



"""simple docstring"""


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 			_a (					__lowerCamelCase			,							__lowerCamelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	StableDiffusionDiffEditPipeline
			UpperCamelCase__			      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
			UpperCamelCase__			      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
			UpperCamelCase__			      =	frozenset(
			    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			UpperCamelCase__			      =	frozenset([])



			def  lowercase__						(		self     :    Dict   )->int:
							torch.manual_seed(0   )
							_UpperCAmelCase					=    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_				,   )
							_UpperCAmelCase					=    DDIMScheduler(
							    beta_start=0.0_0_0_8_5				,   beta_end=0.0_1_2				,   beta_schedule='''scaled_linear'''				,   clip_sample=a_				,   set_alpha_to_one=a_				,   )
							_UpperCAmelCase					=    DDIMInverseScheduler(
							    beta_start=0.0_0_0_8_5				,   beta_end=0.0_1_2				,   beta_schedule='''scaled_linear'''				,   clip_sample=a_				,   set_alpha_to_zero=a_				,   )
							torch.manual_seed(0   )
							_UpperCAmelCase					=    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   )
							_UpperCAmelCase					=    CLIPTextConfig(
							    bos_token_id=0				,   eos_token_id=2				,   hidden_size=3_2				,   intermediate_size=3_7				,   layer_norm_eps=1e-05				,   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				,   )
							_UpperCAmelCase					=    CLIPTextModel(a_   )
							_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							_UpperCAmelCase					=    {
							    "unet": unet,
							    "scheduler": scheduler,
							    "inverse_scheduler": inverse_scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}

							return components



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Union[str, Any]=0   )->List[str]:
							_UpperCAmelCase					=    floats_tensor((1, 1_6, 1_6)				,   rng=random.Random(a_   )   ).to(a_   )
							_UpperCAmelCase					=    floats_tensor((1, 2, 4, 1_6, 1_6)				,   rng=random.Random(a_   )   ).to(a_   )
							if str(a_   ).startswith('''mps'''   ):
											_UpperCAmelCase					=    torch.manual_seed(a_   )
							else:
											_UpperCAmelCase					=    torch.Generator(device=a_   ).manual_seed(a_   )
							_UpperCAmelCase					=    {
							    "prompt": "a dog and a newt",
							    "mask_image": mask,
							    "image_latents": latents,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "inpaint_strength": 1.0,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}

							return inputs



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    Optional[int]=0   )->int:
							_UpperCAmelCase					=    floats_tensor((1, 3, 3_2, 3_2)				,   rng=random.Random(a_   )   ).to(a_   )
							_UpperCAmelCase					=    image.cpu().permute(0				,   2				,   3				,   1   )[0]
							_UpperCAmelCase					=    Image.fromarray(np.uinta(a_   )   ).convert('''RGB'''   )
							if str(a_   ).startswith('''mps'''   ):
											_UpperCAmelCase					=    torch.manual_seed(a_   )
							else:
											_UpperCAmelCase					=    torch.Generator(device=a_   ).manual_seed(a_   )
							_UpperCAmelCase					=    {
							    "image": image,
							    "source_prompt": "a cat and a frog",
							    "target_prompt": "a dog and a newt",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "num_maps_per_mask": 2,
							    "mask_encode_strength": 1.0,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}

							return inputs



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    int=0   )->Any:
							_UpperCAmelCase					=    floats_tensor((1, 3, 3_2, 3_2)				,   rng=random.Random(a_   )   ).to(a_   )
							_UpperCAmelCase					=    image.cpu().permute(0				,   2				,   3				,   1   )[0]
							_UpperCAmelCase					=    Image.fromarray(np.uinta(a_   )   ).convert('''RGB'''   )
							if str(a_   ).startswith('''mps'''   ):
											_UpperCAmelCase					=    torch.manual_seed(a_   )
							else:
											_UpperCAmelCase					=    torch.Generator(device=a_   ).manual_seed(a_   )
							_UpperCAmelCase					=    {
							    "image": image,
							    "prompt": "a cat and a frog",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "inpaint_strength": 1.0,
							    "guidance_scale": 6.0,
							    "decode_latents": True,
							    "output_type": "numpy",
							}
							return inputs



			def  lowercase__						(		self     :    Any   )->Dict:
							if not hasattr(self.pipeline_class				,   '''_optional_components'''   ):
											return

							_UpperCAmelCase					=    self.get_dummy_components()
							_UpperCAmelCase					=    self.pipeline_class(**a_   )
							pipe.to(a_   )
							pipe.set_progress_bar_config(disable=a_   )

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
											setattr(a_				,   a_				,   a_   )
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

							_UpperCAmelCase					=    self.get_dummy_inputs(a_   )
							_UpperCAmelCase					=    pipe(**a_   )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(a_   )
											_UpperCAmelCase					=    self.pipeline_class.from_pretrained(a_   )
											pipe_loaded.to(a_   )
											pipe_loaded.set_progress_bar_config(disable=a_   )

							for optional_component in pipe._optional_components:
											self.assertTrue(
											    getattr(a_				,   a_   ) is None				,   F'`{optional_component}` did not stay set to None after loading.'				,   )

							_UpperCAmelCase					=    self.get_dummy_inputs(a_   )
							_UpperCAmelCase					=    pipe_loaded(**a_   )[0]

							_UpperCAmelCase					=    np.abs(output - output_loaded   ).max()
							self.assertLess(a_				,   1e-4   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Any:
							_UpperCAmelCase					=    "cpu"

							_UpperCAmelCase					=    self.get_dummy_components()
							_UpperCAmelCase					=    self.pipeline_class(**a_   )
							pipe.to(a_   )
							pipe.set_progress_bar_config(disable=a_   )

							_UpperCAmelCase					=    self.get_dummy_mask_inputs(a_   )
							_UpperCAmelCase					=    pipe.generate_mask(**a_   )
							_UpperCAmelCase					=    mask[0, -3:, -3:]

							self.assertEqual(mask.shape				,   (1, 1_6, 1_6)   )
							_UpperCAmelCase					=    np.array([0] * 9   )
							_UpperCAmelCase					=    np.abs(mask_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a_				,   1e-3   )
							self.assertEqual(mask[0, -3, -4]				,   0   )



			def  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    "cpu"

							_UpperCAmelCase					=    self.get_dummy_components()
							_UpperCAmelCase					=    self.pipeline_class(**a_   )
							pipe.to(a_   )
							pipe.set_progress_bar_config(disable=a_   )

							_UpperCAmelCase					=    self.get_dummy_inversion_inputs(a_   )
							_UpperCAmelCase					=    pipe.invert(**a_   ).images
							_UpperCAmelCase					=    image[0, -1, -3:, -3:]

							self.assertEqual(image.shape				,   (2, 3_2, 3_2, 3)   )
							_UpperCAmelCase					=    np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,   )
							_UpperCAmelCase					=    np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a_				,   1e-3   )



			def  lowercase__						(		self     :    Dict   )->Union[str, Any]:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3   )



			def  lowercase__						(		self     :    Optional[int]   )->List[str]:
							_UpperCAmelCase					=    "cpu"

							_UpperCAmelCase					=    self.get_dummy_components()

							_UpperCAmelCase					=    {"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
							_UpperCAmelCase					=    DPMSolverMultistepScheduler(**a_   )
							_UpperCAmelCase					=    DPMSolverMultistepInverseScheduler(**a_   )

							_UpperCAmelCase					=    self.pipeline_class(**a_   )
							pipe.to(a_   )
							pipe.set_progress_bar_config(disable=a_   )

							_UpperCAmelCase					=    self.get_dummy_inversion_inputs(a_   )
							_UpperCAmelCase					=    pipe.invert(**a_   ).images
							_UpperCAmelCase					=    image[0, -1, -3:, -3:]

							self.assertEqual(image.shape				,   (2, 3_2, 3_2, 3)   )
							_UpperCAmelCase					=    np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,   )
							_UpperCAmelCase					=    np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a_				,   1e-3   )



@require_torch_gpu
@slow
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    int   )->Tuple:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



			@classmethod
			def  lowercase__						(		cls     :    Optional[Any]   )->Optional[Any]:
							_UpperCAmelCase					=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''   )

							_UpperCAmelCase					=    raw_image.convert('''RGB'''   ).resize((7_6_8, 7_6_8)   )

							_UpperCAmelCase					=    raw_image



			def  lowercase__						(		self     :    List[Any]   )->List[str]:
							_UpperCAmelCase					=    torch.manual_seed(0   )

							_UpperCAmelCase					=    StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''				,   safety_checker=a_				,   torch_dtype=torch.floataa   )
							_UpperCAmelCase					=    DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase					=    DDIMInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=a_   )

							_UpperCAmelCase					=    "a bowl of fruit"
							_UpperCAmelCase					=    "a bowl of pears"

							_UpperCAmelCase					=    pipe.generate_mask(
							    image=self.raw_image				,   source_prompt=a_				,   target_prompt=a_				,   generator=a_				,   )

							_UpperCAmelCase					=    pipe.invert(
							    prompt=a_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=a_   ).latents

							_UpperCAmelCase					=    pipe(
							    prompt=a_				,   mask_image=a_				,   image_latents=a_				,   generator=a_				,   negative_prompt=a_				,   inpaint_strength=0.7				,   output_type='''numpy'''				,   ).images[0]

							_UpperCAmelCase					=    (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((7_6_8, 7_6_8)   )   )
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1



			def  lowercase__						(		self     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    torch.manual_seed(0   )

							_UpperCAmelCase					=    StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''				,   safety_checker=a_				,   torch_dtype=torch.floataa   )
							_UpperCAmelCase					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase					=    DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=a_   )

							_UpperCAmelCase					=    "a bowl of fruit"
							_UpperCAmelCase					=    "a bowl of pears"

							_UpperCAmelCase					=    pipe.generate_mask(
							    image=self.raw_image				,   source_prompt=a_				,   target_prompt=a_				,   generator=a_				,   )

							_UpperCAmelCase					=    pipe.invert(
							    prompt=a_				,   image=self.raw_image				,   inpaint_strength=0.7				,   generator=a_				,   num_inference_steps=2_5				,   ).latents

							_UpperCAmelCase					=    pipe(
							    prompt=a_				,   mask_image=a_				,   image_latents=a_				,   generator=a_				,   negative_prompt=a_				,   inpaint_strength=0.7				,   num_inference_steps=2_5				,   output_type='''numpy'''				,   ).images[0]

							_UpperCAmelCase					=    (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((7_6_8, 7_6_8)   )   )
							    / 2_5_5
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							List[str]										=   {
    "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   :							List[str]										=   AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    create_model(
				    '''HTSAT-tiny'''					,     '''roberta'''					,     _SCREAMING_SNAKE_CASE					,     precision='''fp32'''					,     device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,     enable_fusion=_SCREAMING_SNAKE_CASE					,     fusion_type='''aff_2d''' if enable_fusion else None					,     )
				return model, model_cfg







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    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:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

								if re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												# replace sequential layers with list
												_UpperCAmelCase					=    re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)

												_UpperCAmelCase					=    key.replace(f'sequential.{sequential_layer}.'					,     f'layers.{int(_SCREAMING_SNAKE_CASE				)//3}.linear.'				)
								elif re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    int(re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)				)

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

												_UpperCAmelCase					=    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
												_UpperCAmelCase					=    value
												_UpperCAmelCase					=    mixed_qkv.size(0				) // 3

												_UpperCAmelCase					=    mixed_qkv[:qkv_dim]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim : qkv_dim * 2]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim * 2 :]

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    ClapModel(_SCREAMING_SNAKE_CASE				)

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

				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				transformers_config.save_pretrained(_SCREAMING_SNAKE_CASE				)


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

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



