



'''simple docstring'''




import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a     :    Tuple 						=					logging.get_logger("""transformers.models.speecht5""")

a     :    Any 						=					{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
a     :    Union[str, Any] 						=					{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
a     :    str 						=					{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
a     :    Dict 						=					{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
a     :    Union[str, Any] 						=					{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
a     :    List[str] 						=					{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
a     :    List[str] 						=					{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
a     :    Union[str, Any] 						=					{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
a     :    Union[str, Any] 						=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a     :    Any 						=					{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a     :    Tuple 						=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a     :    Any 						=					[]
a     :    List[str] 						=					[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
a     :    Optional[Any] 						=					IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
a     :    Optional[int] 						=					IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
a     :    Optional[Any] 						=					IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							for attribute in key.split("""."""	):
														UpperCAmelCase  :	str							  =	getattr(_lowercase			,       _lowercase	)

							if weight_type is not None:
														UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       _lowercase	).shape
							else:
														UpperCAmelCase  :	Tuple							  =	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  :	Any							  =	value
							elif weight_type == "weight_g":
														UpperCAmelCase  :	List[str]							  =	value
							elif weight_type == "weight_v":
														UpperCAmelCase  :	Dict							  =	value
							elif weight_type == "bias":
														UpperCAmelCase  :	List[str]							  =	value
							elif weight_type == "running_mean":
														UpperCAmelCase  :	Any							  =	value
							elif weight_type == "running_var":
														UpperCAmelCase  :	Tuple							  =	value
							elif weight_type == "num_batches_tracked":
														UpperCAmelCase  :	Any							  =	value
							else:
														UpperCAmelCase  :	Union[str, Any]							  =	value

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							for key in ignore_keys:
														if key.endswith(""".*"""	):
																					if name.startswith(key[:-1]	):
																												return True
														elif ".*." in key:
																					UpperCAmelCase  :	Union[str, Any]							  =	key.split(""".*."""	)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	Optional[Any]							  =	[]

							if task == "s2t":
														UpperCAmelCase  :	List[str]							  =	hf_model.speechta.encoder.prenet.feature_encoder
														UpperCAmelCase  :	Any							  =	MAPPING_S2T
														UpperCAmelCase  :	List[Any]							  =	IGNORE_KEYS_S2T
							elif task == "t2s":
														UpperCAmelCase  :	Optional[int]							  =	None
														UpperCAmelCase  :	Optional[Any]							  =	MAPPING_T2S
														UpperCAmelCase  :	Any							  =	IGNORE_KEYS_T2S
							elif task == "s2s":
														UpperCAmelCase  :	List[Any]							  =	hf_model.speechta.encoder.prenet.feature_encoder
														UpperCAmelCase  :	List[Any]							  =	MAPPING_S2S
														UpperCAmelCase  :	Dict							  =	IGNORE_KEYS_S2S
							else:
														raise ValueError(F'''Unsupported task: {task}'''	)

							for name, value in fairseq_dict.items():
														if should_ignore(_lowercase			,       _lowercase	):
																					logger.info(F'''{name} was ignored'''	)
																					continue

														UpperCAmelCase  :	List[Any]							  =	False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       hf_model.config.feat_extract_norm == """group"""			,       )
																					UpperCAmelCase  :	List[str]							  =	True
														else:
																					for key, mapped_key in MAPPING.items():
																												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																												if "*" in key:
																																			UpperCAmelCase  :	Union[str, Any]							  =	key.split(""".*."""	)
																																			if prefix in name and suffix in name:
																																										UpperCAmelCase  :	List[str]							  =	suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			UpperCAmelCase  :	Dict							  =	True
																																			if "*" in mapped_key:
																																										UpperCAmelCase  :	Tuple							  =	name.split(_lowercase	)[0].split("""."""	)[-2]
																																										UpperCAmelCase  :	Optional[Any]							  =	mapped_key.replace("""*"""			,       _lowercase	)
																																			if "weight_g" in name:
																																										UpperCAmelCase  :	List[Any]							  =	"""weight_g"""
																																			elif "weight_v" in name:
																																										UpperCAmelCase  :	List[str]							  =	"""weight_v"""
																																			elif "bias" in name:
																																										UpperCAmelCase  :	List[str]							  =	"""bias"""
																																			elif "weight" in name:
																																										UpperCAmelCase  :	str							  =	"""weight"""
																																			elif "running_mean" in name:
																																										UpperCAmelCase  :	Dict							  =	"""running_mean"""
																																			elif "running_var" in name:
																																										UpperCAmelCase  :	str							  =	"""running_var"""
																																			elif "num_batches_tracked" in name:
																																										UpperCAmelCase  :	str							  =	"""num_batches_tracked"""
																																			else:
																																										UpperCAmelCase  :	Optional[Any]							  =	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			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Tuple							  =	full_name.split("""conv_layers."""	)[-1]
							UpperCAmelCase  :	Any							  =	name.split("""."""	)
							UpperCAmelCase  :	List[Any]							  =	int(items[0]	)
							UpperCAmelCase  :	int							  =	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  :	Tuple							  =	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  :	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:
																					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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''	)
																					UpperCAmelCase  :	int							  =	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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''	)
																					UpperCAmelCase  :	Tuple							  =	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			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       )   ->    Union[str, Any]:
							if config_path is not None:
														UpperCAmelCase  :	Any							  =	SpeechTaConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	str							  =	SpeechTaConfig()

							if task == "s2t":
														UpperCAmelCase  :	Optional[Any]							  =	config.max_text_positions
														UpperCAmelCase  :	int							  =	SpeechTaForSpeechToText(_lowercase	)
							elif task == "t2s":
														UpperCAmelCase  :	Dict							  =	1_8_7_6
														UpperCAmelCase  :	Any							  =	6_0_0
														UpperCAmelCase  :	Optional[Any]							  =	config.max_speech_positions
														UpperCAmelCase  :	List[str]							  =	SpeechTaForTextToSpeech(_lowercase	)
							elif task == "s2s":
														UpperCAmelCase  :	List[Any]							  =	1_8_7_6
														UpperCAmelCase  :	Optional[Any]							  =	config.max_speech_positions
														UpperCAmelCase  :	List[Any]							  =	SpeechTaForSpeechToSpeech(_lowercase	)
							else:
														raise ValueError(F'''Unknown task name: {task}'''	)

							if vocab_path:
														UpperCAmelCase  :	Optional[int]							  =	SpeechTaTokenizer(_lowercase			,       model_max_length=config.max_text_positions	)

														# Mask token behaves like a normal word, i.e. include the space before it
														UpperCAmelCase  :	Optional[Any]							  =	AddedToken("""<mask>"""			,       lstrip=_lowercase			,       rstrip=_lowercase	)
														UpperCAmelCase  :	Optional[int]							  =	mask_token
														tokenizer.add_special_tokens({"""mask_token""": mask_token}	)
														tokenizer.add_tokens(["""<ctc_blank>"""]	)

							UpperCAmelCase  :	List[Any]							  =	SpeechTaFeatureExtractor()
							UpperCAmelCase  :	List[Any]							  =	SpeechTaProcessor(tokenizer=_lowercase			,       feature_extractor=_lowercase	)
							processor.save_pretrained(_lowercase	)

							UpperCAmelCase  :	str							  =	torch.load(_lowercase	)
							recursively_load_weights(fairseq_checkpoint["""model"""]			,       _lowercase			,       _lowercase	)

							model.save_pretrained(_lowercase	)

							if repo_id:
														print("""Pushing to the hub..."""	)
														processor.push_to_hub(_lowercase	)
														model.push_to_hub(_lowercase	)


if __name__ == "__main__":
							a     :    List[Any] 						=					argparse.ArgumentParser()
							parser.add_argument(
							    """--task""",
							    default="""s2t""",
							    type=str,
							    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
							)
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							a     :    Tuple 						=					parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)






'''simple docstring'''




from datetime import datetime as dt
import os

from github import Github


a     :    int      						=					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	str							  =	Github(os.environ["""GITHUB_TOKEN"""]	)
							UpperCAmelCase  :	Dict							  =	g.get_repo("""huggingface/transformers"""	)
							UpperCAmelCase  :	int							  =	repo.get_issues(state="""open"""	)

							for issue in open_issues:
														UpperCAmelCase  :	Optional[int]							  =	sorted([comment for comment in issue.get_comments()]			,       key=lambda _lowercase	: i.created_at			,       reverse=_lowercase	)
														UpperCAmelCase  :	Any							  =	comments[0] if len(_lowercase	) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""	)
														elif (
														    (dt.utcnow() - issue.updated_at).days > 2_3
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would add stale comment to {issue.number}")
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""	)


if __name__ == "__main__":
							main()

