

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase						:  Tuple									=			logging.get_logger("""transformers.models.speecht5""")

_UpperCAmelCase						:  Tuple									=			{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_UpperCAmelCase						:  Tuple									=			{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCAmelCase						:  Optional[int]									=			{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_UpperCAmelCase						:  Optional[int]									=			{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_UpperCAmelCase						:  Any									=			{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCAmelCase						:  Dict									=			{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCAmelCase						:  List[str]									=			{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_UpperCAmelCase						:  Dict									=			{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_UpperCAmelCase						:  str									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase						:  int									=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase						:  Dict									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase						:  List[Any]									=			[]
_UpperCAmelCase						:  str									=			[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_UpperCAmelCase						:  int									=			IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_UpperCAmelCase						:  Dict									=			IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_UpperCAmelCase						:  int									=			IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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

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

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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       []

							if task == "s2t":
														snake_case_     							=       hf_model.speechta.encoder.prenet.feature_encoder
														snake_case_     							=       MAPPING_S2T
														snake_case_     							=       IGNORE_KEYS_S2T
							elif task == "t2s":
														snake_case_     							=       None
														snake_case_     							=       MAPPING_T2S
														snake_case_     							=       IGNORE_KEYS_T2S
							elif task == "s2s":
														snake_case_     							=       hf_model.speechta.encoder.prenet.feature_encoder
														snake_case_     							=       MAPPING_S2S
														snake_case_     							=       IGNORE_KEYS_S2S
							else:
														raise ValueError(F'''Unsupported task: {task}''' )

							for name, value in fairseq_dict.items():
														if should_ignore(UpperCamelCase__  ,				UpperCamelCase__ ):
																					logger.info(F'''{name} was ignored''' )
																					continue

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			snake_case_     							=       True
																																			if "*" in mapped_key:
																																										snake_case_     							=       name.split(UpperCamelCase__ )[0].split('.' )[-2]
																																										snake_case_     							=       mapped_key.replace('*'  ,				UpperCamelCase__ )
																																			if "weight_g" in name:
																																										snake_case_     							=       'weight_g'
																																			elif "weight_v" in name:
																																										snake_case_     							=       'weight_v'
																																			elif "bias" in name:
																																										snake_case_     							=       'bias'
																																			elif "weight" in name:
																																										snake_case_     							=       'weight'
																																			elif "running_mean" in name:
																																										snake_case_     							=       'running_mean'
																																			elif "running_var" in name:
																																										snake_case_     							=       'running_var'
																																			elif "num_batches_tracked" in name:
																																										snake_case_     							=       'num_batches_tracked'
																																			else:
																																										snake_case_     							=       None
																																			set_recursively(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase__ )

							logger.warning(F'''Unused weights: {unused_weights}''' )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       full_name.split('conv_layers.' )[-1]
							snake_case_     							=       name.split('.' )
							snake_case_     							=       int(items[0] )
							snake_case_     							=       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.''' )
																					snake_case_     							=       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.''' )
																					snake_case_     							=       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.''' )
																					snake_case_     							=       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.''' )
																					snake_case_     							=       value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
							else:
														unused_weights.append(UpperCamelCase__ )





@torch.no_grad()
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				):







							'''simple docstring'''






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

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

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

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

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

							snake_case_     							=       torch.load(UpperCamelCase__ )
							recursively_load_weights(fairseq_checkpoint['model']  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							model.save_pretrained(UpperCamelCase__ )

							if repo_id:
														print('Pushing to the hub...' )
														processor.push_to_hub(UpperCamelCase__ )
														model.push_to_hub(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  str									=			argparse.ArgumentParser()
					parser.add_argument(
					    """--task""",
					    default="""s2t""",
					    type=str,
					    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
					)
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)

					_UpperCAmelCase						:  Optional[Any]									=			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,
					)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




