


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__  :List[str]             =							logging.get_logger(__name__)

lowerCAmelCase__  :str             =							{
    '''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__  :Union[str, Any]             =							[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def 	lowerCAmelCase__			(      a__:		List[Any]      ,  a__:		List[Any]      ,  a__:		Optional[Any]      ,  a__:		Tuple      ,  a__:		List[str]	)  ->		Tuple:


							'''simple docstring'''





							for attribute in key.split('.'	):
														_UpperCAmelCase					=      getattr(a__      ,  a__	)

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

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

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		List[Any]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      fairseq_model.state_dict()

							_UpperCAmelCase					=      hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														_UpperCAmelCase					=      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    a__      ,  a__      ,  a__      ,  a__      ,  hf_model.config.feat_extract_norm == 'group'      ,  )
																					_UpperCAmelCase					=      True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
																																			_UpperCAmelCase					=      True
																																			if "*" in mapped_key:
																																										_UpperCAmelCase					=      name.split(a__	)[0].split('.'	)[-2]
																																										_UpperCAmelCase					=      mapped_key.replace('*'      ,  a__	)
																																			if "weight_g" in name:
																																										_UpperCAmelCase					=      'weight_g'
																																			elif "weight_v" in name:
																																										_UpperCAmelCase					=      'weight_v'
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										_UpperCAmelCase					=      'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										_UpperCAmelCase					=      'weight'
																																			else:
																																										_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		Dict      ,  a__:		Optional[int]      ,  a__:		Optional[Any]      ,  a__:		Dict	)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      full_name.split('conv_layers.'	)[-1]
							_UpperCAmelCase					=      name.split('.'	)
							_UpperCAmelCase					=      int(items[0]	)
							_UpperCAmelCase					=      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.'''
																					)
																					_UpperCAmelCase					=      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.'''
																					)
																					_UpperCAmelCase					=      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."
																					)
																					_UpperCAmelCase					=      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.'''
																					)
																					_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		str      ,  a__:		List[str]=None	)  ->		Tuple:


							'''simple docstring'''





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

							if config_path is not None:
														_UpperCAmelCase					=      WavLMConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      WavLMConfig()

							_UpperCAmelCase					=      WavLMModel(a__	)

							recursively_load_weights(a__      ,  a__	)

							hf_wavlm.save_pretrained(a__	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							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__  :Any             =							parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




from __future__ import annotations

from PIL import Image

# Define glider example
lowerCAmelCase__  :str             =							[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowerCAmelCase__  :Any             =							[[0, 1, 0], [0, 1, 0], [0, 1, 0]]

def 	lowerCAmelCase__			(      a__:		list[list[int]]	)  ->		list[list[int]]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for i in range(len(a__	)	):
														_UpperCAmelCase					=      []
														for j in range(len(cells[i]	)	):
																					# Get the number of live neighbours
																					_UpperCAmelCase					=      0
																					if i > 0 and j > 0:
																												neighbour_count += cells[i - 1][j - 1]
																					if i > 0:
																												neighbour_count += cells[i - 1][j]
																					if i > 0 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i - 1][j + 1]
																					if j > 0:
																												neighbour_count += cells[i][j - 1]
																					if j < len(cells[i]	) - 1:
																												neighbour_count += cells[i][j + 1]
																					if i < len(a__	) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(a__	) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(a__	) - 1 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i + 1][j + 1]

																					# Rules of the game of life (excerpt from Wikipedia):
																					# 1. Any live cell with two or three live neighbours survives.
																					# 2. Any dead cell with three live neighbours becomes a live cell.
																					# 3. All other live cells die in the next generation.
																					#    Similarly, all other dead cells stay dead.
																					_UpperCAmelCase					=      cells[i][j] == 1
																					if (
																					    (alive and 2 <= neighbour_count <= 3)
																					    or not alive
																					    and neighbour_count == 3
																					):
																												next_generation_row.append(1	)
																					else:
																												next_generation_row.append(0	)

														next_generation.append(a__	)
							return next_generation

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[Image.Image]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for _ in range(a__	):
														# Create output image
														_UpperCAmelCase					=      Image.new('RGB'      ,  (len(cells[0]	), len(a__	))	)
														_UpperCAmelCase					=      img.load()

														# Save cells to image
														for x in range(len(a__	)	):
																					for y in range(len(cells[0]	)	):
																												_UpperCAmelCase					=      2_5_5 - cells[y][x] * 2_5_5
																												_UpperCAmelCase					=      (colour, colour, colour)

        # Save image
														images.append(a__	)
														_UpperCAmelCase					=      new_generation(a__	)
							return images


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							generate_images(GLIDER, 1_6)
		images[0].save('''out.gif''', save_all=True, append_images=images[1:])
