
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			data






		def __iter__(    self			):
								for element in self.data:
														yield element




def        _SCREAMING_SNAKE_CASE      (		snake_case=True       )						->				List[str]:
						_UpperCAmelCase      =			Accelerator(even_batches=snake_case       )
						assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
						return accelerator




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = False       )						->				List[Any]:
						if iterable:
												_UpperCAmelCase      =			DummyIterableDataset(torch.as_tensor(range(snake_case       )       )       )
						else:
												_UpperCAmelCase      =			TensorDataset(torch.as_tensor(range(snake_case       )       )       )

						_UpperCAmelCase      =			DataLoader(snake_case			,     batch_size=snake_case       )
						_UpperCAmelCase      =			accelerator.prepare(snake_case       )

						return dl




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     )						->				Dict:
						_UpperCAmelCase      =			create_dataloader(accelerator=snake_case			,     dataset_size=snake_case			,     batch_size=snake_case       )

						_UpperCAmelCase      =			[len(batch[0]       ) for batch in dl]

						if accelerator.process_index == 0:
												assert batch_sizes == process_0_expected_batch_sizes
						elif accelerator.process_index == 1:
												assert batch_sizes == process_1_expected_batch_sizes




def        _SCREAMING_SNAKE_CASE      (		)						->				Dict:
						_UpperCAmelCase      =			create_accelerator()

						# without padding, we would expect a different number of batches
						verify_dataloader_batch_sizes(
						    snake_case			,     dataset_size=3			,     batch_size=1			,     process_0_expected_batch_sizes=[1, 1]			,     process_1_expected_batch_sizes=[1, 1]			,     )

						# without padding, we would expect the same number of batches, but different sizes
						verify_dataloader_batch_sizes(
						    snake_case			,     dataset_size=7			,     batch_size=2			,     process_0_expected_batch_sizes=[2, 2]			,     process_1_expected_batch_sizes=[2, 2]			,     )




def        _SCREAMING_SNAKE_CASE      (		)						->				List[Any]:
						_UpperCAmelCase      =			create_accelerator(even_batches=snake_case       )

						verify_dataloader_batch_sizes(
						    snake_case			,     dataset_size=3			,     batch_size=1			,     process_0_expected_batch_sizes=[1, 1]			,     process_1_expected_batch_sizes=[1]			,     )

						verify_dataloader_batch_sizes(
						    snake_case			,     dataset_size=7			,     batch_size=2			,     process_0_expected_batch_sizes=[2, 2]			,     process_1_expected_batch_sizes=[2, 1]			,     )




def        _SCREAMING_SNAKE_CASE      (		)						->				int:
						_UpperCAmelCase      =			create_accelerator(even_batches=snake_case       )

						_UpperCAmelCase      =			torch.nn.Linear(1			,     1       )
						_UpperCAmelCase      =			accelerator.prepare(snake_case       )

						_UpperCAmelCase      =			create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1       )

						_UpperCAmelCase      =			[]
						with accelerator.join_uneven_inputs([ddp_model]       ):
												for batch_idx, batch in enumerate(snake_case       ):
																		_UpperCAmelCase      =			ddp_model(batch[0].float()       )
																		_UpperCAmelCase      =			output.sum()
																		loss.backward()
																		batch_idxs.append(snake_case       )

						accelerator.wait_for_everyone()

						if accelerator.process_index == 0:
												assert batch_idxs == [0, 1]
						elif accelerator.process_index == 1:
												assert batch_idxs == [0]




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						with warnings.catch_warnings(record=snake_case       ) as w:
												with accelerator.join_uneven_inputs([Mock()]       ):
																		pass

												assert issubclass(w[-1].category			,     snake_case       )
												assert "only supported for multi-GPU" in str(w[-1].message       )




def        _SCREAMING_SNAKE_CASE      (		)						->				List[Any]:
						_UpperCAmelCase      =			True
						_UpperCAmelCase      =			False
						_UpperCAmelCase      =			create_accelerator(even_batches=snake_case       )
						_UpperCAmelCase      =			torch.nn.Linear(1			,     1       )
						_UpperCAmelCase      =			accelerator.prepare(snake_case       )
						_UpperCAmelCase      =			create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1       )
						_UpperCAmelCase      =			create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1       )

						with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=snake_case       ):
												_UpperCAmelCase      =			train_dl.batch_sampler.even_batches
												_UpperCAmelCase      =			valid_dl.batch_sampler.even_batches

						assert train_dl_overridden_value == overridden_even_batches
						assert valid_dl_overridden_value == overridden_even_batches
						assert train_dl.batch_sampler.even_batches == default_even_batches
						assert valid_dl.batch_sampler.even_batches == default_even_batches




def        _SCREAMING_SNAKE_CASE      (		)						->				List[str]:
						_UpperCAmelCase      =			True
						_UpperCAmelCase      =			False
						_UpperCAmelCase      =			create_accelerator(even_batches=snake_case       )
						_UpperCAmelCase      =			torch.nn.Linear(1			,     1       )
						_UpperCAmelCase      =			accelerator.prepare(snake_case       )
						create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1			,     iterable=snake_case       )
						_UpperCAmelCase      =			create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1       )

						with warnings.catch_warnings():
												warnings.filterwarnings("""ignore"""       )
												try:
																		with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=snake_case       ):
																								_UpperCAmelCase      =			batch_dl.batch_sampler.even_batches
												except AttributeError:
																		# ensure attribute error is not raised when processing iterable dl
																		raise AssertionError

						assert batch_dl_overridden_value == overridden_even_batches
						assert batch_dl.batch_sampler.even_batches == default_even_batches




def        _SCREAMING_SNAKE_CASE      (		)						->				Union[str, Any]:
						_UpperCAmelCase      =			create_accelerator()
						_UpperCAmelCase      =			torch.nn.Linear(1			,     1       )
						_UpperCAmelCase      =			accelerator.prepare(snake_case       )
						create_dataloader(snake_case			,     dataset_size=3			,     batch_size=1			,     iterable=snake_case       )

						with warnings.catch_warnings(record=snake_case       ) as w:
												with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=snake_case       ):
																		pass

												assert issubclass(w[-1].category			,     snake_case       )
												assert "only supported for map-style datasets" in str(w[-1].message       )




def        _SCREAMING_SNAKE_CASE      (		)						->				Optional[int]:
						_UpperCAmelCase      =			create_accelerator()

						accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""       )
						test_default_ensures_even_batch_sizes()

						accelerator.print("""Run tests with even_batches disabled"""       )
						test_can_disable_even_batches()

						accelerator.print("""Test joining uneven inputs"""       )
						test_can_join_uneven_inputs()

						accelerator.print("""Test overriding even_batches when joining uneven inputs"""       )
						test_join_can_override_even_batches()

						accelerator.print("""Test overriding even_batches for mixed dataloader types"""       )
						test_join_can_override_for_mixed_type_dataloaders()

						accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""       )
						test_join_raises_warning_for_iterable_when_overriding_even_batches()

						accelerator.print("""Test join with non DDP distributed raises warning"""       )
						_UpperCAmelCase      =			accelerator.state.distributed_type
						_UpperCAmelCase      =			DistributedType.FSDP
						test_join_raises_warning_for_non_ddp_distributed(snake_case       )
						_UpperCAmelCase      =			original_state


if __name__ == "__main__":
				main()





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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_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(snake_case			,     snake_case       ):
																		logger.info(f"{name} was ignored"       )
																		continue

												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     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(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														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(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

						logger.warning(f"Unused weights: {unused_weights}"       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_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(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=None			,     )						->				Union[str, Any]:
						if config_path is not None:
												_UpperCAmelCase      =			SpeechTaConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			SpeechTaConfig()

						if task == "s2t":
												_UpperCAmelCase      =			config.max_text_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToText(snake_case       )
						elif task == "t2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			6_0_0
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForTextToSpeech(snake_case       )
						elif task == "s2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToSpeech(snake_case       )
						else:
												raise ValueError(f"Unknown task name: {task}"       )

						if vocab_path:
												_UpperCAmelCase      =			SpeechTaTokenizer(snake_case			,     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=snake_case			,     rstrip=snake_case       )
												_UpperCAmelCase      =			mask_token
												tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
												tokenizer.add_tokens(["""<ctc_blank>"""]       )

						_UpperCAmelCase      =			SpeechTaFeatureExtractor()
						_UpperCAmelCase      =			SpeechTaProcessor(tokenizer=snake_case			,     feature_extractor=snake_case       )
						processor.save_pretrained(snake_case       )

						_UpperCAmelCase      =			torch.load(snake_case       )
						recursively_load_weights(fairseq_checkpoint["""model"""]			,     snake_case			,     snake_case       )

						model.save_pretrained(snake_case       )

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


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,
				)