





from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class     snake_case_		(   a,		a,		a							):






 '''simple docstring'''





 __UpperCamelCase       =      [R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']

 @register_to_config
 def __init__(  self,		A_,		A_,		A_ = None,		A_ = 5_0257,		A_ = 1024,		A_ = 768,		A_ = 12,		A_ = 12,		A_ = None,		A_ = "gelu_new",		A_ = 0.1,		A_ = 0.1,		A_ = 0.1,		A_ = 1E-5,		A_ = 0.02,		A_ = True,		A_ = True,		A_ = False,		A_ = False,		)	->       List[Any]:
    super().__init__()

    UpperCAmelCase__						=prefix_length

    if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
       raise ValueError(
           f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
           f""" `n_embd`: {n_embd} are not equal."""		)

    UpperCAmelCase__						=prefix_inner_dim
    UpperCAmelCase__						=prefix_hidden_dim

    UpperCAmelCase__						=(
        nn.Linear(self.prefix_inner_dim,		self.prefix_hidden_dim		)
        if self.prefix_hidden_dim is not None
        else nn.Identity()
    )
    UpperCAmelCase__						=(
        nn.Linear(self.prefix_hidden_dim,		A_		) if self.prefix_hidden_dim is not None else nn.Identity()
    )

    UpperCAmelCase__						=GPTaConfig(
        vocab_size=A_,		n_positions=A_,		n_embd=A_,		n_layer=A_,		n_head=A_,		n_inner=A_,		activation_function=A_,		resid_pdrop=A_,		embd_pdrop=A_,		attn_pdrop=A_,		layer_norm_epsilon=A_,		initializer_range=A_,		scale_attn_weights=A_,		use_cache=A_,		scale_attn_by_inverse_layer_idx=A_,		reorder_and_upcast_attn=A_,		)
    UpperCAmelCase__						=GPTaLMHeadModel(A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_ = None,		A_ = None,		)	->       Optional[int]:
    UpperCAmelCase__						=self.transformer.transformer.wte(A_		)
    UpperCAmelCase__						=self.encode_prefix(A_		)
    UpperCAmelCase__						=self.decode_prefix(A_		)
    UpperCAmelCase__						=torch.cat((prefix_embeds, embedding_text),		dim=1		)

    if labels is not None:
       UpperCAmelCase__						=self.get_dummy_token(input_ids.shape[0],		input_ids.device		)
       UpperCAmelCase__						=torch.cat((dummy_token, input_ids),		dim=1		)
    UpperCAmelCase__						=self.transformer(inputs_embeds=A_,		labels=A_,		attention_mask=A_		)
    if self.prefix_hidden_dim is not None:
       return out, hidden
    else:
       return out

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       torch.Tensor:
    return torch.zeros(A_,		self.prefix_length,		dtype=torch.intaa,		device=A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       Tuple:
    return self.encode_prefix(A_		)

 @torch.no_grad()
 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       List[str]:
    UpperCAmelCase__						=torch.split(A_,		1,		dim=0		)
    UpperCAmelCase__						=[]
    UpperCAmelCase__						=[]
    for feature in features:
       UpperCAmelCase__						=self.decode_prefix(feature.to(A_		)		)  # back to the clip feature
       # Only support beam search for now
       UpperCAmelCase__			,							UpperCAmelCase__						=self.generate_beam(
           input_embeds=A_,		device=A_,		eos_token_id=A_		)
       generated_tokens.append(output_tokens[0]		)
       generated_seq_lengths.append(seq_lengths[0]		)
    UpperCAmelCase__						=torch.stack(A_		)
    UpperCAmelCase__						=torch.stack(A_		)
    return generated_tokens, generated_seq_lengths






 @torch.no_grad()
 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=None,		A_ = 5,		A_ = 67,		A_ = 1.0,		A_ = None,		)	->       Any:
    UpperCAmelCase__						=eos_token_id
    UpperCAmelCase__						=None
    UpperCAmelCase__						=None
    UpperCAmelCase__						=torch.ones(A_,		device=A_,		dtype=torch.int		)
    UpperCAmelCase__						=torch.zeros(A_,		device=A_,		dtype=torch.bool		)

    if input_embeds is not None:
       UpperCAmelCase__						=input_embeds
    else:
       UpperCAmelCase__						=self.transformer.transformer.wte(A_		)

    for i in range(A_		):
       UpperCAmelCase__						=self.transformer(inputs_embeds=A_		)
       UpperCAmelCase__						=outputs.logits
       UpperCAmelCase__						=logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
       UpperCAmelCase__						=logits.softmax(-1		).log()

       if scores is None:
          UpperCAmelCase__			,							UpperCAmelCase__						=logits.topk(A_,		-1		)
          UpperCAmelCase__						=generated.expand(A_,		*generated.shape[1:]		)
          UpperCAmelCase__			,							UpperCAmelCase__						=next_tokens.permute(1,		0		), scores.squeeze(0		)
          if tokens is None:
             UpperCAmelCase__						=next_tokens
          else:
             UpperCAmelCase__						=tokens.expand(A_,		*tokens.shape[1:]		)
             UpperCAmelCase__						=torch.cat((tokens, next_tokens),		dim=1		)
       else:
          UpperCAmelCase__						=-float(np.inf		)
          UpperCAmelCase__						=0
          UpperCAmelCase__						=scores[:, None] + logits
          seq_lengths[~is_stopped] += 1
          UpperCAmelCase__						=scores_sum / seq_lengths[:, None]
          UpperCAmelCase__			,							UpperCAmelCase__						=scores_sum_average.view(-1		).topk(A_,		-1		)
          UpperCAmelCase__						=next_tokens // scores_sum.shape[1]
          UpperCAmelCase__						=seq_lengths[next_tokens_source]
          UpperCAmelCase__						=next_tokens % scores_sum.shape[1]
          UpperCAmelCase__						=next_tokens.unsqueeze(1		)
          UpperCAmelCase__						=tokens[next_tokens_source]
          UpperCAmelCase__						=torch.cat((tokens, next_tokens),		dim=1		)
          UpperCAmelCase__						=generated[next_tokens_source]
          UpperCAmelCase__						=scores_sum_average * seq_lengths
          UpperCAmelCase__						=is_stopped[next_tokens_source]

       UpperCAmelCase__						=self.transformer.transformer.wte(next_tokens.squeeze()		).view(generated.shape[0],		1,		-1		)
       UpperCAmelCase__						=torch.cat((generated, next_token_embed),		dim=1		)
       UpperCAmelCase__						=is_stopped + next_tokens.eq(A_		).squeeze()
       if is_stopped.all():
          break

    UpperCAmelCase__						=scores / seq_lengths
    UpperCAmelCase__						=scores.argsort(descending=A_		)
    # tokens tensors are already padded to max_seq_length
    UpperCAmelCase__						=[tokens[i] for i in order]
    UpperCAmelCase__						=torch.stack(A_,		dim=0		)
    UpperCAmelCase__						=torch.tensor([seq_lengths[i] for i in order],		dtype=seq_lengths.dtype		)
    return output_texts, seq_lengths










import functools
import operator

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    '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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'wavlm'

 def __init__(  self,		A_=32,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=0.1,		A_=0.0,		A_=0.1,		A_=0.1,		A_=0.02,		A_=1E-5,		A_="group",		A_="gelu",		A_=(512, 512, 512, 512, 512, 512, 512),		A_=(5, 2, 2, 2, 2, 2, 2),		A_=(10, 3, 3, 3, 3, 2, 2),		A_=False,		A_=128,		A_=16,		A_=320,		A_=800,		A_=False,		A_=True,		A_=0.05,		A_=10,		A_=2,		A_=0.0,		A_=10,		A_=320,		A_=2,		A_=0.1,		A_=100,		A_=256,		A_=256,		A_=0.1,		A_="mean",		A_=False,		A_=False,		A_=256,		A_=(512, 512, 512, 512, 1500),		A_=(5, 3, 3, 1, 1),		A_=(1, 2, 3, 1, 1),		A_=512,		A_=80,		A_=0,		A_=1,		A_=2,		A_=False,		A_=3,		A_=2,		A_=3,		A_=None,		**A_,		)	->       Union[str, Any]:
    super().__init__(**A_,		pad_token_id=A_,		bos_token_id=A_,		eos_token_id=A_		)
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=feat_extract_norm
    UpperCAmelCase__						=feat_extract_activation
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=conv_bias
    UpperCAmelCase__						=num_buckets
    UpperCAmelCase__						=max_bucket_distance
    UpperCAmelCase__						=num_conv_pos_embeddings
    UpperCAmelCase__						=num_conv_pos_embedding_groups
    UpperCAmelCase__						=len(self.conv_dim		)
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_dropout
    UpperCAmelCase__						=attention_dropout
    UpperCAmelCase__						=activation_dropout
    UpperCAmelCase__						=feat_proj_dropout
    UpperCAmelCase__						=final_dropout
    UpperCAmelCase__						=layerdrop
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=num_ctc_classes
    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=do_stable_layer_norm
    UpperCAmelCase__						=use_weighted_layer_sum
    UpperCAmelCase__						=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
    UpperCAmelCase__						=apply_spec_augment
    UpperCAmelCase__						=mask_time_prob
    UpperCAmelCase__						=mask_time_length
    UpperCAmelCase__						=mask_time_min_masks
    UpperCAmelCase__						=mask_feature_prob
    UpperCAmelCase__						=mask_feature_length

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

    # ctc loss
    UpperCAmelCase__						=ctc_loss_reduction
    UpperCAmelCase__						=ctc_zero_infinity

    # adapter
    UpperCAmelCase__						=add_adapter
    UpperCAmelCase__						=adapter_kernel_size
    UpperCAmelCase__						=adapter_stride
    UpperCAmelCase__						=num_adapter_layers
    UpperCAmelCase__						=output_hidden_size or hidden_size

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

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






 @property
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    return functools.reduce(operator.mul,		self.conv_stride,		1		)



