


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_(   lowerCamelCase_				)					->		int:
    # encoder.embeddings are double copied in original FLAVA
    return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							List[str]   	=	{}

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

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

        _lowercase :							Optional[Any]   	=	value.float()

    for key, value in codebook_state_dict.items():
        _lowercase :							Optional[int]   	=	value

    return upgrade



@torch.no_grad()
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=None				)					->		str:
    if config_path is not None:
        _lowercase :							List[Any]   	=	FlavaConfig.from_pretrained(lowerCamelCase_				)
    else:
        _lowercase :							str   	=	FlavaConfig()

    _lowercase :							Any   	=	FlavaForPreTraining(lowerCamelCase_				).eval()

    _lowercase :							int   	=	convert_dalle_checkpoint(lowerCamelCase_							,				lowerCamelCase_							,				save_checkpoint=lowerCamelCase_				)

    if os.path.exists(lowerCamelCase_				):
        _lowercase :							str   	=	torch.load(lowerCamelCase_							,				map_location='cpu'				)
    else:
        _lowercase :							Union[str, Any]   	=	torch.hub.load_state_dict_from_url(lowerCamelCase_							,				map_location='cpu'				)

    _lowercase :							Tuple   	=	upgrade_state_dict(lowerCamelCase_							,				lowerCamelCase_				)
    hf_model.load_state_dict(lowerCamelCase_				)
    _lowercase :							Tuple   	=	hf_model.state_dict()
    _lowercase :							Any   	=	count_parameters(lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	count_parameters(lowerCamelCase_				) + count_parameters(lowerCamelCase_				)

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

    hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Optional[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("--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")
 SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				parser.parse_args()

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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    (DPMSolverSinglestepScheduler,)
       lowercase_				:			List[str]									=    (("""num_inference_steps""", 25),)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	{
               'num_train_timesteps': 10_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lambda_min_clipped': -float('inf'),
               'variance_type': None,
           }

           config.update(**lowerCamelCase)
           return config
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	dict(self.forward_default_kwargs)
           _lowercase :							Union[str, Any]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.dummy_sample
           _lowercase :							Optional[int]   	=	0.1 * sample
           _lowercase :							Optional[int]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							List[Any]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)
               # copy over dummy past residuals
               _lowercase :							List[str]   	=	dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							Optional[Any]   	=	scheduler_class.from_pretrained(lowerCamelCase)
                   new_scheduler.set_timesteps(lowerCamelCase)
                   # copy over dummy past residuals
                   _lowercase :							List[Any]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase	,       _lowercase :							List[Any]   	=	sample, sample
               for t in range(lowerCamelCase,   time_step + scheduler.config.solver_order + 1):
                   _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
                   _lowercase :							int   	=	new_scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample

                   assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	dict(self.forward_default_kwargs)
           _lowercase :							List[str]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							List[str]   	=	self.dummy_sample
           _lowercase :							str   	=	0.1 * sample
           _lowercase :							Optional[Any]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config()
               _lowercase :							List[str]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)

               # copy over dummy past residuals (must be after setting timesteps)
               _lowercase :							List[str]   	=	dummy_past_residuals[: scheduler.config.solver_order]

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

                   # copy over dummy past residual (must be after setting timesteps)
                   _lowercase :							List[str]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
               _lowercase :							List[Any]   	=	new_scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           if scheduler is None:
               _lowercase :							str   	=	self.scheduler_classes[0]
               _lowercase :							int   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Dict   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Optional[int]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           return sample
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							Optional[int]   	=	50
           _lowercase :							Union[str, Any]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:]):
               _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							int   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_5_7_4) < 1E-3
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							List[str]   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3

           _lowercase :							str   	=	DEISMultistepScheduler.from_config(scheduler.config)
           _lowercase :							List[str]   	=	DPMSolverMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Tuple   	=	UniPCMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Any   	=	DPMSolverSinglestepScheduler.from_config(scheduler.config)

           _lowercase :							Any   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(thresholding=lowerCamelCase)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=lowerCamelCase,   prediction_type=lowerCamelCase,   sample_max_value=lowerCamelCase,   algorithm_type='dpmsolver++',   solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           _lowercase :							Optional[Any]   	=	self.full_loop(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           assert not torch.isnan(lowerCamelCase).any(), "Samples have nan numbers"
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           self.check_over_configs(lower_order_final=lowerCamelCase)
           self.check_over_configs(lower_order_final=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(lambda_min_clipped=-float('inf'))
           self.check_over_configs(lambda_min_clipped=-5.1)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.check_over_configs(variance_type=lowerCamelCase)
           self.check_over_configs(variance_type='learned_range')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
               self.check_over_forward(num_inference_steps=lowerCamelCase,   time_step=0)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.full_loop()
           _lowercase :							Union[str, Any]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(use_karras_sigmas=lowerCamelCase)
           _lowercase :							List[str]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_2_4_8) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction')
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.1_4_5_3) < 1E-3
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction',   use_karras_sigmas=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.0_6_4_9) < 1E-3




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(thresholding=lowerCamelCase,   dynamic_thresholding_ratio=0)
           _lowercase :							Any   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							str   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           assert sample.dtype == torch.floataa


