

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    '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 				a  (	UpperCAmelCase				):
						_lowercase    						=			"speech_to_text_2"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}


						def __init__(       self						,					A_=10000						,					A_=6						,					A_=2048						,					A_=4						,					A_=0.0						,					A_=True						,					A_="relu"						,					A_=256						,					A_=0.1						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=2						,					A_=True						,					A_=1						,					A_=0						,					A_=2						,					A_=1024						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    vocab_size
										_UpperCAmelCase       :		Any 		=    d_model
										_UpperCAmelCase       :		Any 		=    decoder_ffn_dim
										_UpperCAmelCase       :		Dict 		=    decoder_layers
										_UpperCAmelCase       :		Dict 		=    decoder_attention_heads
										_UpperCAmelCase       :		Tuple 		=    dropout
										_UpperCAmelCase       :		Any 		=    attention_dropout
										_UpperCAmelCase       :		Any 		=    activation_dropout
										_UpperCAmelCase       :		int 		=    activation_function
										_UpperCAmelCase       :		Union[str, Any] 		=    init_std
										_UpperCAmelCase       :		Dict 		=    decoder_layerdrop
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		Optional[Any] 		=    decoder_layers
										_UpperCAmelCase       :		List[Any] 		=    scale_embedding  # scale factor will be sqrt(d_model) if True
										_UpperCAmelCase       :		Union[str, Any] 		=    max_target_positions

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

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


