_key_values_(lowerCamelCase__      ,      direction="inputs"      ,      inverted_values_shape=lowerCamelCase__	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       {0: '''batch''', 1: '''past_sequence + sequence'''}
				else:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {0: '''batch''', 1: '''sequence'''}

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

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			@property
			def 							__UpperCamelCase (			self							: int	)							->       float:

				"""simple docstring"""

				return 1e-3
			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   :      List[str]				     =       super(lowerCamelCase__      ,      self	).generate_dummy_inputs(
				    lowerCamelCase__      ,      batch_size=lowerCamelCase__      ,      seq_length=lowerCamelCase__      ,      is_pair=lowerCamelCase__      ,      framework=lowerCamelCase__	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OrderedDict({"input_ids": common_inputs["input_ids"]}	)

				# Need to add the past_keys
				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
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      Tuple				     =       self._config.hidden_size // self.num_attention_heads
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (
						    batch * self.num_attention_heads,
						    head_dim,
						    past_key_values_length,
						)
						SCREAMING_SNAKE_CASE   :      Dict				     =       (
						    batch * self.num_attention_heads,
						    past_key_values_length,
						    head_dim,
						)
						SCREAMING_SNAKE_CASE   :      Any				     =       [
						    (torch.zeros(lowerCamelCase__	), torch.zeros(lowerCamelCase__	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       common_inputs['''attention_mask''']
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       ordered_inputs['''attention_mask'''].dtype
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(lowerCamelCase__      ,      lowerCamelCase__      ,      dtype=lowerCamelCase__	)]      ,      dim=1	)

				return ordered_inputs






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

				"""simple docstring"""

				return 13


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	)