



import functools
import operator

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


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Any      =  {
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}






class 					UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Optional[Any]				=	"""unispeech-sat"""



	def __init__(       self			,	UpperCAmelCase__=32			,	UpperCAmelCase__=768			,	UpperCAmelCase__=12			,	UpperCAmelCase__=12			,	UpperCAmelCase__=3_072			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__="group"			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=(512, 512, 512, 512, 512, 512, 512)			,	UpperCAmelCase__=(5, 2, 2, 2, 2, 2, 2)			,	UpperCAmelCase__=(10, 3, 3, 3, 3, 2, 2)			,	UpperCAmelCase__=False			,	UpperCAmelCase__=128			,	UpperCAmelCase__=16			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0.05			,	UpperCAmelCase__=10			,	UpperCAmelCase__=2			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=10			,	UpperCAmelCase__=0			,	UpperCAmelCase__=320			,	UpperCAmelCase__=2			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=100			,	UpperCAmelCase__=256			,	UpperCAmelCase__=256			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__="mean"			,	UpperCAmelCase__=False			,	UpperCAmelCase__=False			,	UpperCAmelCase__=256			,	UpperCAmelCase__=(512, 512, 512, 512, 1_500)			,	UpperCAmelCase__=(5, 3, 3, 1, 1)			,	UpperCAmelCase__=(1, 2, 3, 1, 1)			,	UpperCAmelCase__=512			,	UpperCAmelCase__=0			,	UpperCAmelCase__=1			,	UpperCAmelCase__=2			,	UpperCAmelCase__=504			,	**UpperCAmelCase__			,	):
							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__							=       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
							A__							=       num_clusters
							A__							=       do_stable_layer_norm
							A__							=       use_weighted_layer_sum

							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
							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

							# parameters for pretraining with codevector quantized representations
							A__							=       num_codevectors_per_group
							A__							=       num_codevector_groups
							A__							=       contrastive_logits_temperature
							A__							=       feat_quantizer_dropout
							A__							=       num_negatives
							A__							=       codevector_dim
							A__							=       proj_codevector_dim
							A__							=       diversity_loss_weight

							# ctc loss
							A__							=       ctc_loss_reduction
							A__							=       ctc_zero_infinity

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							A__							=       classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							A__							=       list(UpperCAmelCase__		)
							A__							=       list(UpperCAmelCase__		)
							A__							=       list(UpperCAmelCase__		)
							A__							=       xvector_output_dim



	@property
	def 			__A							(       self		):
							return functools.reduce(operator.mul			,	self.conv_stride			,	1		)










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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
													raise ValueError(
													    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"		)



	def 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) or len(self.rope_scaling		) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    F"""got {self.rope_scaling}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





