
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()
_UpperCAmelCase      :				Dict          = logging.get_logger("transformers.models.speecht5")

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


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		for attribute in key.split('.'				):
				UpperCamelCase						=  getattr(lowercase					, lowercase				)

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

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


def    A (      lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase					, lowercase				)     ->						List[str]:
		'''simple docstring'''

		UpperCamelCase						=  []

		if task == "s2t":
				UpperCamelCase						=  hf_model.speechta.encoder.prenet.feature_encoder
				UpperCamelCase						=  MAPPING_S2T
				UpperCamelCase						=  IGNORE_KEYS_S2T
		elif task == "t2s":
				UpperCamelCase						=  None
				UpperCamelCase						=  MAPPING_T2S
				UpperCamelCase						=  IGNORE_KEYS_T2S
		elif task == "s2s":
				UpperCamelCase						=  hf_model.speechta.encoder.prenet.feature_encoder
				UpperCamelCase						=  MAPPING_S2S
				UpperCamelCase						=  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						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  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			,						UpperCamelCase						=  key.split('.*.'				)
										if prefix in name and suffix in name:
												UpperCamelCase						=  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										UpperCamelCase						=  True
										if "*" in mapped_key:
												UpperCamelCase						=  name.split(lowercase				)[0].split('.'				)[-2]
												UpperCamelCase						=  mapped_key.replace('*'					, lowercase				)
										if "weight_g" in name:
												UpperCamelCase						=  'weight_g'
										elif "weight_v" in name:
												UpperCamelCase						=  'weight_v'
										elif "bias" in name:
												UpperCamelCase						=  'bias'
										elif "weight" in name:
												UpperCamelCase						=  'weight'
										elif "running_mean" in name:
												UpperCamelCase						=  'running_mean'
										elif "running_var" in name:
												UpperCamelCase						=  'running_var'
										elif "num_batches_tracked" in name:
												UpperCamelCase						=  'num_batches_tracked'
										else:
												UpperCamelCase						=  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					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  full_name.split('conv_layers.'				)[-1]
		UpperCamelCase						=  name.split('.'				)
		UpperCamelCase						=  int(items[0]				)
		UpperCamelCase						=  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						=  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						=  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						=  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						=  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					, lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=None					, )     ->						List[Any]:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  SpeechTaConfig.from_pretrained(lowercase				)
		else:
				UpperCamelCase						=  SpeechTaConfig()

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

		if vocab_path:
				UpperCamelCase						=  SpeechTaTokenizer(lowercase					, model_max_length=config.max_text_positions				)

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

		UpperCamelCase						=  SpeechTaFeatureExtractor()
		UpperCamelCase						=  SpeechTaProcessor(tokenizer=lowercase					, feature_extractor=lowercase				)
		processor.save_pretrained(lowercase				)

		UpperCamelCase						=  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__":
					_UpperCAmelCase      :				Optional[int]          = 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."
					)

					_UpperCAmelCase      :				Optional[Any]          = 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,
					)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowercase				)
		UpperCamelCase			,						UpperCamelCase						=  divmod(lowercase					, 2				)
		return binary_recursive(lowercase				) + str(lowercase				)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  str(lowercase				).strip()
		if not number:
				raise ValueError('No input value was provided'				)
		UpperCamelCase						=  '-' if number.startswith('-'				) else ''
		UpperCamelCase						=  number.lstrip('-'				)
		if not number.isnumeric():
				raise ValueError('Input value is not an integer'				)
		return f'''{negative}0b{binary_recursive(int(lowercase				)				)}'''


if __name__ == "__main__":
					from doctest import testmod

					testmod()
