





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
							_UpperCAmelCase				:  int			   =   KandinskyVaaControlnetImgaImgPipeline
							_UpperCAmelCase				:  List[Any]			   =   ["image_embeds", "negative_image_embeds", "image", "hint"]
							_UpperCAmelCase				:  Dict			   =   ["image_embeds", "negative_image_embeds", "image", "hint"]
							_UpperCAmelCase				:  Any			   =   [
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							_UpperCAmelCase				:  Union[str, Any]			   =   False







							@property
							def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
										return 3_2







							@property
							def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
										return 3_2







							@property
							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
										return self.time_input_dim







							@property
							def 						__lowerCamelCase   (				self					:     List[str]      )       ->Tuple:
										return self.time_input_dim * 4







							@property
							def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
										return 1_0_0







							@property
							def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
										torch.manual_seed(0      )

										lowerCamelCase__       :						Optional[int]        =							{
										    '''in_channels''': 8,
										    # Out channels is double in channels because predicts mean and variance
										    '''out_channels''': 8,
										    '''addition_embed_type''': '''image_hint''',
										    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
										    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
										    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
										    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
										    '''layers_per_block''': 1,
										    '''encoder_hid_dim''': self.text_embedder_hidden_size,
										    '''encoder_hid_dim_type''': '''image_proj''',
										    '''cross_attention_dim''': self.cross_attention_dim,
										    '''attention_head_dim''': 4,
										    '''resnet_time_scale_shift''': '''scale_shift''',
										    '''class_embed_type''': None,
										}

										lowerCamelCase__       :						List[str]        =							UNetaDConditionModel(**A      )
										return model







							@property
							def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
										return {
										    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
										    "down_block_types": [
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "AttnDownEncoderBlock2D",
										    ],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 1_2,
										    "out_channels": 3,
										    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
										    "vq_embed_dim": 4,
										}







							@property
							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Union[str, Any]:
										torch.manual_seed(0      )
										lowerCamelCase__       :						Union[str, Any]        =							VQModel(**self.dummy_movq_kwargs      )
										return model







							def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
										lowerCamelCase__       :						List[str]        =							self.dummy_unet
										lowerCamelCase__       :						Tuple        =							self.dummy_movq

										lowerCamelCase__       :						int        =							{
										    '''num_train_timesteps''': 1_0_0_0,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_00_85,
										    '''beta_end''': 0.0_12,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowerCamelCase__       :						Any        =							DDIMScheduler(**A      )

										lowerCamelCase__       :						List[str]        =							{
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







							def 						__lowerCamelCase   (				self					:     Tuple		, A					:     List[Any]		, A					:     List[str]=0      )       ->Union[str, Any]:
										lowerCamelCase__       :						List[str]        =							floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(A      )      ).to(A      )
										lowerCamelCase__       :						str        =							floats_tensor((1, self.text_embedder_hidden_size)		, rng=random.Random(seed + 1      )      ).to(
										    A      )
										# create init_image
										lowerCamelCase__       :						Tuple        =							floats_tensor((1, 3, 6_4, 6_4)		, rng=random.Random(A      )      ).to(A      )
										lowerCamelCase__       :						int        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
										lowerCamelCase__       :						str        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
										# create hint
										lowerCamelCase__       :						Dict        =							floats_tensor((1, 3, 6_4, 6_4)		, rng=random.Random(A      )      ).to(A      )

										if str(A      ).startswith('''mps'''      ):
													lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
										else:
													lowerCamelCase__       :						Any        =							torch.Generator(device=A      ).manual_seed(A      )
										lowerCamelCase__       :						Tuple        =							{
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''hint''': hint,
										    '''generator''': generator,
										    '''height''': 6_4,
										    '''width''': 6_4,
										    '''num_inference_steps''': 1_0,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
										lowerCamelCase__       :						Union[str, Any]        =							'''cpu'''

										lowerCamelCase__       :						Tuple        =							self.get_dummy_components()

										lowerCamelCase__       :						Dict        =							self.pipeline_class(**A      )
										lowerCamelCase__       :						Dict        =							pipe.to(A      )

										pipe.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						Optional[Any]        =							pipe(**self.get_dummy_inputs(A      )      )
										lowerCamelCase__       :						Tuple        =							output.images

										lowerCamelCase__       :						Tuple        =							pipe(
										    **self.get_dummy_inputs(A      )		, return_dict=A		, )[0]

										lowerCamelCase__       :						Union[str, Any]        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						List[Any]        =							image_from_tuple[0, -3:, -3:, -1]

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

										lowerCamelCase__       :						Union[str, Any]        =							np.array(
										    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]      )
										assert (
										    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


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







							def 						__lowerCamelCase   (				self					:     str      )       ->List[str]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
										lowerCamelCase__       :						Dict        =							load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''      )

										lowerCamelCase__       :						Optional[int]        =							load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
										lowerCamelCase__       :						int        =							init_image.resize((5_1_2, 5_1_2)      )

										lowerCamelCase__       :						str        =							load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''      )
										lowerCamelCase__       :						Optional[int]        =							torch.from_numpy(np.array(A      )      ).float() / 2_5_5.0
										lowerCamelCase__       :						Any        =							hint.permute(2		, 0		, 1      ).unsqueeze(0      )

										lowerCamelCase__       :						str        =							'''A robot, 4k photo'''

										lowerCamelCase__       :						Union[str, Any]        =							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''		, torch_dtype=torch.floataa      )
										pipe_prior.to(A      )

										lowerCamelCase__       :						Optional[Any]        =							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''		, torch_dtype=torch.floataa      )
										lowerCamelCase__       :						Optional[Any]        =							pipeline.to(A      )

										pipeline.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						Optional[Any]        =							torch.Generator(device='''cpu'''      ).manual_seed(0      )

										lowerCamelCase__       :						Optional[Any]        =							pipe_prior(
										    A		, image=A		, strength=0.85		, generator=A		, negative_prompt=''''''		, ).to_tuple()

										lowerCamelCase__       :						List[Any]        =							pipeline(
										    image=A		, image_embeds=A		, negative_image_embeds=A		, hint=A		, generator=A		, num_inference_steps=1_0_0		, height=5_1_2		, width=5_1_2		, strength=0.5		, output_type='''np'''		, )

										lowerCamelCase__       :						Tuple        =							output.images[0]

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

										assert_mean_pixel_difference(A		, A      )










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[Any]  			=  {
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

_A :   Any  			=  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							create_model(
       '''HTSAT-tiny'''      ,  '''roberta'''      ,  UpperCAmelCase      ,  precision='''fp32'''      ,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,  enable_fusion=UpperCAmelCase      ,  fusion_type='''aff_2d''' if enable_fusion else None      ,  )
   return model, model_cfg



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						Any        =							R'''.*_projection.(\d+).*'''

   for key, value in state_dict.items():
      # check if any key needs to be modified
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            lowerCamelCase__       :						List[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

         lowerCamelCase__       :						Optional[int]        =							key.replace(f"sequential.{sequential_layer}."      ,  f"layers.{int(UpperCAmelCase					)//3}.linear."					)
      elif re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         lowerCamelCase__       :						Optional[int]        =							int(re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)					)

         # Because in CLAP they use `nn.Sequential`...
         lowerCamelCase__       :						str        =							1 if projecton_layer == 0 else 2

         lowerCamelCase__       :						List[str]        =							key.replace(f"_projection.{projecton_layer}."      ,  f"_projection.linear{transformers_projection_layer}."					)

      if "audio" and "qkv" in key:
         # split qkv into query key and value
         lowerCamelCase__       :						Optional[Any]        =							value
         lowerCamelCase__       :						Optional[Any]        =							mixed_qkv.size(0					) // 3

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

         lowerCamelCase__       :						Optional[int]        =							query_layer
         lowerCamelCase__       :						str        =							key_layer
         lowerCamelCase__       :						List[str]        =							value_layer
      else:
         lowerCamelCase__       :						Tuple        =							value

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							ClapModel(UpperCAmelCase					)

   # ignore the spectrogram embedding layer
   model.load_state_dict(UpperCAmelCase      ,  strict=UpperCAmelCase					)

   model.save_pretrained(UpperCAmelCase					)
   transformers_config.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   int  			=  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')
       parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
       _A :   Tuple  			=  parser.parse_args()

       convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



