



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

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			for attribute in key.split("."    ):
						A							:					Union[str, Any]              =						getattr(snake_case_ ,    snake_case_    )

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

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




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						[]

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

						A							:					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" ,    )
									A							:					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:
															A							:					Tuple              =						key.split(".*."    )
															if prefix in name and suffix in name:
																		A							:					Union[str, Any]              =						suffix

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

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

			if vocab_path:
						A							:					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
						A							:					List[str]              =						AddedToken("<mask>" ,    lstrip=snake_case_ ,    rstrip=snake_case_    )
						A							:					List[Any]              =						mask_token
						tokenizer.add_special_tokens({"mask_token": mask_token}    )
						tokenizer.add_tokens(["<ctc_blank>"]    )

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

			A							:					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__":
							__SCREAMING_SNAKE_CASE           =  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."""
							)

							__SCREAMING_SNAKE_CASE           =  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AltCLIPPreTrainedModel""",
							    """AltCLIPModel""",
							    """AltCLIPTextModel""",
							    """AltCLIPVisionModel""",
							]


if TYPE_CHECKING:
							from .configuration_altclip import (
							    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AltCLIPConfig,
							    AltCLIPTextConfig,
							    AltCLIPVisionConfig,
							)
							from .processing_altclip import AltCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_altclip import (
														    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AltCLIPModel,
														    AltCLIPPreTrainedModel,
														    AltCLIPTextModel,
														    AltCLIPVisionModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)