


"""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()
_lowerCamelCase     =  logging.get_logger('transformers.models.speecht5')

_lowerCamelCase     =  {
    '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',
}
_lowerCamelCase     =  {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_lowerCamelCase     =  {
    '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',
}
_lowerCamelCase     =  {
    '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',
}
_lowerCamelCase     =  {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_lowerCamelCase     =  {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_lowerCamelCase     =  {
    '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',
}
_lowerCamelCase     =  {
    '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',
}
_lowerCamelCase     =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCamelCase     =  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCamelCase     =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCamelCase     =  []
_lowerCamelCase     =  [
    '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',
]
_lowerCamelCase     =  IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_lowerCamelCase     =  IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_lowerCamelCase     =  IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]


def __lowercase     (      lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Tuple  ):
				for attribute in key.split("."  ):
								SCREAMING_SNAKE_CASE__     							=				getattr(lowerCamelCase_						,					lowerCamelCase_  )

				if weight_type is not None:
								SCREAMING_SNAKE_CASE__     							=				getattr(lowerCamelCase_						,					lowerCamelCase_  ).shape
				else:
								SCREAMING_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":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "weight_g":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "weight_v":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "bias":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "running_mean":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "running_var":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "num_batches_tracked":
								SCREAMING_SNAKE_CASE__     							=				value
				else:
								SCREAMING_SNAKE_CASE__     							=				value

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


def __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Optional[Any]  ):
				for key in ignore_keys:
								if key.endswith(".*"  ):
												if name.startswith(key[:-1]  ):
																return True
								elif ".*." in key:
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				key.split(".*."  )
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				[]

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

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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				SCREAMING_SNAKE_CASE__     							=				True
																				if "*" in mapped_key:
																								SCREAMING_SNAKE_CASE__     							=				name.split(lowerCamelCase_  )[0].split("."  )[-2]
																								SCREAMING_SNAKE_CASE__     							=				mapped_key.replace("*"						,					lowerCamelCase_  )
																				if "weight_g" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight_g"
																				elif "weight_v" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight_v"
																				elif "bias" in name:
																								SCREAMING_SNAKE_CASE__     							=				"bias"
																				elif "weight" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight"
																				elif "running_mean" in name:
																								SCREAMING_SNAKE_CASE__     							=				"running_mean"
																				elif "running_var" in name:
																								SCREAMING_SNAKE_CASE__     							=				"running_var"
																				elif "num_batches_tracked" in name:
																								SCREAMING_SNAKE_CASE__     							=				"num_batches_tracked"
																				else:
																								SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				full_name.split("conv_layers."  )[-1]
				SCREAMING_SNAKE_CASE__     							=				name.split("."  )
				SCREAMING_SNAKE_CASE__     							=				int(items[0]  )
				SCREAMING_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.'''  )
												SCREAMING_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.'''  )
												SCREAMING_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.'''  )
												SCREAMING_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.'''  )
												SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      str=None						,					lowerCamelCase_  :      Optional[Any]=None						,					lowerCamelCase_  :      Optional[Any]=None						,					):
				if config_path is not None:
								SCREAMING_SNAKE_CASE__     							=				SpeechTaConfig.from_pretrained(lowerCamelCase_  )
				else:
								SCREAMING_SNAKE_CASE__     							=				SpeechTaConfig()

				if task == "s2t":
								SCREAMING_SNAKE_CASE__     							=				config.max_text_positions
								SCREAMING_SNAKE_CASE__     							=				SpeechTaForSpeechToText(lowerCamelCase_  )
				elif task == "t2s":
								SCREAMING_SNAKE_CASE__     							=				1876
								SCREAMING_SNAKE_CASE__     							=				600
								SCREAMING_SNAKE_CASE__     							=				config.max_speech_positions
								SCREAMING_SNAKE_CASE__     							=				SpeechTaForTextToSpeech(lowerCamelCase_  )
				elif task == "s2s":
								SCREAMING_SNAKE_CASE__     							=				1876
								SCREAMING_SNAKE_CASE__     							=				config.max_speech_positions
								SCREAMING_SNAKE_CASE__     							=				SpeechTaForSpeechToSpeech(lowerCamelCase_  )
				else:
								raise ValueError(F'''Unknown task name: {task}'''  )

				if vocab_path:
								SCREAMING_SNAKE_CASE__     							=				SpeechTaTokenizer(lowerCamelCase_						,					model_max_length=config.max_text_positions  )

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

				SCREAMING_SNAKE_CASE__     							=				SpeechTaFeatureExtractor()
				SCREAMING_SNAKE_CASE__     							=				SpeechTaProcessor(tokenizer=lowerCamelCase_						,					feature_extractor=lowerCamelCase_  )
				processor.save_pretrained(lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				torch.load(lowerCamelCase_  )
				recursively_load_weights(fairseq_checkpoint["model"]						,					lowerCamelCase_						,					lowerCamelCase_  )

				model.save_pretrained(lowerCamelCase_  )

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


if __name__ == "__main__":
	_lowerCamelCase     =  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.'
	)

	_lowerCamelCase     =  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 math import pi


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
	print(arc_length(90, 10))
