




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__				     = logging.get_logger("transformers.models.speecht5")

UpperCAmelCase__				     = {
    "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__				     = {
    "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__				     = {
    "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__				     = {
    "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__				     = {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
UpperCAmelCase__				     = {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
UpperCAmelCase__				     = {
    "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__				     = {
    "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__				     = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase__				     = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__				     = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__				     = []
UpperCAmelCase__				     = [
    "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__				     = IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
UpperCAmelCase__				     = IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
UpperCAmelCase__				     = IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Optional[int]				)    -> int:
							'''simple docstring'''


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

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

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







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Any				)    -> int:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    []

							if task == "s2t":
														__lowercase      =    hf_model.speechta.encoder.prenet.feature_encoder
														__lowercase      =    MAPPING_S2T
														__lowercase      =    IGNORE_KEYS_S2T
							elif task == "t2s":
														__lowercase      =    None
														__lowercase      =    MAPPING_T2S
														__lowercase      =    IGNORE_KEYS_T2S
							elif task == "s2s":
														__lowercase      =    hf_model.speechta.encoder.prenet.feature_encoder
														__lowercase      =    MAPPING_S2S
														__lowercase      =    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

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

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


							__lowercase      =    full_name.split('''conv_layers.'''				)[-1]
							__lowercase      =    name.split('''.'''				)
							__lowercase      =    int(items[0]				)
							__lowercase      =    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.'				)
																					__lowercase      =    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.'				)
																					__lowercase      =    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.'				)
																					__lowercase      =    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.'				)
																					__lowercase      =    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(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			List[Any]=None				,       UpperCamelCase__							:			Optional[int]=None				,       UpperCamelCase__							:			List[str]=None				,       )    -> Union[str, Any]:
							'''simple docstring'''


							if config_path is not None:
														__lowercase      =    SpeechTaConfig.from_pretrained(UpperCamelCase__				)
							else:
														__lowercase      =    SpeechTaConfig()

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

							if vocab_path:
														__lowercase      =    SpeechTaTokenizer(UpperCamelCase__				,       model_max_length=config.max_text_positions				)

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

							__lowercase      =    SpeechTaFeatureExtractor()
							__lowercase      =    SpeechTaProcessor(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							__lowercase      =    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__":
			UpperCAmelCase__				     = 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__				     = 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,
			)












import math







def 		_A(       )    -> None:
							'''simple docstring'''


							__lowercase      =    input('''Enter message: '''				)
							__lowercase      =    int(input(F'Enter key [2-{len(UpperCamelCase__				) - 1}]: '				)				)
							__lowercase      =    input('''Encryption/Decryption [e/d]: '''				)

							if mode.lower().startswith('''e'''				):
														__lowercase      =    encrypt_message(UpperCamelCase__				,       UpperCamelCase__				)
							elif mode.lower().startswith('''d'''				):
														__lowercase      =    decrypt_message(UpperCamelCase__				,       UpperCamelCase__				)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}'				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    [''''''] * key
							for col in range(UpperCamelCase__				):
														__lowercase      =    col
														while pointer < len(UpperCamelCase__				):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    math.ceil(len(UpperCamelCase__				) / key				)
							__lowercase      =    key
							__lowercase      =    (num_cols * num_rows) - len(UpperCamelCase__				)
							__lowercase      =    [''''''] * num_cols
							__lowercase      =    0
							__lowercase      =    0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__lowercase      =    0
																					row += 1

							return "".join(UpperCamelCase__				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()






