




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase__				     = [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			str				)    -> int:
							'''simple docstring'''


							for attribute in key.split('''.'''				):
														__lowercase      =    getattr(UpperCamelCase__				,       UpperCamelCase__				)

							if weight_type is not None:
														__lowercase      =    getattr(UpperCamelCase__				,       UpperCamelCase__				).shape
							else:
														__lowercase      =    hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
							    F' {value.shape} for {full_name}'
							)

							if weight_type == "weight":
														__lowercase      =    value
							elif weight_type == "weight_g":
														__lowercase      =    value
							elif weight_type == "weight_v":
														__lowercase      =    value
							elif weight_type == "bias":
														__lowercase      =    value
							else:
														__lowercase      =    value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			int				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    []
							__lowercase      =    fairseq_model.state_dict()

							__lowercase      =    hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														__lowercase      =    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       hf_model.config.feat_extract_norm == '''group'''				,       )
																					__lowercase      =    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																																			__lowercase      =    True
																																			if "*" in mapped_key:
																																										__lowercase      =    name.split(UpperCamelCase__				)[0].split('''.'''				)[-2]
																																										__lowercase      =    mapped_key.replace('''*'''				,       UpperCamelCase__				)
																																			if "weight_g" in name:
																																										__lowercase      =    '''weight_g'''
																																			elif "weight_v" in name:
																																										__lowercase      =    '''weight_v'''
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										__lowercase      =    '''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__lowercase      =    '''weight'''
																																			else:
																																										__lowercase      =    None

																																			set_recursively(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase__				)

							logger.warning(F'Unused weights: {unused_weights}'				)







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[Any]				)    -> Optional[Any]:
							'''simple docstring'''


							__lowercase      =    full_name.split('''conv_layers.'''				)[-1]
							__lowercase      =    name.split('''.'''				)
							__lowercase      =    int(items[0]				)
							__lowercase      =    int(items[1]				)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																					)
																					__lowercase      =    value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																					)
																					__lowercase      =    value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																					    " found."
																					)
																					__lowercase      =    value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																					)
																					__lowercase      =    value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
							else:
														unused_weights.append(UpperCamelCase__				)







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Dict=None				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    torch.load(UpperCamelCase__				)
							__lowercase      =    WavLMConfigOrig(checkpoint['''cfg''']				)
							__lowercase      =    WavLMOrig(UpperCamelCase__				)
							model.load_state_dict(checkpoint['''model''']				)
							model.eval()

							if config_path is not None:
														__lowercase      =    WavLMConfig.from_pretrained(UpperCamelCase__				)
							else:
														__lowercase      =    WavLMConfig()

							__lowercase      =    WavLMModel(UpperCamelCase__				)

							recursively_load_weights(UpperCamelCase__				,       UpperCamelCase__				)

							hf_wavlm.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
			UpperCAmelCase__				     = 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")
			UpperCAmelCase__				     = parser.parse_args()
			convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		CycleDiffusionPipeline
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						UpperCamelCase_	:       int       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       Dict       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
						UpperCamelCase_	:       Dict       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       Optional[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    UNetaDConditionModel(
													    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,							cross_attention_dim=32				,							)
													__lowercase      =    DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='''scaled_linear'''				,							num_train_timesteps=1_000				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							)
													torch.manual_seed(0				)
													__lowercase      =    AutoencoderKL(
													    block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,							latent_channels=4				,							)
													torch.manual_seed(0				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__lowercase      =    CLIPTextModel(lowerCamelCase__				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    image / 2 + 0.5
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''prompt''': '''An astronaut riding an elephant''',
													    '''source_prompt''': '''An astronaut riding a horse''',
													    '''image''': image,
													    '''generator''': generator,
													    '''num_inference_steps''': 2,
													    '''eta''': 0.1,
													    '''strength''': 0.8,
													    '''guidance_scale''': 3,
													    '''source_guidance_scale''': 1,
													    '''output_type''': '''numpy''',
													}
													return inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

													__lowercase      =    self.get_dummy_components()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

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






						@unittest.skipIf(torch_device != '''cuda'''				,							'''This test requires a GPU'''				)
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(lowerCamelCase__				,							'''half'''				):
																											__lowercase      =    module.half()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

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






						@skip_mps
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_local()






						@unittest.skip('''non-deterministic pipeline'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return super().test_inference_batch_single_identical()






						@skip_mps
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_optional_components()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													return super().test_attention_slicing_forward_pass()







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

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(
													    lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				,							torch_dtype=torch.floataa				,							revision='''fp16'''				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image				).max() < 5e-1






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													assert np.abs(image - expected_image				).max() < 2e-2






