
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowerCamelCase	:    int          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, 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_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	:    List[Any]          =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]






def 	a_		(	__lowercase       :     str	, __lowercase       :     List[str]	, __lowercase       :     str	, __lowercase       :     Optional[Any]	, __lowercase       :     Any			)			->  Optional[int]:






			"""simple docstring"""




			for attribute in key.split('.'			):
						_snake_case          =      getattr(__lowercase	, __lowercase			)

			if weight_type is not None:
						_snake_case          =      getattr(__lowercase	, __lowercase			).shape
			else:
						_snake_case          =      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":
						_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 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Tuple			)			->  Dict:






			"""simple docstring"""




			_snake_case          =      []
			_snake_case          =      fairseq_model.state_dict()

			_snake_case          =      hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						_snake_case          =      False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowercase	, __lowercase	, __lowercase	, __lowercase	, hf_model.config.feat_extract_norm == 'group'	, )
									_snake_case          =      True
						else:
									for key, mapped_key in MAPPING.items():
												_snake_case          =      '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
															_snake_case          =      True
															if "*" in mapped_key:
																		_snake_case          =      name.split(__lowercase			)[0].split('.'			)[-2]
																		_snake_case          =      mapped_key.replace('*'	, __lowercase			)
															if "weight_g" in name:
																		_snake_case          =      'weight_g'
															elif "weight_v" in name:
																		_snake_case          =      'weight_v'
															elif "bias" 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(__lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase			)
												continue
						if not is_used:
									unused_weights.append(__lowercase			)

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






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     List[Any]	, __lowercase       :     Dict	, __lowercase       :     Any	, __lowercase       :     int			)			->  Optional[int]:






			"""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:
									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.'''			)
									_snake_case          =      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.'''			)
									_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:
									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.'''			)
									_snake_case          =      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.'''			)
									_snake_case          =      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       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     Optional[Any]=None	, __lowercase       :     Optional[int]=None	, __lowercase       :     str=True			)			->  int:






			"""simple docstring"""




			if config_path is not None:
						_snake_case          =      UniSpeechSatConfig.from_pretrained(__lowercase			)
			else:
						_snake_case          =      UniSpeechSatConfig()

			_snake_case          =      ''

			if is_finetuned:
						_snake_case          =      UniSpeechSatForCTC(__lowercase			)
			else:
						_snake_case          =      UniSpeechSatForPreTraining(__lowercase			)

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

			recursively_load_weights(__lowercase	, __lowercase			)

			hf_wavavec.save_pretrained(__lowercase			)


if __name__ == "__main__":
			_lowerCamelCase	:    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	:    List[Any]          =      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 __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_snake_case          =      requests.get(
			    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''	, headers={'User-agent': 'A random string'}	, )
			if response.status_code == 429:
						raise requests.HTTPError

			_snake_case          =      response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_snake_case          =      {
						    item: data['data']['children'][id_]['data'][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
			# If you get Error 429, that means you are rate limited.Try after some time
			print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))