

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


_a:    List[str] 							=			logging.get_logger(__name__)

_a:    Any 							=			{
    """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 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'speech_to_text_2'
	SCREAMING_SNAKE_CASE__    				=     ['past_key_values']
	SCREAMING_SNAKE_CASE__    				=     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Tuple=10_000		,	lowerCAmelCase	:						int=6		,	lowerCAmelCase	:						str=2_048		,	lowerCAmelCase	:						Any=4		,	lowerCAmelCase	:						Optional[int]=0.0		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						Union[str, Any]="relu"		,	lowerCAmelCase	:						Union[str, Any]=256		,	lowerCAmelCase	:						Any=0.1		,	lowerCAmelCase	:						Tuple=0.0		,	lowerCAmelCase	:						Dict=0.0		,	lowerCAmelCase	:						int=0.02		,	lowerCAmelCase	:						Dict=2		,	lowerCAmelCase	:						Optional[int]=True		,	lowerCAmelCase	:						Optional[Any]=1		,	lowerCAmelCase	:						str=0		,	lowerCAmelCase	:						Dict=2		,	lowerCAmelCase	:						List[str]=1_024		,	**lowerCAmelCase	:						str		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			d_model
		UpperCAmelCase_       				=			decoder_ffn_dim
		UpperCAmelCase_       				=			decoder_layers
		UpperCAmelCase_       				=			decoder_attention_heads
		UpperCAmelCase_       				=			dropout
		UpperCAmelCase_       				=			attention_dropout
		UpperCAmelCase_       				=			activation_dropout
		UpperCAmelCase_       				=			activation_function
		UpperCAmelCase_       				=			init_std
		UpperCAmelCase_       				=			decoder_layerdrop
		UpperCAmelCase_       				=			use_cache
		UpperCAmelCase_       				=			decoder_layers
		UpperCAmelCase_       				=			scale_embedding  # scale factor will be sqrt(d_model) if True
		UpperCAmelCase_       				=			max_target_positions

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


import importlib

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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



def       __lowerCAmelCase      (						A       ,     A=None       ,     A=None    ):
	if conf_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.yaml"
	UpperCAmelCase_       				=			load_config(A       ,     display=A    )
	UpperCAmelCase_       				=			VQModel(**config.model.params    )
	if ckpt_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.pt"
	UpperCAmelCase_       				=			torch.load(A       ,     map_location=A    )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_       				=			sd["state_dict"]
	model.load_state_dict(A       ,     strict=A    )
	model.to(A    )
	del sd
	return model



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



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



def       __lowerCAmelCase      (						A       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			instantiate_from_config(A    )
	if sd is not None:
		model.load_state_dict(A    )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}




def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     map_location="cpu"    )
		UpperCAmelCase_       				=			pl_sd["global_step"]
		print(F"loaded model from global step {global_step}."    )
	else:
		UpperCAmelCase_       				=			{"state_dict": None}
		UpperCAmelCase_       				=			None
	UpperCAmelCase_       				=			load_model_from_config(config.model       ,     pl_sd["state_dict"]       ,     gpu=A       ,     eval_mode=A    )["model"]
	return model, global_step