





'''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_					:    str      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    '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_					:    Optional[int]      	=			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      List[Any]    ,							lowercase						:      str						)	->	int:
				for attribute in key.split("."						):
								_a								= getattr(lowercase    ,							lowercase						)

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

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






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      str						)	->	Optional[Any]:
				_a								= []
				_a								= fairseq_model.state_dict()

				_a								= hf_model.feature_extractor

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

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

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






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






@torch.no_grad()
def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      str    ,							lowercase						:      List[Any]=None						)	->	Union[str, Any]:
				# load the pre-trained checkpoints
				_a								= torch.load(lowercase						)
				_a								= WavLMConfigOrig(checkpoint["cfg"]						)
				_a								= WavLMOrig(lowercase						)
				model.load_state_dict(checkpoint["model"]						)
				model.eval()

				if config_path is not None:
								_a								= WavLMConfig.from_pretrained(lowercase						)
				else:
								_a								= WavLMConfig()

				_a								= WavLMModel(lowercase						)

				recursively_load_weights(lowercase    ,							lowercase						)

				hf_wavlm.save_pretrained(lowercase						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Tuple      	=			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_					:    List[Any]      	=			parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				decoder_start_token_id=__a					,				**__a					,				)
