
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCamelCase_  :	int 				=					{
    """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""",
}
lowerCamelCase_  :	int 				=					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:





				for attribute in key.split("""."""  ):
								UpperCamelCase_:							Optional[int]		=  getattr(_snake_case       ,      _snake_case  )

				if weight_type is not None:
								UpperCamelCase_:							List[str]		=  getattr(_snake_case       ,      _snake_case  ).shape
				else:
								UpperCamelCase_:							Dict		=  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":
								UpperCamelCase_:							Optional[int]		=  value
				elif weight_type == "weight_g":
								UpperCamelCase_:							Union[str, Any]		=  value
				elif weight_type == "weight_v":
								UpperCamelCase_:							Tuple		=  value
				elif weight_type == "bias":
								UpperCamelCase_:							Optional[Any]		=  value
				else:
								UpperCamelCase_:							Dict		=  value

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		int:
				UpperCamelCase_:							str		=  []
				UpperCamelCase_:							int		=  fairseq_model.state_dict()

				UpperCamelCase_:							str		=  hf_model.unispeech_sat.feature_extractor

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							List[Any]		=  False
								if "conv_layers" in name:
												load_conv_layer(
												    _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      hf_model.config.feat_extract_norm == """group"""       ,      )
												UpperCamelCase_:							Union[str, Any]		=  True
								else:
												for key, mapped_key in MAPPING.items():
																UpperCamelCase_:							List[Any]		=  '''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
																				UpperCamelCase_:							List[str]		=  True
																				if "*" in mapped_key:
																								UpperCamelCase_:							Optional[Any]		=  name.split(_snake_case  )[0].split("""."""  )[-2]
																								UpperCamelCase_:							Dict		=  mapped_key.replace("""*"""       ,      _snake_case  )
																				if "weight_g" in name:
																								UpperCamelCase_:							List[Any]		=  '''weight_g'''
																				elif "weight_v" in name:
																								UpperCamelCase_:							Optional[Any]		=  '''weight_v'''
																				elif "bias" in name:
																								UpperCamelCase_:							Union[str, Any]		=  '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCamelCase_:							List[str]		=  '''weight'''
																				else:
																								UpperCamelCase_:							Dict		=  None
																				set_recursively(_snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case  )
																continue
								if not is_used:
												unused_weights.append(_snake_case  )

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







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







@torch.no_grad()
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=None       ,      lowerCamelCase=None       ,      lowerCamelCase=True  )					->		str:





				if config_path is not None:
								UpperCamelCase_:							int		=  UniSpeechSatConfig.from_pretrained(_snake_case  )
				else:
								UpperCamelCase_:							str		=  UniSpeechSatConfig()

				UpperCamelCase_:							Optional[Any]		=  ''''''

				if is_finetuned:
								UpperCamelCase_:							Optional[Any]		=  UniSpeechSatForCTC(_snake_case  )
				else:
								UpperCamelCase_:							Tuple		=  UniSpeechSatForPreTraining(_snake_case  )

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

				recursively_load_weights(_snake_case       ,      _snake_case  )

				hf_wavavec.save_pretrained(_snake_case  )


if __name__ == "__main__":
				lowerCamelCase_  :	Optional[Any] 				=					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"""
				)
				lowerCamelCase_  :	int 				=					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
				)








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


lowerCamelCase_  :	Tuple 				=					logging.get_logger(__name__)

lowerCamelCase_  :	str 				=					{
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	"""lxmert"""
			__UpperCamelCase :  Optional[Any]    =	{}

			def __init__(    self	:    Dict  ,		snake_case_	:    List[str]=3_0522  ,		snake_case_	:    int=768  ,		snake_case_	:    str=12  ,		snake_case_	:    Tuple=9500  ,		snake_case_	:    Optional[Any]=1600  ,		snake_case_	:    str=400  ,		snake_case_	:    List[str]=3072  ,		snake_case_	:    Dict="gelu"  ,		snake_case_	:    Optional[Any]=0.1  ,		snake_case_	:    int=0.1  ,		snake_case_	:    Optional[int]=512  ,		snake_case_	:    Tuple=2  ,		snake_case_	:    Optional[int]=0.02  ,		snake_case_	:    Union[str, Any]=1e-12  ,		snake_case_	:    Tuple=9  ,		snake_case_	:    Tuple=5  ,		snake_case_	:    Union[str, Any]=5  ,		snake_case_	:    List[str]=2048  ,		snake_case_	:    Any=4  ,		snake_case_	:    int=6.67  ,		snake_case_	:    Dict=True  ,		snake_case_	:    Tuple=True  ,		snake_case_	:    int=True  ,		snake_case_	:    str=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[str]=True  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							int		=  vocab_size
							UpperCamelCase_:							Tuple		=  hidden_size
							UpperCamelCase_:							Optional[Any]		=  num_attention_heads
							UpperCamelCase_:							List[str]		=  hidden_act
							UpperCamelCase_:							int		=  intermediate_size
							UpperCamelCase_:							Any		=  hidden_dropout_prob
							UpperCamelCase_:							List[Any]		=  attention_probs_dropout_prob
							UpperCamelCase_:							List[str]		=  max_position_embeddings
							UpperCamelCase_:							Optional[Any]		=  type_vocab_size
							UpperCamelCase_:							Optional[Any]		=  initializer_range
							UpperCamelCase_:							str		=  layer_norm_eps
							UpperCamelCase_:							List[str]		=  num_qa_labels
							UpperCamelCase_:							List[str]		=  num_object_labels
							UpperCamelCase_:							Tuple		=  num_attr_labels
							UpperCamelCase_:							List[Any]		=  l_layers
							UpperCamelCase_:							List[Any]		=  x_layers
							UpperCamelCase_:							Any		=  r_layers
							UpperCamelCase_:							str		=  visual_feat_dim
							UpperCamelCase_:							Union[str, Any]		=  visual_pos_dim
							UpperCamelCase_:							str		=  visual_loss_normalizer
							UpperCamelCase_:							Dict		=  task_matched
							UpperCamelCase_:							Tuple		=  task_mask_lm
							UpperCamelCase_:							List[str]		=  task_obj_predict
							UpperCamelCase_:							Any		=  task_qa
							UpperCamelCase_:							Dict		=  visual_obj_loss
							UpperCamelCase_:							Optional[Any]		=  visual_attr_loss
							UpperCamelCase_:							List[Any]		=  visual_feat_loss
							UpperCamelCase_:							Tuple		=  {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
							super().__init__(**snake_case_ )






