





'''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()
snake_case_     =	logging.get_logger("""transformers.models.speecht5""")

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''',
}
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''',
}
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''',
}
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''',
}
snake_case_     =	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
snake_case_     =	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
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''',
}
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''',
}
snake_case_     =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
snake_case_     =	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case_     =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case_     =	[]
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''',
]
snake_case_     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
snake_case_     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
snake_case_     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]		)		->  str:

    '''simple docstring'''





    for attribute in key.split('''.'''		):
        A       :       Dict							=						getattr(lowerCAmelCase__    ,       lowerCAmelCase__		)

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

    logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]		)		->  Any:

    '''simple docstring'''





    for key in ignore_keys:
        if key.endswith('''.*'''		):
            if name.startswith(key[:-1]		):
                return True
        elif ".*." in key:
            A,	A       :       Any							=						key.split('''.*.'''		)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]		)		->  int:

    '''simple docstring'''





    A       :       Tuple							=						[]

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

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

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

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

    logger.warning(F'''Unused weights: {unused_weights}'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Union[str, Any]		)		->  Optional[Any]:

    '''simple docstring'''





    A       :       Optional[int]							=						full_name.split('''conv_layers.'''		)[-1]
    A       :       List[str]							=						name.split('''.'''		)
    A       :       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       :       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.'''		)
            A       :       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       :       Optional[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.'''		)
            A       :       Any							=						value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    else:
        unused_weights.append(lowerCAmelCase__		)
@torch.no_grad()
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : str=None    ,       UpperCamelCase__    : List[str]=None    ,       UpperCamelCase__    : Union[str, Any]=None    ,       )		->  List[Any]:

    '''simple docstring'''





    if config_path is not None:
        A       :       Tuple							=						SpeechTaConfig.from_pretrained(lowerCAmelCase__		)
    else:
        A       :       Union[str, Any]							=						SpeechTaConfig()

    if task == "s2t":
        A       :       Dict							=						config.max_text_positions
        A       :       Union[str, Any]							=						SpeechTaForSpeechToText(lowerCAmelCase__		)
    elif task == "t2s":
        A       :       Union[str, Any]							=						1_876
        A       :       List[Any]							=						600
        A       :       Union[str, Any]							=						config.max_speech_positions
        A       :       str							=						SpeechTaForTextToSpeech(lowerCAmelCase__		)
    elif task == "s2s":
        A       :       int							=						1_876
        A       :       Dict							=						config.max_speech_positions
        A       :       List[str]							=						SpeechTaForSpeechToSpeech(lowerCAmelCase__		)
    else:
        raise ValueError(F'''Unknown task name: {task}'''		)

    if vocab_path:
        A       :       Union[str, Any]							=						SpeechTaTokenizer(lowerCAmelCase__    ,       model_max_length=config.max_text_positions		)

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

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

    A       :       int							=						torch.load(lowerCAmelCase__		)
    recursively_load_weights(fairseq_checkpoint['''model''']    ,       lowerCAmelCase__    ,       lowerCAmelCase__		)

    model.save_pretrained(lowerCAmelCase__		)

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


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

     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,
     )







'''simple docstring'''






import requests

snake_case_     =	"""YOUR API KEY"""
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : str = giphy_api_key		)		->  list:
    A       :       Optional[Any]							=						'''+'''.join(query.split()		)
    A       :       List[Any]							=						F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
    A       :       int							=						requests.get(UpperCamelCase__		).json()['''data''']
    return [gif["url"] for gif in gifs]


if __name__ == "__main__":
     print("""\n""".join(get_gifs("""space ship""")))
