





"""simple docstring"""




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_		,			A_		,			A_    :			Union[str, Any]									=    pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3					,   dim=0   )
  A_		,			A_		,			A_    :			Any									=    pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3					,   dim=0   )

  A_    :			Any									=    pt_weights[f"""{prefix}.out_proj.weight"""]
  A_    :			Optional[int]									=    pt_weights[f"""{prefix}.out_proj.bias"""]

  A_    :			Any									=    q_proj
  A_    :			Optional[Any]									=    q_proj_bias

  A_    :			Union[str, Any]									=    k_proj
  A_    :			Optional[Any]									=    k_proj_bias

  A_    :			List[Any]									=    v_proj
  A_    :			Optional[int]									=    v_proj_bias

  A_    :			Dict									=    out_proj_weights
  A_    :			str									=    out_proj_bias





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  copy_linear(hf_mlp.fca					,   _UpperCAmelCase					,   f"""{prefix}.c_fc"""   )
  copy_linear(hf_mlp.fca					,   _UpperCAmelCase					,   f"""{prefix}.c_proj"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Union[str, Any]									=    pt_weights[f"""{prefix}.weight"""].data
  A_    :			str									=    pt_weights[f"""{prefix}.bias"""].data





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  copy_linear(hf_layer.layer_norma					,   _UpperCAmelCase					,   f"""{prefix}.ln_1"""   )
  copy_linear(hf_layer.layer_norma					,   _UpperCAmelCase					,   f"""{prefix}.ln_2"""   )

  # copy MLP
  copy_mlp(hf_layer.mlp					,   _UpperCAmelCase					,   f"""{prefix}.mlp"""   )

  # copy attn
  copy_attn_layer(hf_layer.self_attn					,   _UpperCAmelCase					,   f"""{prefix}.attn"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  for layer_id, hf_layer in enumerate(_UpperCAmelCase   ):
    copy_layer(_UpperCAmelCase					,   _UpperCAmelCase					,   f"""{prefix}.{layer_id}"""   )





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Any									=    pt_weights['text_projection'].data.T

  # copy text encoder
  for name, param in hf_model.text_model.named_parameters():
    A_    :			Any									=    pt_weights[f"""bert.{name}"""].data





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Tuple									=    pt_weights['visual.proj'].data.T

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

  # copy embeddings
  A_    :			Tuple									=    pt_weights['visual.conv1.weight'].data
  A_    :			str									=    pt_weights['visual.class_embedding'].data
  A_    :			int									=    pt_weights['visual.positional_embedding'].data

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





@torch.no_grad()
def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None   ):






  """simple docstring"""





  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  A_    :			str									=    ChineseCLIPConfig.from_pretrained(_UpperCAmelCase   )

  A_    :			List[Any]									=    ChineseCLIPModel(_UpperCAmelCase   ).eval()

  A_    :			int									=    torch.load(_UpperCAmelCase					,   map_location='cpu'   )['state_dict']
  A_    :			Optional[Any]									=    {(name[7:] if name.startswith('module.'   ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(_UpperCAmelCase					,   _UpperCAmelCase   )
  copy_vision_model_and_projection(_UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Optional[Any]									=    pt_weights['logit_scale'].data

  hf_model.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
   lowerCamelCase_	:		Optional[int]         =   argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       help='Path to the output folder storing converted hf PyTorch model.',
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
   )
   parser.add_argument(
       '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
   )
   lowerCamelCase_	:		Dict         =   parser.parse_args()

   convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
   print('The conversion is finished!')


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class      snake_case_      (					_a      ,			_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =StableDiffusionParadigmsPipeline
							__UpperCAmelCase      =TEXT_TO_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_BATCH_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS




							def 							A__	(			self				):
												torch.manual_seed(0				)
												__lowerCAmelCase       =				UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)       ,    layers_per_block=2       ,    sample_size=3_2       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,    cross_attention_dim=3_2       ,    attention_head_dim=(2, 4)       ,    use_linear_projection=_A       ,    )
												__lowerCAmelCase       =				DDIMParallelScheduler(
												    beta_start=0.0_0085       ,    beta_end=0.012       ,    beta_schedule='scaled_linear'       ,    clip_sample=_A       ,    set_alpha_to_one=_A       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				AutoencoderKL(
												    block_out_channels=[3_2, 6_4]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,    latent_channels=4       ,    sample_size=1_2_8       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				CLIPTextConfig(
												    bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=3_2       ,    intermediate_size=3_7       ,    layer_norm_eps=1e-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1_0_0_0       ,    hidden_act='gelu'       ,    projection_dim=5_1_2       ,    )
												__lowerCAmelCase       =				CLIPTextModel(_A				)
												__lowerCAmelCase       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												__lowerCAmelCase       =				{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components




							def 							A__	(			self       ,    _A       ,    _A=0				):
												if str(_A				).startswith('mps'				):
																	__lowerCAmelCase       =				torch.manual_seed(_A				)
												else:
																	__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]				)

												assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				DDPMParallelScheduler()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]				)

												assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




							def 							A__	(			self				):
												super().test_inference_batch_consistent(batch_sizes=[1, 2]				)




							def 							A__	(			self				):
												super().test_inference_batch_single_identical(batch_size=2       ,    expected_max_diff=3e-3				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				'french fries'
												__lowerCAmelCase       =				sd_pipe(**_A       ,    negative_prompt=_A				)
												__lowerCAmelCase       =				output.images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]

												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]				)

												assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2





@slow
@require_torch_gpu
class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							def 							A__	(			self				):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 							A__	(			self       ,    _A=0				):
												__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'stabilityai/stable-diffusion-2-base'
												__lowerCAmelCase       =				DDIMParallelScheduler.from_pretrained(_A       ,    subfolder='scheduler'				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline.from_pretrained(_A       ,    scheduler=_A       ,    safety_checker=_A				)
												pipe.to(_A				)
												pipe.set_progress_bar_config(disable=_A				)
												pipe.enable_attention_slicing()

												__lowerCAmelCase       =				self.get_inputs()
												__lowerCAmelCase       =				pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)

												__lowerCAmelCase       =				np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]				)

												assert np.abs(expected_slice - image_slice				).max() < 1e-2






