



"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       tempfile.mkdtemp()

             __a		:		Tuple									=       BlipImageProcessor()
             __a		:		List[Any]									=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

             __a		:		List[str]									=       BlipaProcessor(__lowerCAmelCase						,     __lowerCAmelCase							)

             processor.save_pretrained(self.tmpdirname							)



      def     lowerCAmelCase__(self						,     **_lowercase							):





             '''simple docstring'''

             return AutoProcessor.from_pretrained(self.tmpdirname						,     **__lowerCAmelCase							).tokenizer



      def     lowerCAmelCase__(self						,     **_lowercase							):





             '''simple docstring'''

             return AutoProcessor.from_pretrained(self.tmpdirname						,     **__lowerCAmelCase							).image_processor



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             shutil.rmtree(self.tmpdirname							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       [np.random.randint(255						,     size=(3, 30, 400)						,     dtype=np.uinta							)]

             __a		:		int									=       [Image.fromarray(np.moveaxis(__lowerCAmelCase						,     0						,     -1							)							) for x in image_inputs]

             return image_inputs



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		str									=       BlipaProcessor(tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor()							)
             processor.save_pretrained(self.tmpdirname							)

             __a		:		Optional[int]									=       self.get_tokenizer(bos_token="""(BOS)"""						,     eos_token="""(EOS)"""							)
             __a		:		List[str]									=       self.get_image_processor(do_normalize=__lowerCAmelCase						,     padding_value=1.0							)

             __a		:		str									=       BlipaProcessor.from_pretrained(
                 self.tmpdirname						,     bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						,     do_normalize=__lowerCAmelCase						,     padding_value=1.0							)

             self.assertEqual(processor.tokenizer.get_vocab()						,     tokenizer_add_kwargs.get_vocab()							)
             self.assertIsInstance(processor.tokenizer						,     __lowerCAmelCase							)

             self.assertEqual(processor.image_processor.to_json_string()						,     image_processor_add_kwargs.to_json_string()							)
             self.assertIsInstance(processor.image_processor						,     __lowerCAmelCase							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       self.get_image_processor()
             __a		:		int									=       self.get_tokenizer()

             __a		:		List[str]									=       BlipaProcessor(tokenizer=__lowerCAmelCase						,     image_processor=__lowerCAmelCase							)

             __a		:		Tuple									=       self.prepare_image_inputs()

             __a		:		Optional[Any]									=       image_processor(__lowerCAmelCase						,     return_tensors="""np"""							)
             __a		:		List[str]									=       processor(images=__lowerCAmelCase						,     return_tensors="""np"""							)

             for key in input_feat_extract.keys():
                    self.assertAlmostEqual(input_feat_extract[key].sum()						,     input_processor[key].sum()						,     delta=1e-2							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       self.get_image_processor()
             __a		:		str									=       self.get_tokenizer()

             __a		:		List[Any]									=       BlipaProcessor(tokenizer=__lowerCAmelCase						,     image_processor=__lowerCAmelCase							)

             __a		:		Any									=       """lower newer"""

             __a		:		Dict									=       processor(text=__lowerCAmelCase							)

             __a		:		List[str]									=       tokenizer(__lowerCAmelCase						,     return_token_type_ids=__lowerCAmelCase							)

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]						,     encoded_processor[key]							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       self.get_image_processor()
             __a		:		Dict									=       self.get_tokenizer()

             __a		:		List[Any]									=       BlipaProcessor(tokenizer=__lowerCAmelCase						,     image_processor=__lowerCAmelCase							)

             __a		:		Dict									=       """lower newer"""
             __a		:		Tuple									=       self.prepare_image_inputs()

             __a		:		Any									=       processor(text=__lowerCAmelCase						,     images=__lowerCAmelCase							)

             self.assertListEqual(list(inputs.keys()							)						,     ["""pixel_values""", """input_ids""", """attention_mask"""]							)

             # test if it raises when no input is passed
             with pytest.raises(__lowerCAmelCase							):
                    processor()



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		int									=       self.get_image_processor()
             __a		:		List[Any]									=       self.get_tokenizer()

             __a		:		Optional[int]									=       BlipaProcessor(tokenizer=__lowerCAmelCase						,     image_processor=__lowerCAmelCase							)

             __a		:		Optional[Any]									=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             __a		:		Dict									=       processor.batch_decode(__lowerCAmelCase							)
             __a		:		str									=       tokenizer.batch_decode(__lowerCAmelCase							)

             self.assertListEqual(__lowerCAmelCase						,     __lowerCAmelCase							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[int]									=       self.get_image_processor()
             __a		:		str									=       self.get_tokenizer()

             __a		:		Any									=       BlipaProcessor(tokenizer=__lowerCAmelCase						,     image_processor=__lowerCAmelCase							)

             __a		:		Tuple									=       """lower newer"""
             __a		:		Optional[int]									=       self.prepare_image_inputs()

             __a		:		List[str]									=       processor(text=__lowerCAmelCase						,     images=__lowerCAmelCase							)

             # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
             self.assertListEqual(list(inputs.keys()							)						,     ["""pixel_values""", """input_ids""", """attention_mask"""]							)






"""simple docstring"""

import importlib

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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





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





def       __magic_name__    (   _lowerCamelCase				: Any					):
							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__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

