
'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 						a   (				__magic_name__				,unittest.TestCase    ):
				_snake_case		    = ShapEImgaImgPipeline
				_snake_case		    = ['''image''']
				_snake_case		    = ['''image''']
				_snake_case		    = [
				    '''num_images_per_prompt''',
				    '''num_inference_steps''',
				    '''generator''',
				    '''latents''',
				    '''guidance_scale''',
				    '''frame_size''',
				    '''output_type''',
				    '''return_dict''',
				]
				_snake_case		    = False




				@property
				def 	__snake_case							(							self	:				str      ):
										return 32




				@property
				def 	__snake_case							(							self	:				Optional[int]      ):
										return 32




				@property
				def 	__snake_case							(							self	:				int      ):
										return self.time_input_dim * 4




				@property
				def 	__snake_case							(							self	:				List[Any]      ):
										return 8




				@property
				def 	__snake_case							(							self	:				Optional[Any]      ):
										torch.manual_seed(0      )
										snake_case       :     List[str] =    CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size,    image_size=64,    projection_dim=self.text_embedder_hidden_size,    intermediate_size=37,    num_attention_heads=4,    num_channels=3,    num_hidden_layers=5,    patch_size=1,    )

										snake_case       :     List[Any] =    CLIPVisionModel(SCREAMING_SNAKE_CASE_      )
										return model




				@property
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[int] =    CLIPImageProcessor(
										    crop_size=2_24,    do_center_crop=SCREAMING_SNAKE_CASE_,    do_normalize=SCREAMING_SNAKE_CASE_,    do_resize=SCREAMING_SNAKE_CASE_,    image_mean=[0.48145466, 0.4578275, 0.40821073],    image_std=[0.26862954, 0.26130258, 0.27577711],    resample=3,    size=2_24,    )

										return image_processor




				@property
				def 	__snake_case							(							self	:				Optional[int]      ):
										torch.manual_seed(0      )

										snake_case       :     List[Any] =    {
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 16,
										    '''embedding_dim''': self.time_input_dim,
										    '''num_embeddings''': 32,
										    '''embedding_proj_dim''': self.text_embedder_hidden_size,
										    '''time_embed_dim''': self.time_embed_dim,
										    '''num_layers''': 1,
										    '''clip_embed_dim''': self.time_input_dim * 2,
										    '''additional_embeddings''': 0,
										    '''time_embed_act_fn''': '''gelu''',
										    '''norm_in_type''': '''layer''',
										    '''embedding_proj_norm_type''': '''layer''',
										    '''encoder_hid_proj_type''': None,
										    '''added_emb_type''': None,
										}

										snake_case       :     List[str] =    PriorTransformer(**SCREAMING_SNAKE_CASE_      )
										return model




				@property
				def 	__snake_case							(							self	:				Dict      ):
										torch.manual_seed(0      )

										snake_case       :     List[str] =    {
										    '''param_shapes''': (
										        (self.renderer_dim, 93),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										        (self.renderer_dim, 8),
										    ),
										    '''d_latent''': self.time_input_dim,
										    '''d_hidden''': self.renderer_dim,
										    '''n_output''': 12,
										    '''background''': (
										        0.1,
										        0.1,
										        0.1,
										    ),
										}
										snake_case       :     Optional[Any] =    ShapERenderer(**SCREAMING_SNAKE_CASE_      )
										return model




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Any =    self.dummy_prior
										snake_case       :     str =    self.dummy_image_encoder
										snake_case       :     Dict =    self.dummy_image_processor
										snake_case       :     Tuple =    self.dummy_renderer

										snake_case       :     Optional[int] =    HeunDiscreteScheduler(
										    beta_schedule='''exp''',    num_train_timesteps=10_24,    prediction_type='''sample''',    use_karras_sigmas=SCREAMING_SNAKE_CASE_,    clip_sample=SCREAMING_SNAKE_CASE_,    clip_sample_range=1.0,    )
										snake_case       :     Dict =    {
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''image_processor''': image_processor,
										    '''renderer''': renderer,
										    '''scheduler''': scheduler,
										}

										return components




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0      ):
										snake_case       :     int =    floats_tensor((1, 3, 64, 64),    rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )

										if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																snake_case       :     Dict =    torch.manual_seed(SCREAMING_SNAKE_CASE_      )
										else:
																snake_case       :     Union[str, Any] =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    {
										    '''image''': input_image,
										    '''generator''': generator,
										    '''num_inference_steps''': 1,
										    '''frame_size''': 32,
										    '''output_type''': '''np''',
										}
										return inputs




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     List[str] =    '''cpu'''

										snake_case       :     Optional[Any] =    self.get_dummy_components()

										snake_case       :     List[str] =    self.pipeline_class(**SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    pipe.to(SCREAMING_SNAKE_CASE_      )

										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Any =    pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )      )
										snake_case       :     Dict =    output.images[0]
										snake_case       :     Optional[int] =    image[0, -3:, -3:, -1]

										assert image.shape == (20, 32, 32, 3)

										snake_case       :     Optional[int] =    np.array(
										    [
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										        0.00039216,
										    ]      )

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




				def 	__snake_case							(							self	:				Optional[Any]      ):
										# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
										self._test_inference_batch_consistent(batch_sizes=[1, 2]      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     Union[str, Any] =    torch_device == '''cpu'''
										snake_case       :     int =    True
										self._test_inference_batch_single_identical(
										    batch_size=2,    test_max_difference=SCREAMING_SNAKE_CASE_,    relax_max_difference=SCREAMING_SNAKE_CASE_,    )




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Tuple =    self.get_dummy_components()
										snake_case       :     Union[str, Any] =    self.pipeline_class(**SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    pipe.to(SCREAMING_SNAKE_CASE_      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[str] =    1
										snake_case       :     Optional[Any] =    2

										snake_case       :     int =    self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )

										for key in inputs.keys():
																if key in self.batch_params:
																						snake_case       :     Optional[int] =    batch_size * [inputs[key]]

										snake_case       :     Dict =    pipe(**SCREAMING_SNAKE_CASE_,    num_images_per_prompt=SCREAMING_SNAKE_CASE_      )[0]

										assert images.shape[0] == batch_size * num_images_per_prompt




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




				def 	__snake_case							(							self	:				Optional[int]      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Dict =    load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
										snake_case       :     Union[str, Any] =    load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/shap_e/test_shap_e_img2img_out.npy'''      )
										snake_case       :     Union[str, Any] =    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
										snake_case       :     Any =    pipe.to(SCREAMING_SNAKE_CASE_      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Any =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )

										snake_case       :     List[str] =    pipe(
										    SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=3.0,    num_inference_steps=64,    frame_size=64,    output_type='''np''',    ).images[0]

										assert images.shape == (20, 64, 64, 3)

										assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )





'''simple docstring'''





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 (       A_							:					Dict    ,							A_							:					Any    ,							A_							:					List[str]    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						for attribute in key.split('''.'''       ):
												snake_case       :     Tuple =    getattr(A_    ,							A_       )

						if weight_type is not None:
												snake_case       :     Optional[Any] =    getattr(A_    ,							A_       ).shape
						else:
												snake_case       :     Any =    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":
												snake_case       :     Optional[Any] =    value
						elif weight_type == "weight_g":
												snake_case       :     Any =    value
						elif weight_type == "weight_v":
												snake_case       :     int =    value
						elif weight_type == "bias":
												snake_case       :     Any =    value
						else:
												snake_case       :     Optional[int] =    value

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

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     Dict =    []
						snake_case       :     Optional[Any] =    fairseq_model.state_dict()

						snake_case       :     int =    hf_model.feature_extractor

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

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

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

def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[str] =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     List[Any] =    name.split('''.'''       )
						snake_case       :     Dict =    int(items[0]       )
						snake_case       :     Any =    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."""
																		)
																		snake_case       :     Tuple =    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."""
																		)
																		snake_case       :     Dict =    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."
																		)
																		snake_case       :     str =    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."""
																		)
																		snake_case       :     Tuple =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(A_       )

@torch.no_grad()
def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Optional[Any]=None       ):
						# load the pre-trained checkpoints
						snake_case       :     Any =    torch.load(A_       )
						snake_case       :     List[str] =    WavLMConfigOrig(checkpoint['''cfg''']       )
						snake_case       :     Union[str, Any] =    WavLMOrig(A_       )
						model.load_state_dict(checkpoint['''model''']       )
						model.eval()

						if config_path is not None:
												snake_case       :     Union[str, Any] =    WavLMConfig.from_pretrained(A_       )
						else:
												snake_case       :     Any =    WavLMConfig()

						snake_case       :     Optional[int] =    WavLMModel(A_       )

						recursively_load_weights(A_    ,							A_       )

						hf_wavlm.save_pretrained(A_       )


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)

