


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


_UpperCAmelCase	:     Any   =logging.get_logger(__name__)

_UpperCAmelCase	:     int   ={
    '''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  snake_case__(							_lowerCAmelCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Union[str, Any]    =							"""speech_to_text_2"""
						SCREAMING_SNAKE_CASE__					:     Any    =							["""past_key_values"""]
						SCREAMING_SNAKE_CASE__					:     Any    =							{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}




						def __init__( self      ,  __lowercase=1_0_0_0_0      ,  __lowercase=6      ,  __lowercase=2_0_4_8      ,  __lowercase=4      ,  __lowercase=0.0      ,  __lowercase=True      ,  __lowercase="relu"      ,  __lowercase=2_5_6      ,  __lowercase=0.1      ,  __lowercase=0.0      ,  __lowercase=0.0      ,  __lowercase=0.02      ,  __lowercase=2      ,  __lowercase=True      ,  __lowercase=1      ,  __lowercase=0      ,  __lowercase=2      ,  __lowercase=1_0_2_4      ,  **__lowercase      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					str			 = vocab_size
										lowerCAmelCase_       :					str			 = d_model
										lowerCAmelCase_       :					List[str]			 = decoder_ffn_dim
										lowerCAmelCase_       :					Tuple			 = decoder_layers
										lowerCAmelCase_       :					List[str]			 = decoder_attention_heads
										lowerCAmelCase_       :					Tuple			 = dropout
										lowerCAmelCase_       :					Optional[int]			 = attention_dropout
										lowerCAmelCase_       :					Optional[int]			 = activation_dropout
										lowerCAmelCase_       :					int			 = activation_function
										lowerCAmelCase_       :					int			 = init_std
										lowerCAmelCase_       :					Optional[int]			 = decoder_layerdrop
										lowerCAmelCase_       :					str			 = use_cache
										lowerCAmelCase_       :					List[Any]			 = decoder_layers
										lowerCAmelCase_       :					Union[str, Any]			 = scale_embedding  # scale factor will be sqrt(d_model) if True
										lowerCAmelCase_       :					Optional[Any]			 = max_target_positions

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






'''simple docstring'''


lowerCAmelCase					:     str												='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase					:     int												=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase					:     List[str]												={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


