




'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = (DPMSolverMultistepScheduler,)
					lowercase__    = (("""num_inference_steps""", 2_5),)






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       {
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							    "sample_max_value": 1.0,
							    "algorithm_type": "dpmsolver++",
							    "solver_type": "midpoint",
							    "lower_order_final": False,
							    "lambda_min_clipped": -float("inf" ),
							    "variance_type": None,
							}

							config.update(**__UpperCamelCase )
							return config






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=0,  **__UpperCamelCase ):
							_snake_case	       =       dict(self.forward_default_kwargs )
							_snake_case	       =       kwargs.pop("num_inference_steps",  __UpperCamelCase )
							_snake_case	       =       self.dummy_sample
							_snake_case	       =       0.1 * sample
							_snake_case	       =       [residual + 0.2, residual + 0.15, residual + 0.10]

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

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(__UpperCamelCase )
											_snake_case	       =       scheduler_class.from_pretrained(__UpperCamelCase )
											new_scheduler.set_timesteps(__UpperCamelCase )
											# copy over dummy past residuals
											_snake_case	       =       dummy_past_residuals[: new_scheduler.config.solver_order]

									_snake_case       ,    _snake_case	       =       sample, sample
									for t in range(__UpperCamelCase,  time_step + scheduler.config.solver_order + 1 ):
											_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample
											_snake_case	       =       new_scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							pass






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=0,  **__UpperCamelCase ):
							_snake_case	       =       dict(self.forward_default_kwargs )
							_snake_case	       =       kwargs.pop("num_inference_steps",  __UpperCamelCase )
							_snake_case	       =       self.dummy_sample
							_snake_case	       =       0.1 * sample
							_snake_case	       =       [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									_snake_case	       =       self.get_scheduler_config()
									_snake_case	       =       scheduler_class(**__UpperCamelCase )
									scheduler.set_timesteps(__UpperCamelCase )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(__UpperCamelCase )
											_snake_case	       =       scheduler_class.from_pretrained(__UpperCamelCase )
											# copy over dummy past residuals
											new_scheduler.set_timesteps(__UpperCamelCase )

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

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample
									_snake_case	       =       new_scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample

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






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=None,  **__UpperCamelCase ):
							if scheduler is None:
									_snake_case	       =       self.scheduler_classes[0]
									_snake_case	       =       self.get_scheduler_config(**__UpperCamelCase )
									_snake_case	       =       scheduler_class(**__UpperCamelCase )

							_snake_case	       =       10
							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter
							scheduler.set_timesteps(__UpperCamelCase )

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ).prev_sample

							return sample






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       dict(self.forward_default_kwargs )

							_snake_case	       =       kwargs.pop("num_inference_steps",  __UpperCamelCase )

							for scheduler_class in self.scheduler_classes:
									_snake_case	       =       self.get_scheduler_config()
									_snake_case	       =       scheduler_class(**__UpperCamelCase )

									_snake_case	       =       self.dummy_sample
									_snake_case	       =       0.1 * sample

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

									# copy over dummy past residuals (must be done after set_timesteps)
									_snake_case	       =       [residual + 0.2, residual + 0.15, residual + 0.10]
									_snake_case	       =       dummy_past_residuals[: scheduler.config.solver_order]

									_snake_case	       =       scheduler.timesteps[5]
									_snake_case	       =       scheduler.timesteps[6]

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample
									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase ).prev_sample

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.check_over_configs(thresholding=__UpperCamelCase )
							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=__UpperCamelCase,  prediction_type=__UpperCamelCase,  sample_max_value=__UpperCamelCase,  algorithm_type="dpmsolver++",  solver_order=__UpperCamelCase,  solver_type=__UpperCamelCase,  )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
											for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
															if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																	if order == 3:
																			continue
															else:
																	self.check_over_configs(
																	    solver_order=__UpperCamelCase,  solver_type=__UpperCamelCase,  prediction_type=__UpperCamelCase,  algorithm_type=__UpperCamelCase,  )
															_snake_case	       =       self.full_loop(
															    solver_order=__UpperCamelCase,  solver_type=__UpperCamelCase,  prediction_type=__UpperCamelCase,  algorithm_type=__UpperCamelCase,  )
															assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.check_over_configs(lower_order_final=__UpperCamelCase )
							self.check_over_configs(lower_order_final=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.check_over_configs(lambda_min_clipped=-float("inf" ) )
							self.check_over_configs(lambda_min_clipped=-5.1 )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.check_over_configs(variance_type=__UpperCamelCase )
							self.check_over_configs(variance_type="learned_range" )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=__UpperCamelCase,  time_step=0 )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.full_loop()
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 0.33_01 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.full_loop(thresholding=__UpperCamelCase,  dynamic_thresholding_ratio=0.87,  sample_max_value=0.5 )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 1.13_64 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.full_loop(prediction_type="v_prediction" )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 0.22_51 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.full_loop(prediction_type="v_prediction",  use_karras_sigmas=__UpperCamelCase )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 0.20_96 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_snake_case	       =       DPMSolverMultistepScheduler(**self.get_scheduler_config() )
							_snake_case	       =       self.full_loop(scheduler=__UpperCamelCase )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 0.33_01 ) < 1E-3

							_snake_case	       =       DPMSolverSinglestepScheduler.from_config(scheduler.config )
							_snake_case	       =       UniPCMultistepScheduler.from_config(scheduler.config )
							_snake_case	       =       DEISMultistepScheduler.from_config(scheduler.config )
							_snake_case	       =       DPMSolverMultistepScheduler.from_config(scheduler.config )

							_snake_case	       =       self.full_loop(scheduler=__UpperCamelCase )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

							assert abs(result_mean.item() - 0.33_01 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config(thresholding=__UpperCamelCase,  dynamic_thresholding_ratio=0 )
							_snake_case	       =       scheduler_class(**__UpperCamelCase )

							_snake_case	       =       10
							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter.half()
							scheduler.set_timesteps(__UpperCamelCase )

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ).prev_sample

							assert sample.dtype == torch.floataa






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							for scheduler_class in self.scheduler_classes:
									_snake_case	       =       self.get_scheduler_config(**__UpperCamelCase )
									_snake_case	       =       scheduler_class(**__UpperCamelCase )

									scheduler.set_timesteps(scheduler.config.num_train_timesteps )
									assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps








import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )
    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )

    # copy MLP
    copy_mlp(hf_layer.mlp       ,		pt_layer.mlp       )

    # copy attn
    copy_attn_layer(hf_layer.self_attn       ,		pt_layer.attn       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm       ,		pt_model.ln_final       )

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers       ,		pt_model.transformer.resblocks       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm       ,		pt_model.visual.ln_pre       )
    copy_linear(hf_model.vision_model.post_layernorm       ,		pt_model.visual.ln_post       )

    # copy embeds
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers       ,		pt_model.visual.transformer.resblocks       )




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

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

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						: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 fairseq checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

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