

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__		   =    {
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    ['''LayoutLMTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    [
        '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMForMaskedLM''',
        '''LayoutLMForSequenceClassification''',
        '''LayoutLMForTokenClassification''',
        '''LayoutLMForQuestionAnswering''',
        '''LayoutLMModel''',
        '''LayoutLMPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    [
        '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMForMaskedLM''',
        '''TFLayoutLMForSequenceClassification''',
        '''TFLayoutLMForTokenClassification''',
        '''TFLayoutLMForQuestionAnswering''',
        '''TFLayoutLMMainLayer''',
        '''TFLayoutLMModel''',
        '''TFLayoutLMPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase__		   =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




import functools
import operator

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


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "hubert"


       def __init__(		self			,   _UpperCamelCase=32			,   _UpperCamelCase=7_68			,   _UpperCamelCase=12			,   _UpperCamelCase=12			,   _UpperCamelCase=30_72			,   _UpperCamelCase="gelu"			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=True			,   _UpperCamelCase=0.0			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-5			,   _UpperCamelCase="group"			,   _UpperCamelCase="gelu"			,   _UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,   _UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)			,   _UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)			,   _UpperCamelCase=False			,   _UpperCamelCase=1_28			,   _UpperCamelCase=16			,   _UpperCamelCase=False			,   _UpperCamelCase=True			,   _UpperCamelCase=0.05			,   _UpperCamelCase=10			,   _UpperCamelCase=2			,   _UpperCamelCase=0.0			,   _UpperCamelCase=10			,   _UpperCamelCase=0			,   _UpperCamelCase="sum"			,   _UpperCamelCase=False			,   _UpperCamelCase=False			,   _UpperCamelCase=2_56			,   _UpperCamelCase=0			,   _UpperCamelCase=1			,   _UpperCamelCase=2			,   **_UpperCamelCase			,   )			->       Any:






              """simple docstring"""


              super().__init__(**_UpperCamelCase			,   pad_token_id=_UpperCamelCase			,   bos_token_id=_UpperCamelCase			,   eos_token_id=_UpperCamelCase      )
              __snake_case					  =       hidden_size
              __snake_case					  =       feat_extract_norm
              __snake_case					  =       feat_extract_activation
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       conv_bias
              __snake_case					  =       num_conv_pos_embeddings
              __snake_case					  =       num_conv_pos_embedding_groups
              __snake_case					  =       len(self.conv_dim      )
              __snake_case					  =       num_hidden_layers
              __snake_case					  =       intermediate_size
              __snake_case					  =       hidden_act
              __snake_case					  =       num_attention_heads
              __snake_case					  =       hidden_dropout
              __snake_case					  =       attention_dropout
              __snake_case					  =       activation_dropout
              __snake_case					  =       feat_proj_layer_norm
              __snake_case					  =       feat_proj_dropout
              __snake_case					  =       final_dropout
              __snake_case					  =       layerdrop
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       initializer_range
              __snake_case					  =       vocab_size
              __snake_case					  =       do_stable_layer_norm
              __snake_case					  =       use_weighted_layer_sum
              __snake_case					  =       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
              __snake_case					  =       apply_spec_augment
              __snake_case					  =       mask_time_prob
              __snake_case					  =       mask_time_length
              __snake_case					  =       mask_time_min_masks
              __snake_case					  =       mask_feature_prob
              __snake_case					  =       mask_feature_length
              __snake_case					  =       mask_feature_min_masks

              # ctc loss
              __snake_case					  =       ctc_loss_reduction
              __snake_case					  =       ctc_zero_infinity






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






              """simple docstring"""


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

