


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[List[PIL.Image.Image], np.ndarray]
    A_  :     Optional[List[bool]]


if is_transformers_available() and is_torch_available():
       from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



import functools
import operator

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


__UpperCamelCase				:  Union[str, Any]      					=							logging.get_logger(__name__)

__UpperCamelCase				:  str      					=							{
    '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  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[Any]			= 'unispeech-sat'





    def __init__(	self				:   int       ,   __snake_case				:   Optional[int]=32       ,   __snake_case				:   Dict=768       ,   __snake_case				:   Optional[Any]=12       ,   __snake_case				:   Optional[int]=12       ,   __snake_case				:   Dict=3_072       ,   __snake_case				:   List[str]="gelu"       ,   __snake_case				:   Any=0.1       ,   __snake_case				:   Tuple=0.1       ,   __snake_case				:   Optional[int]=0.1       ,   __snake_case				:   Tuple=0.0       ,   __snake_case				:   List[Any]=0.0       ,   __snake_case				:   Tuple=0.1       ,   __snake_case				:   Union[str, Any]=0.1       ,   __snake_case				:   Dict=0.02       ,   __snake_case				:   Optional[Any]=1E-5       ,   __snake_case				:   Optional[int]="group"       ,   __snake_case				:   str="gelu"       ,   __snake_case				:   Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)       ,   __snake_case				:   str=(5, 2, 2, 2, 2, 2, 2)       ,   __snake_case				:   Tuple=(10, 3, 3, 3, 3, 2, 2)       ,   __snake_case				:   int=False       ,   __snake_case				:   Optional[int]=128       ,   __snake_case				:   Any=16       ,   __snake_case				:   Union[str, Any]=False       ,   __snake_case				:   Union[str, Any]=True       ,   __snake_case				:   List[Any]=0.05       ,   __snake_case				:   Dict=10       ,   __snake_case				:   int=2       ,   __snake_case				:   Optional[Any]=0.0       ,   __snake_case				:   Optional[int]=10       ,   __snake_case				:   List[Any]=0       ,   __snake_case				:   Optional[int]=320       ,   __snake_case				:   int=2       ,   __snake_case				:   Any=0.1       ,   __snake_case				:   Optional[int]=100       ,   __snake_case				:   Tuple=256       ,   __snake_case				:   List[str]=256       ,   __snake_case				:   List[Any]=0.1       ,   __snake_case				:   Tuple="mean"       ,   __snake_case				:   List[Any]=False       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   Optional[Any]=256       ,   __snake_case				:   Tuple=(512, 512, 512, 512, 1_500)       ,   __snake_case				:   Optional[int]=(5, 3, 3, 1, 1)       ,   __snake_case				:   Any=(1, 2, 3, 1, 1)       ,   __snake_case				:   int=512       ,   __snake_case				:   Optional[int]=0       ,   __snake_case				:   Dict=1       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   Union[str, Any]=504       ,   **__snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case       ,   pad_token_id=__snake_case       ,   bos_token_id=__snake_case       ,   eos_token_id=__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   hidden_size
       UpperCAmelCase_							:      int						=   feat_extract_norm
       UpperCAmelCase_							:      Dict						=   feat_extract_activation
       UpperCAmelCase_							:      Union[str, Any]						=   list(__snake_case	)
       UpperCAmelCase_							:      List[str]						=   list(__snake_case	)
       UpperCAmelCase_							:      Any						=   list(__snake_case	)
       UpperCAmelCase_							:      Any						=   conv_bias
       UpperCAmelCase_							:      List[str]						=   num_conv_pos_embeddings
       UpperCAmelCase_							:      Dict						=   num_conv_pos_embedding_groups
       UpperCAmelCase_							:      Optional[int]						=   len(self.conv_dim	)
       UpperCAmelCase_							:      List[str]						=   num_hidden_layers
       UpperCAmelCase_							:      Dict						=   intermediate_size
       UpperCAmelCase_							:      Union[str, Any]						=   hidden_act
       UpperCAmelCase_							:      str						=   num_attention_heads
       UpperCAmelCase_							:      Union[str, Any]						=   hidden_dropout
       UpperCAmelCase_							:      List[str]						=   attention_dropout
       UpperCAmelCase_							:      Optional[Any]						=   activation_dropout
       UpperCAmelCase_							:      Dict						=   feat_proj_dropout
       UpperCAmelCase_							:      Optional[Any]						=   final_dropout
       UpperCAmelCase_							:      List[Any]						=   layerdrop
       UpperCAmelCase_							:      int						=   layer_norm_eps
       UpperCAmelCase_							:      int						=   initializer_range
       UpperCAmelCase_							:      List[Any]						=   vocab_size
       UpperCAmelCase_							:      int						=   num_clusters
       UpperCAmelCase_							:      int						=   do_stable_layer_norm
       UpperCAmelCase_							:      Any						=   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
       UpperCAmelCase_							:      int						=   apply_spec_augment
       UpperCAmelCase_							:      Optional[Any]						=   mask_time_prob
       UpperCAmelCase_							:      str						=   mask_time_length
       UpperCAmelCase_							:      Any						=   mask_time_min_masks
       UpperCAmelCase_							:      str						=   mask_feature_prob
       UpperCAmelCase_							:      str						=   mask_feature_length
       UpperCAmelCase_							:      Tuple						=   mask_feature_min_masks

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

       # ctc loss
       UpperCAmelCase_							:      Any						=   ctc_loss_reduction
       UpperCAmelCase_							:      Optional[Any]						=   ctc_zero_infinity

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       UpperCAmelCase_							:      Optional[int]						=   classifier_proj_size

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





    @property
    def 					_lowerCamelCase (	self				:   List[str]	):





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