


"""simple docstring"""



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


__snake_case             =   logging.get_logger(__name__)

__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 _lowerCAmelCase    (				a__     ):
    __UpperCAmelCase			:	Tuple							  =  '''speech_to_text_2'''
    __UpperCAmelCase			:	Dict							  =  ['''past_key_values''']
    __UpperCAmelCase			:	List[str]							  =  {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







    def __init__(     self						,     UpperCamelCase__=1_0000						,     UpperCamelCase__=6						,     UpperCamelCase__=2048						,     UpperCamelCase__=4						,     UpperCamelCase__=0.0						,     UpperCamelCase__=True						,     UpperCamelCase__="relu"						,     UpperCamelCase__=256						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.02						,     UpperCamelCase__=2						,     UpperCamelCase__=True						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     UpperCamelCase__=2						,     UpperCamelCase__=1024						,     **UpperCamelCase__						,     )     ->		List[str]:

        '''simple docstring'''




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

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









"""simple docstring"""



import importlib

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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				List[Any]				 =   model.encode(lowercase			)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
    snake_case   :				Union[str, Any]				 =   model.decode(lowercase			)
    return xrec







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """simple docstring"""





    if "target" not in config:
        raise KeyError("Expected key `target` to instantiate."			)
    return get_obj_from_str(config["target"]			)(**config.get("params"						,							{}			)			)







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





    if ckpt:
        snake_case   :				Dict				 =   torch.load(lowercase						,							map_location="cpu"			)
        snake_case   :				Any				 =   pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'			)
    else:
        snake_case   :				Any				 =   {"state_dict": None}
        snake_case   :				List[str]				 =   None
    snake_case   :				Dict				 =   load_model_from_config(config.model						,							pl_sd["state_dict"]						,							gpu=lowercase						,							eval_mode=lowercase			)["model"]
    return model, global_step





