

'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_    						=       {
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""LayoutLMv3TokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """LayoutLMv3ForQuestionAnswering""",
							    """LayoutLMv3ForSequenceClassification""",
							    """LayoutLMv3ForTokenClassification""",
							    """LayoutLMv3Model""",
							    """LayoutLMv3PreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFLayoutLMv3ForQuestionAnswering""",
							    """TFLayoutLMv3ForSequenceClassification""",
							    """TFLayoutLMv3ForTokenClassification""",
							    """TFLayoutLMv3Model""",
							    """TFLayoutLMv3PreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""LayoutLMv3FeatureExtractor"""]
							UpperCamelCase_    						=       ["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_layoutlmva import (
							    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    LayoutLMvaConfig,
							    LayoutLMvaOnnxConfig,
							)
							from .processing_layoutlmva import LayoutLMvaProcessor
							from .tokenization_layoutlmva import LayoutLMvaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlmva import (
														    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMvaForQuestionAnswering,
														    LayoutLMvaForSequenceClassification,
														    LayoutLMvaForTokenClassification,
														    LayoutLMvaModel,
														    LayoutLMvaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlmva import (
														    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMvaForQuestionAnswering,
														    TFLayoutLMvaForSequenceClassification,
														    TFLayoutLMvaForTokenClassification,
														    TFLayoutLMvaModel,
														    TFLayoutLMvaPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
														from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





