






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      :    Any   		=   logging.get_logger("""transformers.models.speecht5""")

__lowerCamelCase      :    List[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      :    List[str]   		=   {
    """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[Any]   		=   {
    """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      :    Any   		=   {
    """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      :    str   		=   {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__lowerCamelCase      :    str   		=   {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__lowerCamelCase      :    Optional[int]   		=   {
    """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      :    Optional[int]   		=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase      :    List[str]   		=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase      :    Tuple   		=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase      :    Dict   		=   []
__lowerCamelCase      :    Dict   		=   [
    """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      :    Dict   		=   IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__lowerCamelCase      :    Union[str, Any]   		=   IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							List[Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   Union[str, Any]     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Union[str, Any]     =						getattr(snake_case_  ,						snake_case_     ).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__				:   Dict     =						value
	elif weight_type == "weight_g":
		snake_case__				:   List[Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   int     =						value
	elif weight_type == "bias":
		snake_case__				:   List[Any]     =						value
	elif weight_type == "running_mean":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "running_var":
		snake_case__				:   Dict     =						value
	elif weight_type == "num_batches_tracked":
		snake_case__				:   Optional[int]     =						value
	else:
		snake_case__				:   Tuple     =						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_      :							Tuple  ,						snake_case_      :							Union[str, Any]     ):
	for key in ignore_keys:
		if key.endswith(".*"     ):
			if name.startswith(key[:-1]     ):
				return True
		elif ".*." in key:
			snake_case__				:   Optional[Any]     =						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_      :							int  ,						snake_case_      :							Dict  ,						snake_case_      :							Dict     ):
	snake_case__				:   Optional[Any]     =						[]

	if task == "s2t":
		snake_case__				:   Union[str, Any]     =						hf_model.speechta.encoder.prenet.feature_encoder
		snake_case__				:   int     =						MAPPING_S2T
		snake_case__				:   List[str]     =						IGNORE_KEYS_S2T
	elif task == "t2s":
		snake_case__				:   str     =						None
		snake_case__				:   Optional[Any]     =						MAPPING_T2S
		snake_case__				:   Tuple     =						IGNORE_KEYS_T2S
	elif task == "s2s":
		snake_case__				:   List[Any]     =						hf_model.speechta.encoder.prenet.feature_encoder
		snake_case__				:   Any     =						MAPPING_S2S
		snake_case__				:   Tuple     =						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

		snake_case__				:   Any     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   Dict     =						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__				:   Tuple     =						key.split(".*."     )
					if prefix in name and suffix in name:
						snake_case__				:   Union[str, Any]     =						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					snake_case__				:   Optional[Any]     =						True
					if "*" in mapped_key:
						snake_case__				:   Dict     =						name.split(snake_case_     )[0].split("."     )[-2]
						snake_case__				:   List[Any]     =						mapped_key.replace("*"  ,						snake_case_     )
					if "weight_g" in name:
						snake_case__				:   Any     =						"weight_g"
					elif "weight_v" in name:
						snake_case__				:   Optional[int]     =						"weight_v"
					elif "bias" in name:
						snake_case__				:   List[str]     =						"bias"
					elif "weight" in name:
						snake_case__				:   Tuple     =						"weight"
					elif "running_mean" in name:
						snake_case__				:   Any     =						"running_mean"
					elif "running_var" in name:
						snake_case__				:   Dict     =						"running_var"
					elif "num_batches_tracked" in name:
						snake_case__				:   Optional[int]     =						"num_batches_tracked"
					else:
						snake_case__				:   List[str]     =						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_      :							int  ,						snake_case_      :							str  ,						snake_case_      :							Tuple  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							int     ):
	snake_case__				:   Optional[int]     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Optional[Any]     =						name.split("."     )
	snake_case__				:   Optional[Any]     =						int(items[0]     )
	snake_case__				:   int     =						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__				:   Any     =						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__				:   Union[str, Any]     =						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__				:   str     =						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__				:   Dict     =						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_      :							int  ,						snake_case_      :							Tuple  ,						snake_case_      :							Tuple  ,						snake_case_      :							int=None  ,						snake_case_      :							str=None  ,						snake_case_      :							int=None  ,						):
	if config_path is not None:
		snake_case__				:   List[Any]     =						SpeechTaConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   List[Any]     =						SpeechTaConfig()

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

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

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

	snake_case__				:   Any     =						SpeechTaFeatureExtractor()
	snake_case__				:   Union[str, Any]     =						SpeechTaProcessor(tokenizer=snake_case_  ,						feature_extractor=snake_case_     )
	processor.save_pretrained(snake_case_     )

	snake_case__				:   str     =						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__":
	__lowerCamelCase      :    Tuple   		=   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      :    int   		=   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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



