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

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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			for attribute in key.split('''.'''						):
						lowerCamelCase_					=  getattr(lowerCAmelCase__	,lowerCAmelCase__						)

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

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






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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  []

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

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

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

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

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

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

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

			lowerCamelCase_					=  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__":
						A_    							=					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."""
						)

						A_    							=					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"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



