


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			:  Union[str, Any]     =logging.get_logger(__name__)

_UpperCAmelCase			:  Optional[Any]     ={
    """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			:  Optional[int]     =[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:
				for attribute in key.split('''.''' ):
								lowerCAmelCase_ :      int    				=				getattr(lowerCAmelCase_ ,   lowerCAmelCase_ )

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

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



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[str]:
				lowerCAmelCase_ :      Dict    				=				[]
				lowerCAmelCase_ :      str    				=				fairseq_model.state_dict()

				lowerCAmelCase_ :      Optional[Any]    				=				hf_model.feature_extractor

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

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

				logger.warning(f"""Unused weights: {unused_weights}""" )



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



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=None )-> Tuple:
				# load the pre-trained checkpoints
				lowerCAmelCase_ :      Any    				=				torch.load(lowerCAmelCase_ )
				lowerCAmelCase_ :      Dict    				=				WavLMConfigOrig(checkpoint['''cfg'''] )
				lowerCAmelCase_ :      Union[str, Any]    				=				WavLMOrig(lowerCAmelCase_ )
				model.load_state_dict(checkpoint['''model'''] )
				model.eval()

				if config_path is not None:
								lowerCAmelCase_ :      Any    				=				WavLMConfig.from_pretrained(lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      Tuple    				=				WavLMConfig()

				lowerCAmelCase_ :      Optional[Any]    				=				WavLMModel(lowerCAmelCase_ )

				recursively_load_weights(lowerCAmelCase_ ,   lowerCAmelCase_ )

				hf_wavlm.save_pretrained(lowerCAmelCase_ )


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



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# initialize a list to hold all the tweepy Tweets
				lowerCAmelCase_ :      Dict    				=				[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				lowerCAmelCase_ :      Optional[int]    				=				api.user_timeline(screen_name=lowerCAmelCase_ ,   count=200 )

				# save most recent tweets
				alltweets.extend(lowerCAmelCase_ )

				# save the id of the oldest tweet less one
				lowerCAmelCase_ :      str    				=				alltweets[-1].id - 1

				# keep grabbing tweets until there are no tweets left to grab
				while len(lowerCAmelCase_ ) > 0:
								print(f"""getting tweets before {oldest}""" )

								# all subsequent requests use the max_id param to prevent duplicates
								lowerCAmelCase_ :      Optional[Any]    				=				api.user_timeline(
								    screen_name=lowerCAmelCase_ ,   count=200 ,   max_id=lowerCAmelCase_ )

								# save most recent tweets
								alltweets.extend(lowerCAmelCase_ )

								# update the id of the oldest tweet less one
								lowerCAmelCase_ :      Optional[Any]    				=				alltweets[-1].id - 1

								print(f"""...{len(lowerCAmelCase_ )} tweets downloaded so far""" )

				# transform the tweepy tweets into a 2D array that will populate the csv
				lowerCAmelCase_ :      Union[str, Any]    				=				[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

				# write the csv
				with open(f"""new_{screen_name}_tweets.csv""" ,   '''w''' ) as f:
								lowerCAmelCase_ :      Optional[int]    				=				csv.writer(lowerCAmelCase_ )
								writer.writerow(['''id''', '''created_at''', '''text'''] )
								writer.writerows(lowerCAmelCase_ )


if __name__ == "__main__":
						# pass in the username of the account you want to download
						get_all_tweets("""FirePing32""")