

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(ScoreSdeVeScheduler,)
		a__       							=	()


		@property
		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    4
			_UpperCamelCase :		List[str]								=    3
			_UpperCamelCase :		Tuple								=    8
			_UpperCamelCase :		Optional[int]								=    8

			_UpperCamelCase :		Union[str, Any]								=    torch.rand((batch_size, num_channels, height, width))

			return sample


		@property
		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    4
			_UpperCamelCase :		Optional[Any]								=    3
			_UpperCamelCase :		Any								=    8
			_UpperCamelCase :		Tuple								=    8

			_UpperCamelCase :		List[Any]								=    batch_size * num_channels * height * width
			_UpperCamelCase :		str								=    torch.arange(__snake_case)
			_UpperCamelCase :		List[Any]								=    sample.reshape(__snake_case						,							__snake_case						,							__snake_case						,							__snake_case)
			_UpperCamelCase :		List[Any]								=    sample / num_elems
			_UpperCamelCase :		Optional[Any]								=    sample.permute(3						,							0						,							1						,							2)

			return sample


		def 		A__       (					self):
			def model(__snake_case						,							__snake_case						,							*__snake_case):
				return sample * t / (t + 1)

			return model


		def 		A__       (					self						,							**__snake_case):
			_UpperCamelCase :		int								=    {
			    'num_train_timesteps': 20_00,
			    'snr': 0.1_5,
			    'sigma_min': 0.0_1,
			    'sigma_max': 13_48,
			    'sampling_eps': 1e-5,
			}

			config.update(**__snake_case)
			return config


		def 		A__       (					self						,							__snake_case=0						,							**__snake_case):
			_UpperCamelCase :		int								=    dict(self.forward_default_kwargs)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		List[str]								=    self.dummy_sample
				_UpperCamelCase :		int								=    0.1 * sample

				_UpperCamelCase :		str								=    self.get_scheduler_config(**__snake_case)
				_UpperCamelCase :		List[str]								=    scheduler_class(**__snake_case)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase :		List[Any]								=    scheduler_class.from_pretrained(__snake_case)

				_UpperCamelCase :		int								=    scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		int								=    new_scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

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

				_UpperCamelCase :		List[str]								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Union[str, Any]								=    new_scheduler.step_correct(
				    __snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

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


		def 		A__       (					self						,							__snake_case=0						,							**__snake_case):
			_UpperCamelCase :		Union[str, Any]								=    dict(self.forward_default_kwargs)
			kwargs.update(__snake_case)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		Optional[Any]								=    self.dummy_sample
				_UpperCamelCase :		Optional[Any]								=    0.1 * sample

				_UpperCamelCase :		Any								=    self.get_scheduler_config()
				_UpperCamelCase :		Union[str, Any]								=    scheduler_class(**__snake_case)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase :		Tuple								=    scheduler_class.from_pretrained(__snake_case)

				_UpperCamelCase :		Union[str, Any]								=    scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Optional[Any]								=    new_scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

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

				_UpperCamelCase :		Tuple								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Any								=    new_scheduler.step_correct(
				    __snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

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


		def 		A__       (					self):
			for timesteps in [10, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=__snake_case)


		def 		A__       (					self):
			for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]						,							[1, 1_00, 10_00]):
				self.check_over_configs(sigma_min=__snake_case						,							sigma_max=__snake_case)


		def 		A__       (					self):
			for t in [0.1, 0.5, 0.7_5]:
				self.check_over_forward(time_step=__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    dict(self.forward_default_kwargs)

			_UpperCamelCase :		List[Any]								=    self.scheduler_classes[0]
			_UpperCamelCase :		Tuple								=    self.get_scheduler_config()
			_UpperCamelCase :		List[Any]								=    scheduler_class(**__snake_case)

			_UpperCamelCase :		List[Any]								=    3

			_UpperCamelCase :		Any								=    self.dummy_model()
			_UpperCamelCase :		Dict								=    self.dummy_sample_deter

			scheduler.set_sigmas(__snake_case)
			scheduler.set_timesteps(__snake_case)
			_UpperCamelCase :		List[str]								=    torch.manual_seed(0)

			for i, t in enumerate(scheduler.timesteps):
				_UpperCamelCase :		Optional[int]								=    scheduler.sigmas[i]

				for _ in range(scheduler.config.correct_steps):
					with torch.no_grad():
						_UpperCamelCase :		str								=    model(__snake_case						,							__snake_case)
					_UpperCamelCase :		Optional[Any]								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=__snake_case						,							**__snake_case).prev_sample

				with torch.no_grad():
					_UpperCamelCase :		Union[str, Any]								=    model(__snake_case						,							__snake_case)

				_UpperCamelCase :		Any								=    scheduler.step_pred(__snake_case						,							__snake_case						,							__snake_case						,							generator=__snake_case						,							**__snake_case)
				_UpperCamelCase						,		_UpperCamelCase :		Optional[int]								=    output.prev_sample, output.prev_sample_mean

			_UpperCamelCase :		int								=    torch.sum(torch.abs(__snake_case))
			_UpperCamelCase :		List[Any]								=    torch.mean(torch.abs(__snake_case))

			assert np.isclose(result_sum.item()						,							1_4_3_7_2_7_5_8_5_2_8.0)
			assert np.isclose(result_mean.item()						,							1_8_7_1_4_5_3_0.0)






		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    dict(self.forward_default_kwargs)

			_UpperCamelCase :		List[str]								=    kwargs.pop('num_inference_steps'						,							__snake_case)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		List[Any]								=    self.get_scheduler_config()
				_UpperCamelCase :		Optional[Any]								=    scheduler_class(**__snake_case)

				_UpperCamelCase :		Optional[Any]								=    self.dummy_sample
				_UpperCamelCase :		int								=    0.1 * sample

				if num_inference_steps is not None and hasattr(__snake_case						,							'set_timesteps'):
					scheduler.set_timesteps(__snake_case)
				elif num_inference_steps is not None and not hasattr(__snake_case						,							'set_timesteps'):
					_UpperCamelCase :		Union[str, Any]								=    num_inference_steps

				_UpperCamelCase :		Any								=    scheduler.step_pred(__snake_case						,							0						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Optional[int]								=    scheduler.step_pred(__snake_case						,							1						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

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







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

	_UpperCamelCase :		Any								=    out_proj_weights
	_UpperCamelCase :		Optional[int]								=    out_proj_bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''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_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    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_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    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 :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		str								=    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_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase :		Dict								=    CLIPConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    CLIPConfig(projection_dim=5_1_2				,							text_config={}				,							vision_config={}						)

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

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

	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		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""")
							lowerCAmelCase__             =		parser.parse_args()

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




