



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :int					):






	'''simple docstring'''

	a						=		s.rsplit(_UpperCamelCase ,					_UpperCamelCase					)
	return new.join(_UpperCamelCase					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






	'''simple docstring'''

	return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






	'''simple docstring'''

	a						=		{}

	a						=		["group_1", "group_2", "group_3", "group_4"]
	for key, value in state_dict.items():
		for group_key in group_keys:
			if group_key in key:
				a						=		key.replace(F"""{group_key}.""" ,					F"""{group_key}.group."""					)

		if "res_path" in key:
			a						=		key.replace("res_path." ,					"res_path.path."					)

		if key.endswith(".w"					):
			a						=		rreplace(_UpperCamelCase ,					".w" ,					".weight" ,					1					)
		if key.endswith(".b"					):
			a						=		rreplace(_UpperCamelCase ,					".b" ,					".bias" ,					1					)

		a						=		value.float()

	return upgrade


@torch.no_grad()
def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :int=None ,					UpperCAmelCase__ :List[Any]=True					):






	'''simple docstring'''

	from dall_e import Encoder

	a						=		Encoder()
	if os.path.exists(_UpperCamelCase					):
		a						=		torch.load(_UpperCamelCase					)
	else:
		a						=		torch.hub.load_state_dict_from_url(_UpperCamelCase					)

	if isinstance(_UpperCamelCase ,					_UpperCamelCase					):
		a						=		ckpt.state_dict()
	encoder.load_state_dict(_UpperCamelCase					)

	if config_path is not None:
		a						=		FlavaImageCodebookConfig.from_pretrained(_UpperCamelCase					)
	else:
		a						=		FlavaImageCodebookConfig()

	a						=		FlavaImageCodebook(_UpperCamelCase					).eval()
	a						=		encoder.state_dict()

	a						=		upgrade_state_dict(_UpperCamelCase					)
	hf_model.load_state_dict(_UpperCamelCase					)
	a						=		hf_model.state_dict()
	a						=		count_parameters(_UpperCamelCase					)
	a						=		count_parameters(_UpperCamelCase					)

	assert torch.allclose(_UpperCamelCase ,					_UpperCamelCase ,					atol=1E-3					)

	if save_checkpoint:
		hf_model.save_pretrained(_UpperCamelCase					)
	else:
		return hf_state_dict


if __name__ == "__main__":
					A_		:     List[Any]        =     argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					A_		:     Tuple        =     parser.parse_args()

					convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

