


"""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()
__SCREAMING_SNAKE_CASE							:      List[Any]						=      logging.get_logger('transformers.models.speecht5')

__SCREAMING_SNAKE_CASE							:      Optional[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',
}
__SCREAMING_SNAKE_CASE							:      List[str]						=      {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
__SCREAMING_SNAKE_CASE							:      int						=      {
    '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',
}
__SCREAMING_SNAKE_CASE							:      Any						=      {
    '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',
}
__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
__SCREAMING_SNAKE_CASE							:      Dict						=      {
    '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',
}
__SCREAMING_SNAKE_CASE							:      Optional[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',
}
__SCREAMING_SNAKE_CASE							:      Dict						=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__SCREAMING_SNAKE_CASE							:      int						=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__SCREAMING_SNAKE_CASE							:      int						=      []
__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      [
    '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',
]
__SCREAMING_SNAKE_CASE							:      Any						=      IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
__SCREAMING_SNAKE_CASE							:      Tuple						=      IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:


							for attribute in key.split("""."""			):
														snake_case_          =						getattr(__UpperCamelCase			,	__UpperCamelCase			)

							if weight_type is not None:
														snake_case_          =						getattr(__UpperCamelCase			,	__UpperCamelCase			).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
							elif weight_type == "running_mean":
														snake_case_          =						value
							elif weight_type == "running_var":
														snake_case_          =						value
							elif weight_type == "num_batches_tracked":
														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			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Tuple:


							for key in ignore_keys:
														if key.endswith(""".*"""			):
																					if name.startswith(key[:-1]			):
																												return True
														elif ".*." in key:
																					snake_case_    ,  snake_case_          =						key.split(""".*."""			)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						[]

							if task == "s2t":
														snake_case_          =						hf_model.speechta.encoder.prenet.feature_encoder
														snake_case_          =						MAPPING_S2T
														snake_case_          =						IGNORE_KEYS_S2T
							elif task == "t2s":
														snake_case_          =						None
														snake_case_          =						MAPPING_T2S
														snake_case_          =						IGNORE_KEYS_T2S
							elif task == "s2s":
														snake_case_          =						hf_model.speechta.encoder.prenet.feature_encoder
														snake_case_          =						MAPPING_S2S
														snake_case_          =						IGNORE_KEYS_S2S
							else:
														raise ValueError(f"""Unsupported task: {task}"""			)

							for name, value in fairseq_dict.items():
														if should_ignore(__UpperCamelCase			,	__UpperCamelCase			):
																					logger.info(f"""{name} was ignored"""			)
																					continue

														snake_case_          =						False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	hf_model.config.feat_extract_norm == """group"""			,	)
																					snake_case_          =						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:
																																			snake_case_    ,  snake_case_          =						key.split(""".*."""			)
																																			if prefix in name and suffix in name:
																																										snake_case_          =						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			snake_case_          =						True
																																			if "*" in mapped_key:
																																										snake_case_          =						name.split(__UpperCamelCase			)[0].split("""."""			)[-2]
																																										snake_case_          =						mapped_key.replace("""*"""			,	__UpperCamelCase			)
																																			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:
																																										snake_case_          =						"""weight"""
																																			elif "running_mean" in name:
																																										snake_case_          =						"""running_mean"""
																																			elif "running_var" in name:
																																										snake_case_          =						"""running_var"""
																																			elif "num_batches_tracked" in name:
																																										snake_case_          =						"""num_batches_tracked"""
																																			else:
																																										snake_case_          =						None
																																			set_recursively(__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			)
																												continue
														if not is_used:
																					unused_weights.append(__UpperCamelCase			)

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							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.conv_layers[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.conv_layers[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(__UpperCamelCase			)







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	)      ->			List[Any]:


							if config_path is not None:
														snake_case_          =						SpeechTaConfig.from_pretrained(__UpperCamelCase			)
							else:
														snake_case_          =						SpeechTaConfig()

							if task == "s2t":
														snake_case_          =						config.max_text_positions
														snake_case_          =						SpeechTaForSpeechToText(__UpperCamelCase			)
							elif task == "t2s":
														snake_case_          =						1_876
														snake_case_          =						600
														snake_case_          =						config.max_speech_positions
														snake_case_          =						SpeechTaForTextToSpeech(__UpperCamelCase			)
							elif task == "s2s":
														snake_case_          =						1_876
														snake_case_          =						config.max_speech_positions
														snake_case_          =						SpeechTaForSpeechToSpeech(__UpperCamelCase			)
							else:
														raise ValueError(f"""Unknown task name: {task}"""			)

							if vocab_path:
														snake_case_          =						SpeechTaTokenizer(__UpperCamelCase			,	model_max_length=config.max_text_positions			)

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

							snake_case_          =						SpeechTaFeatureExtractor()
							snake_case_          =						SpeechTaProcessor(tokenizer=__UpperCamelCase			,	feature_extractor=__UpperCamelCase			)
							processor.save_pretrained(__UpperCamelCase			)

							snake_case_          =						torch.load(__UpperCamelCase			)
							recursively_load_weights(fairseq_checkpoint["""model"""]			,	__UpperCamelCase			,	__UpperCamelCase			)

							model.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[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.'
				)

				__SCREAMING_SNAKE_CASE							:      List[str]						=      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"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list:

							if len(_SCREAMING_SNAKE_CASE			) <= 1:
														return [tuple(_SCREAMING_SNAKE_CASE			)]

							snake_case_          =						[]

							def generate(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														if k == 1:
																					res.append(tuple(arr[:]			)			)
																					return

														generate(k - 1			,	_SCREAMING_SNAKE_CASE			)

														for i in range(k - 1			):
																					if k % 2 == 0:  # k is even
																												snake_case_    ,  snake_case_          =						arr[k - 1], arr[i]
																					else:  # k is odd
																												snake_case_    ,  snake_case_          =						arr[k - 1], arr[0]
																					generate(k - 1			,	_SCREAMING_SNAKE_CASE			)

							generate(len(_SCREAMING_SNAKE_CASE			)			,	_SCREAMING_SNAKE_CASE			)
							return res


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      List[Any]						=      input('Enter numbers separated by a comma:\n').strip()
				__SCREAMING_SNAKE_CASE							:      str						=      [int(item) for item in user_input.split(',')]
				print(heaps(arr))



