


"""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()
SCREAMING_SNAKE_CASE :    str									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    int									=				{
    """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""",
}
SCREAMING_SNAKE_CASE :    List[str]									=				[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[str]     ,   _snake_case						:      Any     ,   _snake_case						:      Any     )     ->Tuple:
	"""simple docstring"""





	for attribute in key.split('''.'''     ):
		__snake_case	:						Optional[Any]						=				getattr(_snake_case     ,   _snake_case     )

	if weight_type is not None:
		__snake_case	:						Tuple						=				getattr(_snake_case     ,   _snake_case     ).shape
	else:
		__snake_case	:						List[str]						=				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	:						int						=				value
	elif weight_type == "weight_g":
		__snake_case	:						List[str]						=				value
	elif weight_type == "weight_v":
		__snake_case	:						str						=				value
	elif weight_type == "bias":
		__snake_case	:						Any						=				value
	else:
		__snake_case	:						Dict						=				value

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






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	__snake_case	:						str						=				fairseq_model.state_dict()

	__snake_case	:						Optional[Any]						=				hf_model.feature_extractor

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

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

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






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Tuple     ,   _snake_case						:      Tuple     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      Union[str, Any]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						str						=				full_name.split('''conv_layers.'''     )[-1]
	__snake_case	:						Union[str, Any]						=				name.split('''.'''     )
	__snake_case	:						Optional[Any]						=				int(items[0]     )
	__snake_case	:						List[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	:						Optional[Any]						=				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	:						Union[str, Any]						=				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	:						Optional[int]						=				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	:						Union[str, Any]						=				value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(_snake_case     )






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      str=None     )     ->str:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				torch.load(_snake_case     )
	__snake_case	:						Any						=				WavLMConfigOrig(checkpoint['''cfg''']     )
	__snake_case	:						Tuple						=				WavLMOrig(_snake_case     )
	model.load_state_dict(checkpoint['''model''']     )
	model.eval()

	if config_path is not None:
		__snake_case	:						List[str]						=				WavLMConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Optional[Any]						=				WavLMConfig()

	__snake_case	:						int						=				WavLMModel(_snake_case     )

	recursively_load_weights(_snake_case     ,   _snake_case     )

	hf_wavlm.save_pretrained(_snake_case     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Union[str, 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""")
			SCREAMING_SNAKE_CASE :    Dict									=				parser.parse_args()
			convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









"""simple docstring"""
import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=ShapEPipeline
		lowerCamelCase__				=['prompt']
		lowerCamelCase__				=['prompt']
		lowerCamelCase__				=[
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCamelCase__				=False



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 32



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.time_input_dim * 4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 8



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
			return tokenizer



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Optional[int]						=				CLIPTextConfig(
			    bos_token_id=0				,       eos_token_id=2				,       hidden_size=self.text_embedder_hidden_size				,       projection_dim=self.text_embedder_hidden_size				,       intermediate_size=37				,       layer_norm_eps=1E-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=10_00				,       )
			return CLIPTextModelWithProjection(a_					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Optional[int]						=				{
			    '''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''',
			    '''encoder_hid_proj_type''': None,
			    '''added_emb_type''': None,
			}

			__snake_case	:						Optional[int]						=				PriorTransformer(**a_					)
			return model



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)

			__snake_case	:						Optional[int]						=				{
			    '''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	:						List[Any]						=				ShapERenderer(**a_					)
			return model



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.dummy_prior
			__snake_case	:						str						=				self.dummy_text_encoder
			__snake_case	:						str						=				self.dummy_tokenizer
			__snake_case	:						Tuple						=				self.dummy_renderer

			__snake_case	:						int						=				HeunDiscreteScheduler(
			    beta_schedule='''exp'''				,       num_train_timesteps=10_24				,       prediction_type='''sample'''				,       use_karras_sigmas=a_				,       clip_sample=a_				,       clip_sample_range=1.0				,       )
			__snake_case	:						Union[str, Any]						=				{
			    '''prior''': prior,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''renderer''': renderer,
			    '''scheduler''': scheduler,
			}

			return components



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

			if str(a_					).startswith('''mps'''					):
				__snake_case	:						Tuple						=				torch.manual_seed(a_					)
			else:
				__snake_case	:						Union[str, Any]						=				torch.Generator(device=a_					).manual_seed(a_					)
			__snake_case	:						Optional[int]						=				{
			    '''prompt''': '''horse''',
			    '''generator''': generator,
			    '''num_inference_steps''': 1,
			    '''frame_size''': 32,
			    '''output_type''': '''np''',
			}
			return inputs



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''cpu'''

			__snake_case	:						str						=				self.get_dummy_components()

			__snake_case	:						List[Any]						=				self.pipeline_class(**a_					)
			__snake_case	:						str						=				pipe.to(a_					)

			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				pipe(**self.get_dummy_inputs(a_					)					)
			__snake_case	:						List[str]						=				output.images[0]
			__snake_case	:						List[str]						=				image[0, -3:, -3:, -1]

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

			__snake_case	:						List[Any]						=				np.array(
			    [
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			    ]					)

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_inference_batch_consistent(batch_sizes=[1, 2]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				torch_device == '''cpu'''
			__snake_case	:						Tuple						=				True

			self._test_inference_batch_single_identical(
			    batch_size=2				,       test_max_difference=a_				,       relax_max_difference=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.get_dummy_components()
			__snake_case	:						int						=				self.pipeline_class(**a_					)
			__snake_case	:						int						=				pipe.to(a_					)
			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				1
			__snake_case	:						List[Any]						=				2

			__snake_case	:						int						=				self.get_dummy_inputs(a_					)

			for key in inputs.keys():
				if key in self.batch_params:
					__snake_case	:						Dict						=				batch_size * [inputs[key]]

			__snake_case	:						Union[str, Any]						=				pipe(**a_				,       num_images_per_prompt=a_					)[0]

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



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/shap_e/test_shap_e_np_out.npy'''					)
			__snake_case	:						Optional[Any]						=				ShapEPipeline.from_pretrained('''openai/shap-e'''					)
			__snake_case	:						Optional[int]						=				pipe.to(a_					)
			pipe.set_progress_bar_config(disable=a_					)

			__snake_case	:						Optional[Any]						=				torch.Generator(device=a_					).manual_seed(0					)

			__snake_case	:						str						=				pipe(
			    '''a shark'''				,       generator=a_				,       guidance_scale=15.0				,       num_inference_steps=64				,       frame_size=64				,       output_type='''np'''				,       ).images[0]

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

			assert_mean_pixel_difference(a_				,       a_					)





