



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						List[str]    							=      {
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= 'beit'

					def __init__(    self				,       lowerCamelCase__=8_1_9_2				,       lowerCamelCase__=7_6_8				,       lowerCamelCase__=1_2				,       lowerCamelCase__=1_2				,       lowerCamelCase__=3_0_7_2				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=1e-12				,       lowerCamelCase__=2_2_4				,       lowerCamelCase__=1_6				,       lowerCamelCase__=3				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=True				,       lowerCamelCase__=[3, 5, 7, 1_1]				,       lowerCamelCase__=[1, 2, 3, 6]				,       lowerCamelCase__=True				,       lowerCamelCase__=0.4				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=1				,       lowerCamelCase__=False				,       lowerCamelCase__=2_5_5				,       **lowerCamelCase__				,       ):
									super().__init__(**lowerCamelCase__   )

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				layer_norm_eps

									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				use_mask_token
									_lowerCamelCase        =				use_absolute_position_embeddings
									_lowerCamelCase        =				use_relative_position_bias
									_lowerCamelCase        =				use_shared_relative_position_bias
									_lowerCamelCase        =				layer_scale_init_value
									_lowerCamelCase        =				drop_path_rate
									_lowerCamelCase        =				use_mean_pooling
									# decode head attributes (semantic segmentation)
									_lowerCamelCase        =				out_indices
									_lowerCamelCase        =				pool_scales
									# auxiliary head attributes (semantic segmentation)
									_lowerCamelCase        =				use_auxiliary_head
									_lowerCamelCase        =				auxiliary_loss_weight
									_lowerCamelCase        =				auxiliary_channels
									_lowerCamelCase        =				auxiliary_num_convs
									_lowerCamelCase        =				auxiliary_concat_input
									_lowerCamelCase        =				semantic_loss_ignore_index


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Dict										= version.parse('1.11'    )

					@property
					def 			snake_case__      (    self   ):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									    ]   )





					@property
					def 			snake_case__      (    self   ):
									return 1e-4





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
