



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:List[str]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
SCREAMING_SNAKE_CASE_:Any					=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    for attribute in key.split("""."""		):
        A			:	Any										=       getattr(_lowerCAmelCase				,					_lowerCAmelCase		)

    if weight_type is not None:
        A			:	int										=       getattr(_lowerCAmelCase				,					_lowerCAmelCase		).shape
    else:
        A			:	Dict										=       hf_pointer.shape

    assert hf_shape == value.shape, (
        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			:	Tuple										=       value
    elif weight_type == "weight_g":
        A			:	List[str]										=       value
    elif weight_type == "weight_v":
        A			:	Dict										=       value
    elif weight_type == "bias":
        A			:	Optional[int]										=       value
    else:
        A			:	str										=       value

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Optional[int]										=       []
    A			:	str										=       fairseq_model.state_dict()

    A			:	Union[str, Any]										=       hf_model.feature_extractor

    # if encoder has different dim to decoder -> use proj_weight
    A			:	Tuple										=       None

    for name, value in fairseq_dict.items():
        A			:	List[str]										=       False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					hf_model.config.feat_extract_norm == """group"""				,					)
            A			:	Union[str, Any]										=       True
        elif name.split("""."""		)[0] == "proj":
            A			:	Optional[Any]										=       fairseq_model.proj
            A			:	Tuple										=       True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                    A			:	Optional[Any]										=       True
                    if "*" in mapped_key:
                        A			:	List[str]										=       name.split(_lowerCAmelCase		)[0].split("""."""		)[-2]
                        A			:	int										=       mapped_key.replace("""*"""				,					_lowerCAmelCase		)
                    if "weight_g" in name:
                        A			:	List[Any]										=       """weight_g"""
                    elif "weight_v" in name:
                        A			:	Optional[int]										=       """weight_v"""
                    elif "bias" in name:
                        A			:	str										=       """bias"""
                    elif "weight" in name:
                        A			:	Tuple										=       """weight"""
                    else:
                        A			:	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}'''		)

    return proj_weight




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	Any										=       full_name.split("""conv_layers."""		)[-1]
    A			:	int										=       name.split("""."""		)
    A			:	str										=       int(items[0]		)
    A			:	Optional[Any]										=       int(items[1]		)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
            )
            A			:	Tuple										=       value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
            )
            A			:	Tuple										=       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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                " found."
            )
            A			:	List[Any]										=       value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'''{full_name} has size {value.shape}, but'''
                f''' {feature_extractor[layer_id].layer_norm.weight.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}.'''		)
    else:
        unused_weights.append(_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A					,							A			:	Tuple										=       emb.weight.shape
    A			:	Union[str, Any]										=       nn.Linear(_lowerCAmelCase				,					_lowerCAmelCase				,					bias=_lowerCAmelCase		)
    A			:	List[Any]										=       emb.weight.data
    return lin_layer




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    with open(_lowerCAmelCase				,					"""r"""				,					encoding="""utf-8"""		) as f:
        A			:	Any										=       f.readlines()
        A			:	Optional[int]										=       [line.split(""" """		)[0] for line in lines]

    A			:	List[Any]										=       len(_lowerCAmelCase		)

    A			:	str										=       {
        """<s>""": 0,
        """<pad>""": 1,
        """</s>""": 2,
        """<unk>""": 3,
    }

    vocab_dict.update(dict(zip(_lowerCAmelCase				,					range(4				,					num_words + 4		)		)		)		)
    return vocab_dict




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					)	->  str:



    """simple docstring"""






    A			:	List[str]										=       WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
    A			:	Any										=       SpeechaTextaConfig.from_pretrained(
        _lowerCAmelCase				,					vocab_size=_lowerCAmelCase				,					decoder_layers=_lowerCAmelCase				,					do_stable_layer_norm=_lowerCAmelCase		)

    A			:	Optional[Any]										=       WavaVecaFeatureExtractor(
        feature_size=1				,					sampling_rate=1_6000				,					padding_value=0				,					do_normalize=_lowerCAmelCase				,					return_attention_mask=_lowerCAmelCase				,					)

    A					,							A					,							A			:	Any										=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]				,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
    A			:	int										=       model[0].eval()

    # set weights for wav2vec2 encoder
    A			:	Optional[int]										=       WavaVecaModel(_lowerCAmelCase		)
    A			:	Tuple										=       recursively_load_weights_wavaveca(model.encoder				,					_lowerCAmelCase		)

    A			:	Optional[int]										=       SpeechaTextaForCausalLM(_lowerCAmelCase		)
    A					,							A			:	List[Any]										=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,					strict=_lowerCAmelCase		)

    # set output linear layer
    unexpected_keys.remove("""embed_out"""		)
    A			:	List[str]										=       nn.Parameter(model.decoder.embed_out.detach()		)

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''		)
    logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''		)

    A			:	Any										=       SpeechEncoderDecoderModel(encoder=_lowerCAmelCase				,					decoder=_lowerCAmelCase		)
    A			:	Optional[Any]										=       False

    # add projection layer
    A			:	Union[str, Any]										=       nn.Parameter(projection_layer.weight		)
    A			:	str										=       nn.Parameter(projection_layer.bias		)

    A			:	Union[str, Any]										=       create_vocab_dict(_lowerCAmelCase		)

    with open(os.path.join(_lowerCAmelCase				,					"""vocab.json"""		)				,					"""w"""		) as fp:
        json.dump(_lowerCAmelCase				,					_lowerCAmelCase		)

    A			:	Optional[int]										=       SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase				,					"""vocab.json"""		)		)
    tokenizer.save_pretrained(_lowerCAmelCase		)

    A			:	Any										=       hf_wavavec.config.to_dict()
    A			:	Optional[int]										=       tokenizer.pad_token_id
    A			:	List[str]										=       tokenizer.bos_token_id
    A			:	Dict										=       tokenizer.eos_token_id
    A			:	List[str]										=       """speech_to_text_2"""
    A			:	Tuple										=       """wav2vec2"""

    A			:	Any										=       SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

    hf_wavavec.save_pretrained(_lowerCAmelCase		)
    feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Any					=						argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument(
         """--encoder_config_path""",
         default="""facebook/wav2vec2-large-lv60""",
         type=str,
         help="""Path to hf encoder wav2vec2 checkpoint config""",
     )
     parser.add_argument(
         """--decoder_config_path""",
         default="""facebook/s2t-small-mustc-en-fr-st""",
         type=str,
         help="""Path to hf decoder s2t checkpoint config""",
     )
     parser.add_argument("""--vocab_size""", default=10_224, type=int, help="""Vocab size of decoder""")
     parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

     SCREAMING_SNAKE_CASE_:List[str]					=						parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


