






import importlib

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					List[Any]=False		)  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						OmegaConf.load(UpperCamelCase_		)
						if display:
												print(yaml.dump(OmegaConf.to_container(UpperCamelCase_		)		)		)
						return config







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					Union[str, Any]=None		)  ->   Optional[Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]		)  ->   Union[str, Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					List[Any]=False		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						string.rsplit('.'    ,       1		)
						if reload:
												lowerCAmelCase__    						=						importlib.import_module(UpperCamelCase_		)
												importlib.reload(UpperCamelCase_		)
						return getattr(importlib.import_module(UpperCamelCase_    ,       package=UpperCamelCase_		)    ,       cls		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]		)  ->   int:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Optional[int]=True    ,       UpperCamelCase_       :					Union[str, Any]=True		)  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						instantiate_from_config(UpperCamelCase_		)
						if sd is not None:
												model.load_state_dict(UpperCamelCase_		)
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Any		)  ->   int:

						"""simple docstring"""



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













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





