



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

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			for attribute in key.split("."					):
						lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

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

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



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[]

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

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

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

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

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			full_name.split("conv_layers."					)[-1]
			lowercase__   					=			name.split("."					)
			lowercase__   					=			int(items[0]					)
			lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			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.'''					)
									lowercase__   					=			value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(SCREAMING_SNAKE_CASE_					)



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					):
			if config_path is not None:
						lowercase__   					=			SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
			else:
						lowercase__   					=			SpeechTaConfig()

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

			if vocab_path:
						lowercase__   					=			SpeechTaTokenizer(SCREAMING_SNAKE_CASE_  ,					model_max_length=config.max_text_positions					)

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

			lowercase__   					=			SpeechTaFeatureExtractor()
			lowercase__   					=			SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,					feature_extractor=SCREAMING_SNAKE_CASE_					)
			processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			torch.load(SCREAMING_SNAKE_CASE_					)
			recursively_load_weights(fairseq_checkpoint["model"]  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			model.save_pretrained(SCREAMING_SNAKE_CASE_					)

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


if __name__ == "__main__":
							lowercase_						=       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."""
							)

							lowercase_						=       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,
							)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							lowercase_						=       parser.parse_args()

							if args.model_type == "roberta":
														lowercase_						=       RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase_						=       """roberta"""
							elif args.model_type == "gpt2":
														lowercase_						=       GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase_						=       """transformer"""

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase_						=       state_dict[F'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase_						=       F'{prefix}.embeddings.{w}.weight'
																					lowercase_						=       state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase_						=       F'{prefix}.embeddings.LayerNorm.{w}'
																					lowercase_						=       state_dict[param_name]

    # Transformer Blocks #
							lowercase_						=       0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       state_dict["""lm_head.weight"""]

							print(F'N layers selected for distillation: {std_idx}')
							print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)




