

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						:  str									=			logging.get_logger(__name__)

_UpperCAmelCase						:  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""",
}
_UpperCAmelCase						:  Tuple									=			[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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

							if weight_type is not None:
														snake_case_     							=       getattr(UpperCamelCase__  ,				UpperCamelCase__ ).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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							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(
																					    UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				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(UpperCamelCase__ )[0].split('.' )[-2]
																																										snake_case_     							=       mapped_key.replace('*'  ,				UpperCamelCase__ )
																																			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(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase__ )

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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							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(UpperCamelCase__ )





@torch.no_grad()
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=None ):







							'''simple docstring'''






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

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

							snake_case_     							=       WavLMModel(UpperCamelCase__ )

							recursively_load_weights(UpperCamelCase__  ,				UpperCamelCase__ )

							hf_wavlm.save_pretrained(UpperCamelCase__ )


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







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




