






'''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()
__lowerCamelCase   :					List[Any]   							=					logging.get_logger('''transformers.models.speecht5''')

__lowerCamelCase   :					Any   							=					{
    '''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''',
}
__lowerCamelCase   :					Any   							=					{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__lowerCamelCase   :					List[str]   							=					{
    '''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''',
}
__lowerCamelCase   :					str   							=					{
    '''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''',
}
__lowerCamelCase   :					Dict   							=					{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowerCamelCase   :					Union[str, Any]   							=					{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowerCamelCase   :					Tuple   							=					{
    '''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''',
}
__lowerCamelCase   :					List[str]   							=					{
    '''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''',
}
__lowerCamelCase   :					List[Any]   							=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase   :					Union[str, Any]   							=					{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase   :					int   							=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase   :					Union[str, Any]   							=					[]
__lowerCamelCase   :					Tuple   							=					[
    '''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''',
]
__lowerCamelCase   :					Any   							=					IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowerCamelCase   :					Any   							=					IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowerCamelCase   :					Dict   							=					IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			for attribute in key.split("."						):
						snake_case_				:					Optional[int]   =       getattr(__magic_name__     ,__magic_name__						)

			if weight_type is not None:
						snake_case_				:					Union[str, Any]   =       getattr(__magic_name__     ,__magic_name__						).shape
			else:
						snake_case_				:					Tuple   =       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_				:					Any   =       value
			elif weight_type == "weight_g":
						snake_case_				:					List[Any]   =       value
			elif weight_type == "weight_v":
						snake_case_				:					Tuple   =       value
			elif weight_type == "bias":
						snake_case_				:					List[Any]   =       value
			elif weight_type == "running_mean":
						snake_case_				:					int   =       value
			elif weight_type == "running_var":
						snake_case_				:					int   =       value
			elif weight_type == "num_batches_tracked":
						snake_case_				:					Union[str, Any]   =       value
			else:
						snake_case_				:					Dict   =       value

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       []

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

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

						snake_case_				:					List[str]   =       False
						if "conv_layers" in name:
									load_conv_layer(
									    __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,hf_model.config.feat_extract_norm == "group"     ,)
									snake_case_				:					Optional[int]   =       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_				:					Dict   =       key.split(".*."						)
															if prefix in name and suffix in name:
																		snake_case_				:					List[Any]   =       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															snake_case_				:					Dict   =       True
															if "*" in mapped_key:
																		snake_case_				:					Tuple   =       name.split(__magic_name__						)[0].split("."						)[-2]
																		snake_case_				:					Union[str, Any]   =       mapped_key.replace("*"     ,__magic_name__						)
															if "weight_g" in name:
																		snake_case_				:					Union[str, Any]   =       "weight_g"
															elif "weight_v" in name:
																		snake_case_				:					Union[str, Any]   =       "weight_v"
															elif "bias" in name:
																		snake_case_				:					List[Any]   =       "bias"
															elif "weight" in name:
																		snake_case_				:					List[str]   =       "weight"
															elif "running_mean" in name:
																		snake_case_				:					List[Any]   =       "running_mean"
															elif "running_var" in name:
																		snake_case_				:					str   =       "running_var"
															elif "num_batches_tracked" in name:
																		snake_case_				:					Optional[int]   =       "num_batches_tracked"
															else:
																		snake_case_				:					Any   =       None
															set_recursively(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
												continue
						if not is_used:
									unused_weights.append(__magic_name__						)

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    Any:



			"""simple docstring"""



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

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=None     ,__magic_name__=None     ,__magic_name__=None     ,)->    Optional[Any]:



			"""simple docstring"""



			if config_path is not None:
						snake_case_				:					Optional[int]   =       SpeechTaConfig.from_pretrained(__magic_name__						)
			else:
						snake_case_				:					int   =       SpeechTaConfig()

			if task == "s2t":
						snake_case_				:					Optional[int]   =       config.max_text_positions
						snake_case_				:					List[Any]   =       SpeechTaForSpeechToText(__magic_name__						)
			elif task == "t2s":
						snake_case_				:					str   =       1876
						snake_case_				:					Any   =       600
						snake_case_				:					Any   =       config.max_speech_positions
						snake_case_				:					Optional[Any]   =       SpeechTaForTextToSpeech(__magic_name__						)
			elif task == "s2s":
						snake_case_				:					Tuple   =       1876
						snake_case_				:					str   =       config.max_speech_positions
						snake_case_				:					Dict   =       SpeechTaForSpeechToSpeech(__magic_name__						)
			else:
						raise ValueError(F'''Unknown task name: {task}'''						)

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

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

			snake_case_				:					int   =       SpeechTaFeatureExtractor()
			snake_case_				:					Dict   =       SpeechTaProcessor(tokenizer=__magic_name__     ,feature_extractor=__magic_name__						)
			processor.save_pretrained(__magic_name__						)

			snake_case_				:					Optional[Any]   =       torch.load(__magic_name__						)
			recursively_load_weights(fairseq_checkpoint["model"]     ,__magic_name__     ,__magic_name__						)

			model.save_pretrained(__magic_name__						)

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


if __name__ == "__main__":
		__lowerCamelCase   :					List[Any]   							=					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.'''
		)

		__lowerCamelCase   :					List[str]   							=					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 argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)




