



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 	__a					(   lowerCAmelCase_       : int    )				->			List[str]:






						'''simple docstring'''



						if "model" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""model."""      ,""""""    )
						if "norm1" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""norm1"""      ,"""attention.output.LayerNorm"""    )
						if "norm2" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""norm2"""      ,"""output.LayerNorm"""    )
						if "norm" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""norm"""      ,"""LayerNorm"""    )
						if "transformer" in orig_key:
												UpperCAmelCase_=       orig_key.split("""."""    )[0].split("""_"""    )[-1]
												UpperCAmelCase_=       orig_key.replace(F"""transformer_{layer_num}"""      ,F"""encoder.layer.{layer_num}"""    )
						if "mha.attn" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""mha.attn"""      ,"""attention.self"""    )
						if "mha" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""mha"""      ,"""attention"""    )
						if "W_q" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""W_q"""      ,"""self.query"""    )
						if "W_k" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""W_k"""      ,"""self.key"""    )
						if "W_v" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""W_v"""      ,"""self.value"""    )
						if "ff1" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""ff1"""      ,"""intermediate.dense"""    )
						if "ff2" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""ff2"""      ,"""output.dense"""    )
						if "ff" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""ff"""      ,"""output.dense"""    )
						if "mlm_class" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""mlm.mlm_class"""      ,"""cls.predictions.decoder"""    )
						if "mlm" in orig_key:
												UpperCAmelCase_=       orig_key.replace("""mlm"""      ,"""cls.predictions.transform"""    )
						if "cls" not in orig_key:
												UpperCAmelCase_=       """yoso.""" + orig_key

						return orig_key



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Any    )				->			List[Any]:






						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												UpperCAmelCase_=       orig_state_dict.pop(lowerCAmelCase_    )

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		UpperCAmelCase_=       val

						UpperCAmelCase_=       orig_state_dict["""cls.predictions.decoder.bias"""]
						UpperCAmelCase_=       torch.arange(lowerCAmelCase_    ).expand((1, -1)    ) + 2

						return orig_state_dict



def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )["""model_state_dict"""]
						UpperCAmelCase_=       YosoConfig.from_json_file(lowerCAmelCase_    )
						UpperCAmelCase_=       YosoForMaskedLM(lowerCAmelCase_    )

						UpperCAmelCase_=       convert_checkpoint_helper(config.max_position_embeddings      ,lowerCAmelCase_    )

						print(model.load_state_dict(lowerCAmelCase_    )    )
						model.eval()
						model.save_pretrained(lowerCAmelCase_    )

						print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""    )


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The json file for YOSO model config.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	__A       =						parser.parse_args()
	convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

							UpperCAmelCase_=       [Image.fromarray(np.moveaxis(__UpperCAmelCase						,  0						,  -1					)					) for x in image_inputs]

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							UpperCAmelCase_=       self.get_tokenizer(bos_token="""(BOS)"""						,  eos_token="""(EOS)"""					)
							UpperCAmelCase_=       self.get_image_processor(do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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




