
"""simple docstring"""




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


_A			:       Any   		=       logging.get_logger(__name__)

_A			:       int   		=       {
    """facebook/s2t-small-librispeech-asr""": (
        """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				"""speech_to_text"""
   __lowerCAmelCase            =				["""past_key_values"""]
   __lowerCAmelCase            =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
   def __init__(			self	,    _a=10_000	,    _a=12	,    _a=2_048	,    _a=4	,    _a=6	,    _a=2_048	,    _a=4	,    _a=0.0	,    _a=0.0	,    _a=True	,    _a=True	,    _a="relu"	,    _a=256	,    _a=0.1	,    _a=0.0	,    _a=0.0	,    _a=0.0_2	,    _a=2	,    _a=True	,    _a=1	,    _a=0	,    _a=2	,    _a=6_000	,    _a=1_024	,    _a=2	,    _a=(5, 5)	,    _a=1_024	,    _a=80	,    _a=1	,    **_a	,    ):
          lowercase    :				str       		=							vocab_size
          lowercase    :				List[Any]       		=							d_model
          lowercase    :				Optional[int]       		=							encoder_ffn_dim
          lowercase    :				List[str]       		=							encoder_layers
          lowercase    :				Tuple       		=							encoder_attention_heads
          lowercase    :				Dict       		=							decoder_ffn_dim
          lowercase    :				List[str]       		=							decoder_layers
          lowercase    :				Dict       		=							decoder_attention_heads
          lowercase    :				Union[str, Any]       		=							dropout
          lowercase    :				Optional[Any]       		=							attention_dropout
          lowercase    :				Any       		=							activation_dropout
          lowercase    :				Any       		=							activation_function
          lowercase    :				Union[str, Any]       		=							init_std
          lowercase    :				int       		=							encoder_layerdrop
          lowercase    :				Optional[Any]       		=							decoder_layerdrop
          lowercase    :				Tuple       		=							use_cache
          lowercase    :				List[Any]       		=							encoder_layers
          lowercase    :				Union[str, Any]       		=							scale_embedding  # scale factor will be sqrt(d_model) if True
          lowercase    :				Optional[Any]       		=							max_source_positions
          lowercase    :				Optional[int]       		=							max_target_positions
          lowercase    :				Optional[Any]       		=							num_conv_layers
          lowercase    :				Tuple       		=							list(_a				)
          lowercase    :				int       		=							conv_channels
          lowercase    :				Optional[int]       		=							input_feat_per_channel
          lowercase    :				Tuple       		=							input_channels

          if len(self.conv_kernel_sizes				) != self.num_conv_layers:
                 raise ValueError(
                     "Configuration for convolutional module is incorrect. "
                     "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
                     f"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes				)}`, """
                     f"""`config.num_conv_layers = {self.num_conv_layers}`."""				)

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




"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


