


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

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

def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Any			) ->      Tuple:
		for attribute in key.split('''.'''			):
				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Any			) ->      Optional[Any]:
		for key in ignore_keys:
				if key.endswith('''.*'''			):
						if name.startswith(key[:-1]			):
								return True
				elif ".*." in key:
						__a      ,     __a           =			key.split('''.*.'''			)
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[str]			) ->      int:
		__a           =			[]

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

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

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

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

		if vocab_path:
				__a           =			SpeechTaTokenizer(lowerCAmelCase__    ,					model_max_length=config.max_text_positions			)

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

		__a           =			SpeechTaFeatureExtractor()
		__a           =			SpeechTaProcessor(tokenizer=lowerCAmelCase__    ,					feature_extractor=lowerCAmelCase__			)
		processor.save_pretrained(lowerCAmelCase__			)

		__a           =			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__":
				lowercase_			     =  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."
				)

				lowercase_			     =  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   int  						=	logging.get_logger(__name__)

lowerCamelCase							:   List[Any]  						=	{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							"""realm"""



      def __init__(self    :    str   ,						UpperCamelCase    :    List[Any]=30522   ,						UpperCamelCase    :    List[Any]=768   ,						UpperCamelCase    :    int=128   ,						UpperCamelCase    :    Any=12   ,						UpperCamelCase    :    Tuple=12   ,						UpperCamelCase    :    List[Any]=8   ,						UpperCamelCase    :    Union[str, Any]=3072   ,						UpperCamelCase    :    List[str]="gelu_new"   ,						UpperCamelCase    :    Any=0.1   ,						UpperCamelCase    :    List[str]=0.1   ,						UpperCamelCase    :    Dict=512   ,						UpperCamelCase    :    Dict=2   ,						UpperCamelCase    :    List[Any]=0.02   ,						UpperCamelCase    :    List[Any]=1E-12   ,						UpperCamelCase    :    Dict=256   ,						UpperCamelCase    :    Union[str, Any]=10   ,						UpperCamelCase    :    Optional[int]=1E-3   ,						UpperCamelCase    :    Tuple=5   ,						UpperCamelCase    :    Optional[int]=320   ,						UpperCamelCase    :    List[str]=13353718   ,						UpperCamelCase    :    Optional[Any]=5000   ,						UpperCamelCase    :    str=1   ,						UpperCamelCase    :    Union[str, Any]=0   ,						UpperCamelCase    :    List[Any]=2   ,						**UpperCamelCase    :    int   ,						):

             '''simple docstring'''







             super().__init__(pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						**UpperCamelCase )

             # Common config
             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= hidden_size
             lowercase__      			= retriever_proj_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= num_candidates
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_act
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= initializer_range
             lowercase__      			= type_vocab_size
             lowercase__      			= layer_norm_eps

             # Reader config
             lowercase__      			= span_hidden_size
             lowercase__      			= max_span_width
             lowercase__      			= reader_layer_norm_eps
             lowercase__      			= reader_beam_size
             lowercase__      			= reader_seq_len

             # Retrieval config
             lowercase__      			= num_block_records
             lowercase__      			= searcher_beam_size




