
'''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()
__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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',
}
__lowerCAmelCase												=	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							for attribute in key.split("""."""				):
														_snake_case       =  getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							if weight_type is not None:
														_snake_case       =  getattr(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				).shape
							else:
														_snake_case       =  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       =  value
							elif weight_type == "weight_g":
														_snake_case       =  value
							elif weight_type == "weight_v":
														_snake_case       =  value
							elif weight_type == "bias":
														_snake_case       =  value
							else:
														_snake_case       =  value

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  []
							_snake_case       =  fairseq_model.state_dict()

							_snake_case       =  hf_model.feature_extractor

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

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

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  full_name.split("""conv_layers."""				)[-1]
							_snake_case       =  name.split("""."""				)
							_snake_case       =  int(items[0]				)
							_snake_case       =  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       =  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       =  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       =  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       =  value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(_SCREAMING_SNAKE_CASE				)






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=None				):
							# load the pre-trained checkpoints
							_snake_case       =  torch.load(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  WavLMConfigOrig(checkpoint["""cfg"""]				)
							_snake_case       =  WavLMOrig(_SCREAMING_SNAKE_CASE				)
							model.load_state_dict(checkpoint["""model"""]				)
							model.eval()

							if config_path is not None:
														_snake_case       =  WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							else:
														_snake_case       =  WavLMConfig()

							_snake_case       =  WavLMModel(_SCREAMING_SNAKE_CASE				)

							recursively_load_weights(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							hf_wavlm.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__lowerCAmelCase												=	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')
				__lowerCAmelCase												=	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 diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

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

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


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







							'''simple docstring'''

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

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1