




# Copyright 2023 The HuggingFace Inc. 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a													=			{
    "Acehnese Arabic": "ace_Arab",
    "Acehnese Latin": "ace_Latn",
    "Mesopotamian Arabic": "acm_Arab",
    "Ta'izzi-Adeni Arabic": "acq_Arab",
    "Tunisian Arabic": "aeb_Arab",
    "Afrikaans": "afr_Latn",
    "South Levantine Arabic": "ajp_Arab",
    "Akan": "aka_Latn",
    "Amharic": "amh_Ethi",
    "North Levantine Arabic": "apc_Arab",
    "Modern Standard Arabic": "arb_Arab",
    "Modern Standard Arabic Romanized": "arb_Latn",
    "Najdi Arabic": "ars_Arab",
    "Moroccan Arabic": "ary_Arab",
    "Egyptian Arabic": "arz_Arab",
    "Assamese": "asm_Beng",
    "Asturian": "ast_Latn",
    "Awadhi": "awa_Deva",
    "Central Aymara": "ayr_Latn",
    "South Azerbaijani": "azb_Arab",
    "North Azerbaijani": "azj_Latn",
    "Bashkir": "bak_Cyrl",
    "Bambara": "bam_Latn",
    "Balinese": "ban_Latn",
    "Belarusian": "bel_Cyrl",
    "Bemba": "bem_Latn",
    "Bengali": "ben_Beng",
    "Bhojpuri": "bho_Deva",
    "Banjar Arabic": "bjn_Arab",
    "Banjar Latin": "bjn_Latn",
    "Standard Tibetan": "bod_Tibt",
    "Bosnian": "bos_Latn",
    "Buginese": "bug_Latn",
    "Bulgarian": "bul_Cyrl",
    "Catalan": "cat_Latn",
    "Cebuano": "ceb_Latn",
    "Czech": "ces_Latn",
    "Chokwe": "cjk_Latn",
    "Central Kurdish": "ckb_Arab",
    "Crimean Tatar": "crh_Latn",
    "Welsh": "cym_Latn",
    "Danish": "dan_Latn",
    "German": "deu_Latn",
    "Southwestern Dinka": "dik_Latn",
    "Dyula": "dyu_Latn",
    "Dzongkha": "dzo_Tibt",
    "Greek": "ell_Grek",
    "English": "eng_Latn",
    "Esperanto": "epo_Latn",
    "Estonian": "est_Latn",
    "Basque": "eus_Latn",
    "Ewe": "ewe_Latn",
    "Faroese": "fao_Latn",
    "Fijian": "fij_Latn",
    "Finnish": "fin_Latn",
    "Fon": "fon_Latn",
    "French": "fra_Latn",
    "Friulian": "fur_Latn",
    "Nigerian Fulfulde": "fuv_Latn",
    "Scottish Gaelic": "gla_Latn",
    "Irish": "gle_Latn",
    "Galician": "glg_Latn",
    "Guarani": "grn_Latn",
    "Gujarati": "guj_Gujr",
    "Haitian Creole": "hat_Latn",
    "Hausa": "hau_Latn",
    "Hebrew": "heb_Hebr",
    "Hindi": "hin_Deva",
    "Chhattisgarhi": "hne_Deva",
    "Croatian": "hrv_Latn",
    "Hungarian": "hun_Latn",
    "Armenian": "hye_Armn",
    "Igbo": "ibo_Latn",
    "Ilocano": "ilo_Latn",
    "Indonesian": "ind_Latn",
    "Icelandic": "isl_Latn",
    "Italian": "ita_Latn",
    "Javanese": "jav_Latn",
    "Japanese": "jpn_Jpan",
    "Kabyle": "kab_Latn",
    "Jingpho": "kac_Latn",
    "Kamba": "kam_Latn",
    "Kannada": "kan_Knda",
    "Kashmiri Arabic": "kas_Arab",
    "Kashmiri Devanagari": "kas_Deva",
    "Georgian": "kat_Geor",
    "Central Kanuri Arabic": "knc_Arab",
    "Central Kanuri Latin": "knc_Latn",
    "Kazakh": "kaz_Cyrl",
    "Kabiyè": "kbp_Latn",
    "Kabuverdianu": "kea_Latn",
    "Khmer": "khm_Khmr",
    "Kikuyu": "kik_Latn",
    "Kinyarwanda": "kin_Latn",
    "Kyrgyz": "kir_Cyrl",
    "Kimbundu": "kmb_Latn",
    "Northern Kurdish": "kmr_Latn",
    "Kikongo": "kon_Latn",
    "Korean": "kor_Hang",
    "Lao": "lao_Laoo",
    "Ligurian": "lij_Latn",
    "Limburgish": "lim_Latn",
    "Lingala": "lin_Latn",
    "Lithuanian": "lit_Latn",
    "Lombard": "lmo_Latn",
    "Latgalian": "ltg_Latn",
    "Luxembourgish": "ltz_Latn",
    "Luba-Kasai": "lua_Latn",
    "Ganda": "lug_Latn",
    "Luo": "luo_Latn",
    "Mizo": "lus_Latn",
    "Standard Latvian": "lvs_Latn",
    "Magahi": "mag_Deva",
    "Maithili": "mai_Deva",
    "Malayalam": "mal_Mlym",
    "Marathi": "mar_Deva",
    "Minangkabau Arabic ": "min_Arab",
    "Minangkabau Latin": "min_Latn",
    "Macedonian": "mkd_Cyrl",
    "Plateau Malagasy": "plt_Latn",
    "Maltese": "mlt_Latn",
    "Meitei Bengali": "mni_Beng",
    "Halh Mongolian": "khk_Cyrl",
    "Mossi": "mos_Latn",
    "Maori": "mri_Latn",
    "Burmese": "mya_Mymr",
    "Dutch": "nld_Latn",
    "Norwegian Nynorsk": "nno_Latn",
    "Norwegian Bokmål": "nob_Latn",
    "Nepali": "npi_Deva",
    "Northern Sotho": "nso_Latn",
    "Nuer": "nus_Latn",
    "Nyanja": "nya_Latn",
    "Occitan": "oci_Latn",
    "West Central Oromo": "gaz_Latn",
    "Odia": "ory_Orya",
    "Pangasinan": "pag_Latn",
    "Eastern Panjabi": "pan_Guru",
    "Papiamento": "pap_Latn",
    "Western Persian": "pes_Arab",
    "Polish": "pol_Latn",
    "Portuguese": "por_Latn",
    "Dari": "prs_Arab",
    "Southern Pashto": "pbt_Arab",
    "Ayacucho Quechua": "quy_Latn",
    "Romanian": "ron_Latn",
    "Rundi": "run_Latn",
    "Russian": "rus_Cyrl",
    "Sango": "sag_Latn",
    "Sanskrit": "san_Deva",
    "Santali": "sat_Olck",
    "Sicilian": "scn_Latn",
    "Shan": "shn_Mymr",
    "Sinhala": "sin_Sinh",
    "Slovak": "slk_Latn",
    "Slovenian": "slv_Latn",
    "Samoan": "smo_Latn",
    "Shona": "sna_Latn",
    "Sindhi": "snd_Arab",
    "Somali": "som_Latn",
    "Southern Sotho": "sot_Latn",
    "Spanish": "spa_Latn",
    "Tosk Albanian": "als_Latn",
    "Sardinian": "srd_Latn",
    "Serbian": "srp_Cyrl",
    "Swati": "ssw_Latn",
    "Sundanese": "sun_Latn",
    "Swedish": "swe_Latn",
    "Swahili": "swh_Latn",
    "Silesian": "szl_Latn",
    "Tamil": "tam_Taml",
    "Tatar": "tat_Cyrl",
    "Telugu": "tel_Telu",
    "Tajik": "tgk_Cyrl",
    "Tagalog": "tgl_Latn",
    "Thai": "tha_Thai",
    "Tigrinya": "tir_Ethi",
    "Tamasheq Latin": "taq_Latn",
    "Tamasheq Tifinagh": "taq_Tfng",
    "Tok Pisin": "tpi_Latn",
    "Tswana": "tsn_Latn",
    "Tsonga": "tso_Latn",
    "Turkmen": "tuk_Latn",
    "Tumbuka": "tum_Latn",
    "Turkish": "tur_Latn",
    "Twi": "twi_Latn",
    "Central Atlas Tamazight": "tzm_Tfng",
    "Uyghur": "uig_Arab",
    "Ukrainian": "ukr_Cyrl",
    "Umbundu": "umb_Latn",
    "Urdu": "urd_Arab",
    "Northern Uzbek": "uzn_Latn",
    "Venetian": "vec_Latn",
    "Vietnamese": "vie_Latn",
    "Waray": "war_Latn",
    "Wolof": "wol_Latn",
    "Xhosa": "xho_Latn",
    "Eastern Yiddish": "ydd_Hebr",
    "Yoruba": "yor_Latn",
    "Yue Chinese": "yue_Hant",
    "Chinese Simplified": "zho_Hans",
    "Chinese Traditional": "zho_Hant",
    "Standard Malay": "zsm_Latn",
    "Zulu": "zul_Latn",
}




class       _A				(		__lowercase     ):
		__a					    =       """facebook/nllb-200-distilled-600M"""
		__a					    =       (
		    """This is a tool that translates text from a language to another. It takes three inputs: `text`, which should """
		    """be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, """
		    """which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in """
		    """plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."""
		)
		__a					    =       """translator"""
		__a					    =       AutoTokenizer
		__a					    =       AutoModelForSeqaSeqLM
		__a					    =       LANGUAGE_CODES

		__a					    =       ["""text""", """text""", """text"""]
		__a					    =       ["""text"""]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								if src_lang not in self.lang_to_code:
														raise ValueError(F"{src_lang} is not a supported language."			)
								if tgt_lang not in self.lang_to_code:
														raise ValueError(F"{tgt_lang} is not a supported language."			)
								_UpperCAmelCase      =			self.lang_to_code[src_lang]
								_UpperCAmelCase      =			self.lang_to_code[tgt_lang]
								return self.pre_processor._build_translation_inputs(
								    _SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""						,		src_lang=_SCREAMING_SNAKE_CASE						,		tgt_lang=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return self.model.generate(**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return self.post_processor.decode(outputs[0].tolist()						,		skip_special_tokens=_SCREAMING_SNAKE_CASE			)





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,
				)