
'''simple docstring'''



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__A								=			logging.get_logger(__name__)

__A								=			{
    "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_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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__A								=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        _A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						for attribute in key.split("""."""		):
												lowercase__							       =				getattr(lowercase__						,						lowercase__		)

						if weight_type is not None:
												lowercase__							       =				getattr(lowercase__						,						lowercase__		).shape
						else:
												lowercase__							       =				hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    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":
												lowercase__							       =				value
						elif weight_type == "weight_g":
												lowercase__							       =				value
						elif weight_type == "weight_v":
												lowercase__							       =				value
						elif weight_type == "bias":
												lowercase__							       =				value
						else:
												lowercase__							       =				value

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





def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				[]
						lowercase__							       =				fairseq_model.state_dict()

						lowercase__							       =				hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase__							       =				False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						hf_model.config.feat_extract_norm == """group"""						,						)
																		lowercase__							       =				True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase__							       =				"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
																														if "layer_norm_for_extract" in name and (".".join(name.split("""."""		)[:-1]		) != key):
																																				# special case since naming is very similar
																																				continue
																														lowercase__							       =				True
																														if "*" in mapped_key:
																																				lowercase__							       =				name.split(lowercase__		)[0].split("""."""		)[-2]
																																				lowercase__							       =				mapped_key.replace("""*"""						,						lowercase__		)
																														if "weight_g" in name:
																																				lowercase__							       =				"""weight_g"""
																														elif "weight_v" in name:
																																				lowercase__							       =				"""weight_v"""
																														elif "bias" in name:
																																				lowercase__							       =				"""bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase__							       =				"""weight"""
																														else:
																																				lowercase__							       =				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        _A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				full_name.split("""conv_layers."""		)[-1]
						lowercase__							       =				name.split("""."""		)
						lowercase__							       =				int(items[0]		)
						lowercase__							       =				int(items[1]		)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''		)
																		lowercase__							       =				value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''		)
																		lowercase__							       =				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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''		)
																		lowercase__							       =				value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''		)
																		lowercase__							       =				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        _A						(							lowercase__						,						lowercase__						,						lowercase__=None						,						lowercase__=None						,						lowercase__=True		):
						if config_path is not None:
												lowercase__							       =				UniSpeechSatConfig.from_pretrained(lowercase__		)
						else:
												lowercase__							       =				UniSpeechSatConfig()

						lowercase__							       =				""""""

						if is_finetuned:
												lowercase__							       =				UniSpeechSatForCTC(lowercase__		)
						else:
												lowercase__							       =				UniSpeechSatForPreTraining(lowercase__		)

						lowercase__							       =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]						,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
						lowercase__							       =				model[0].eval()

						recursively_load_weights(lowercase__						,						lowercase__		)

						hf_wavavec.save_pretrained(lowercase__		)


if __name__ == "__main__":
				__A								=			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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				__A								=			parser.parse_args()
				convert_unispeech_sat_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)











"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

A:   Dict								=		{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						'conditional_detr'
   __lowerCAmelCase  :   Union[str, Any]						=						['past_key_values']
   __lowerCAmelCase  :   int						=						{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=300      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=6      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="relu"      ,     _SCREAMING_SNAKE_CASE=256      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1.0      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="sine"      ,     _SCREAMING_SNAKE_CASE="resnet50"      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.25      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Tuple:





    '''simple docstring'''




    if backbone_config is not None and use_timm_backbone:
     raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""						)

    if not use_timm_backbone:
     if backbone_config is None:
      logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
      UpperCAmelCase	:					Tuple	=	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
     elif isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					str	=	backbone_config.get("""model_type"""						)
      UpperCAmelCase	:					int	=	CONFIG_MAPPING[backbone_model_type]
      UpperCAmelCase	:					Union[str, Any]	=	config_class.from_dict(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	use_timm_backbone
    UpperCAmelCase	:					Optional[int]	=	backbone_config
    UpperCAmelCase	:					List[str]	=	num_channels
    UpperCAmelCase	:					Any	=	num_queries
    UpperCAmelCase	:					Union[str, Any]	=	d_model
    UpperCAmelCase	:					List[str]	=	encoder_ffn_dim
    UpperCAmelCase	:					Optional[int]	=	encoder_layers
    UpperCAmelCase	:					Union[str, Any]	=	encoder_attention_heads
    UpperCAmelCase	:					Optional[Any]	=	decoder_ffn_dim
    UpperCAmelCase	:					Any	=	decoder_layers
    UpperCAmelCase	:					Optional[int]	=	decoder_attention_heads
    UpperCAmelCase	:					Optional[int]	=	dropout
    UpperCAmelCase	:					Dict	=	attention_dropout
    UpperCAmelCase	:					Dict	=	activation_dropout
    UpperCAmelCase	:					Any	=	activation_function
    UpperCAmelCase	:					Any	=	init_std
    UpperCAmelCase	:					Tuple	=	init_xavier_std
    UpperCAmelCase	:					Optional[int]	=	encoder_layerdrop
    UpperCAmelCase	:					Any	=	decoder_layerdrop
    UpperCAmelCase	:					Any	=	encoder_layers
    UpperCAmelCase	:					Optional[Any]	=	auxiliary_loss
    UpperCAmelCase	:					List[Any]	=	position_embedding_type
    UpperCAmelCase	:					Union[str, Any]	=	backbone
    UpperCAmelCase	:					List[Any]	=	use_pretrained_backbone
    UpperCAmelCase	:					Dict	=	dilation
    # Hungarian matcher
    UpperCAmelCase	:					Optional[int]	=	class_cost
    UpperCAmelCase	:					List[str]	=	bbox_cost
    UpperCAmelCase	:					List[str]	=	giou_cost
    # Loss coefficients
    UpperCAmelCase	:					List[Any]	=	mask_loss_coefficient
    UpperCAmelCase	:					List[str]	=	dice_loss_coefficient
    UpperCAmelCase	:					Optional[int]	=	cls_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	bbox_loss_coefficient
    UpperCAmelCase	:					Union[str, Any]	=	giou_loss_coefficient
    UpperCAmelCase	:					int	=	focal_alpha
    super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.encoder_attention_heads

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.d_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	copy.deepcopy(self.__dict__						)
    if self.backbone_config is not None:
     UpperCAmelCase	:					Union[str, Any]	=	self.backbone_config.to_dict()
    UpperCAmelCase	:					Dict	=	self.__class__.model_type
    return output



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						version.parse('1.11' )

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ("""pixel_mask""", {0: """batch"""}),
        ]						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      float:





    '''simple docstring'''




    return 1E-5




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 12
