




"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def       UpperCAmelCase__ (snake_case__							:      List[Any]					):
		"""simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      Optional[int]					):
		"""simple docstring"""

		_snake_case							:	List[Any]			=						{}

		for key, value in state_dict.items():
				if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
						continue

				_snake_case							:	Dict			=						key.replace("""heads.cmd.mim_head.cls.predictions"""	,     """mmm_image_head"""					)
				_snake_case							:	List[str]			=						key.replace("""heads.cmd.mlm_head.cls.predictions"""	,     """mmm_text_head"""					)
				_snake_case							:	List[str]			=						key.replace("""heads.cmd.itm_head.cls"""	,     """itm_head"""					)
				_snake_case							:	Union[str, Any]			=						key.replace("""heads.cmd.itm_head.pooler"""	,     """itm_head.pooler"""					)
				_snake_case							:	Optional[Any]			=						key.replace("""heads.cmd.clip_head.logit_scale"""	,     """flava.logit_scale"""					)
				_snake_case							:	List[str]			=						key.replace("""heads.fairseq_mlm.cls.predictions"""	,     """mlm_head"""					)
				_snake_case							:	Optional[int]			=						key.replace("""heads.imagenet.mim_head.cls.predictions"""	,     """mim_head"""					)
				_snake_case							:	str			=						key.replace("""mm_text_projection"""	,     """flava.text_to_mm_projection"""					)
				_snake_case							:	str			=						key.replace("""mm_image_projection"""	,     """flava.image_to_mm_projection"""					)
				_snake_case							:	str			=						key.replace("""image_encoder.module"""	,     """flava.image_model"""					)
				_snake_case							:	Dict			=						key.replace("""text_encoder.module"""	,     """flava.text_model"""					)
				_snake_case							:	List[str]			=						key.replace("""mm_encoder.module.encoder.cls_token"""	,     """flava.multimodal_model.cls_token"""					)
				_snake_case							:	Any			=						key.replace("""mm_encoder.module"""	,     """flava.multimodal_model"""					)
				_snake_case							:	List[Any]			=						key.replace("""text_projection"""	,     """flava.text_projection"""					)
				_snake_case							:	Dict			=						key.replace("""image_projection"""	,     """flava.image_projection"""					)

				_snake_case							:	Tuple			=						value.float()

		for key, value in codebook_state_dict.items():
				_snake_case							:	Optional[Any]			=						value

		return upgrade


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      List[Any]=None					):
		"""simple docstring"""

		if config_path is not None:
				_snake_case							:	str			=						FlavaConfig.from_pretrained(snake_case__					)
		else:
				_snake_case							:	int			=						FlavaConfig()

		_snake_case							:	int			=						FlavaForPreTraining(snake_case__					).eval()

		_snake_case							:	Tuple			=						convert_dalle_checkpoint(snake_case__	,     snake_case__	,     save_checkpoint=snake_case__					)

		if os.path.exists(snake_case__					):
				_snake_case							:	int			=						torch.load(snake_case__	,     map_location="""cpu"""					)
		else:
				_snake_case							:	List[Any]			=						torch.hub.load_state_dict_from_url(snake_case__	,     map_location="""cpu"""					)

		_snake_case							:	Any			=						upgrade_state_dict(snake_case__	,     snake_case__					)
		hf_model.load_state_dict(snake_case__					)
		_snake_case							:	List[Any]			=						hf_model.state_dict()
		_snake_case							:	Union[str, Any]			=						count_parameters(snake_case__					)
		_snake_case							:	Optional[Any]			=						count_parameters(snake_case__					) + count_parameters(snake_case__					)

		assert torch.allclose(snake_case__	,     snake_case__	,     atol=1e-3					)

		hf_model.save_pretrained(snake_case__					)


if __name__ == "__main__":
					A_      			=       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('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					A_      			=       parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   **a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10


