
"""simple docstring"""




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_A     :			str  						=			logging.get_logger(__name__)

_A     :			str  						=			{
    """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""",
}
_A     :			Any  						=			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Dict	,					__snake_case							:			List[str]	,					__snake_case							:			Optional[Any]	,					__snake_case							:			Union[str, Any]    )					->							List[Any]:





       for attribute in key.split("."    ):
              lowercase       :							Optional[Any]									=				getattr(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    )

       if weight_type is not None:
              lowercase       :							str									=				getattr(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    ).shape
       else:
              lowercase       :							Optional[Any]									=				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":
              lowercase       :							Tuple									=				value
       elif weight_type == "weight_g":
              lowercase       :							Any									=				value
       elif weight_type == "weight_v":
              lowercase       :							Any									=				value
       elif weight_type == "bias":
              lowercase       :							Dict									=				value
       else:
              lowercase       :							Union[str, Any]									=				value

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


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			Optional[Any]    )					->							List[Any]:
       lowercase       :							Optional[int]									=				[]
       lowercase       :							Optional[Any]									=				fairseq_model.state_dict()

       lowercase       :							Tuple									=				hf_model.feature_extractor
       lowercase       :							int									=				hf_model.adapter

       for name, value in fairseq_dict.items():
              lowercase       :							Optional[Any]									=				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       :							Optional[int]									=				True
              elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]    ):
                     load_adapter(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    )
                     lowercase       :							int									=				True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
                                   lowercase       :							Dict									=				True
                                   if "*" in mapped_key:
                                          lowercase       :							List[str]									=				name.split(SCREAMING_SNAKE_CASE__    )[0].split("."    )[-2]
                                          lowercase       :							Dict									=				mapped_key.replace("*"	,					SCREAMING_SNAKE_CASE__    )
                                   if "weight_g" in name:
                                          lowercase       :							List[Any]									=				'weight_g'
                                   elif "weight_v" in name:
                                          lowercase       :							int									=				'weight_v'
                                   elif "bias" in name:
                                          lowercase       :							int									=				'bias'
                                   elif "weight" in name:
                                          lowercase       :							Optional[int]									=				'weight'
                                   else:
                                          lowercase       :							Any									=				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     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Any	,					__snake_case							:			List[Any]	,					__snake_case							:			str	,					__snake_case							:			Any    )					->							List[str]:
       lowercase       :							str									=				full_name.split("conv_layers."    )[-1]
       lowercase       :							List[Any]									=				name.split("."    )
       lowercase       :							Optional[int]									=				int(items[0]    )
       lowercase       :							List[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."""
                     )
                     lowercase       :							Any									=				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."""
                     )
                     lowercase       :							int									=				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."
                     )
                     lowercase       :							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."""
                     )
                     lowercase       :							Tuple									=				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__    )


def     __magic_name__      ( __snake_case							:			Optional[Any]	,					__snake_case							:			List[str]	,					__snake_case							:			Optional[int]	,					__snake_case							:			Dict    )					->							Optional[int]:
       lowercase       :							Optional[int]									=				full_name.split("adaptor."    )[-1]
       lowercase       :							str									=				name.split("."    )

       if items[1].isdigit():
              lowercase       :							Any									=				int(items[1]    )
       else:
              lowercase       :							int									=				None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                            lowercase       :							str									=				value
                            logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                            lowercase       :							str									=				value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                            lowercase       :							Optional[Any]									=				value
                            logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                            lowercase       :							Optional[Any]									=				value
                            logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""    )
       elif isinstance(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__    ):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
                     lowercase       :							List[Any]									=				value
                     logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""    )
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
                     lowercase       :							Dict									=				value
                     logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""    )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__    )


def     __magic_name__      ( __snake_case							:			Tuple    )					->							List[str]:
       lowercase       :							List[Any]									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					bias=SCREAMING_SNAKE_CASE__    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			List[str]	,					__snake_case							:			Any	,					__snake_case							:			List[Any]	,					__snake_case							:			List[Any]	,					__snake_case							:			str	,					__snake_case							:			Any	,					__snake_case							:			Dict	,					__snake_case							:			str	,					__snake_case							:			Any	,					__snake_case							:			List[Any]	,					)					->							Optional[int]:
       lowercase       :							int									=				WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__	,					add_adapter=SCREAMING_SNAKE_CASE__	,					adapter_stride=SCREAMING_SNAKE_CASE__	,					adapter_kernel_size=SCREAMING_SNAKE_CASE__	,					use_auth_token=SCREAMING_SNAKE_CASE__	,					output_hidden_size=SCREAMING_SNAKE_CASE__	,					)
       lowercase       :							str									=				MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # load model
       lowercase       :							str									=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]	,					arg_overrides={
               "config_yaml": config_yaml_path,
               "data": "/".join(dict_path.split("/"    )[:-1]    ),
               "w2v_path": checkpoint_path,
               "load_pretrained_decoder_from": None,
           }	,					)
       lowercase       :							Any									=				model[0].eval()

       # load feature extractor
       lowercase       :							List[Any]									=				WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__	,					use_auth_token=SCREAMING_SNAKE_CASE__    )

       # set weights for wav2vec2 encoder
       lowercase       :							List[Any]									=				WavaVecaModel(SCREAMING_SNAKE_CASE__    )

       recursively_load_weights_wavaveca(model.encoder	,					SCREAMING_SNAKE_CASE__    )

       # load decoder weights
       lowercase       :							int									=				MBartForCausalLM(SCREAMING_SNAKE_CASE__    )
       lowercase       :							Union[str, Any]									=				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,					strict=SCREAMING_SNAKE_CASE__    )
       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}"""    )

       lowercase       :							Dict									=				SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__	,					decoder=SCREAMING_SNAKE_CASE__    )
       lowercase       :							List[Any]									=				False

       lowercase       :							Any									=				MBartaaTokenizer(SCREAMING_SNAKE_CASE__    )
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__    )

       lowercase       :							int									=				hf_wavavec.config.to_dict()
       lowercase       :							Optional[int]									=				tokenizer.pad_token_id
       lowercase       :							Tuple									=				tokenizer.bos_token_id
       lowercase       :							Any									=				tokenizer.eos_token_id
       lowercase       :							int									=				'mbart50'
       lowercase       :							Tuple									=				'wav2vec2'

       lowercase       :							Tuple									=				tokenizer.eos_token_id
       lowercase       :							Dict									=				25_0004
       lowercase       :							List[str]									=				tokenizer.eos_token_id

       lowercase       :							Dict									=				SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__    )

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__    )
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   _A     :			int  						=			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("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
   parser.add_argument(
       """--encoder_config_path""",
       default="""facebook/wav2vec2-xls-r-1b""",
       type=str,
       help="""Path to hf encoder wav2vec2 checkpoint config""",
   )
   parser.add_argument(
       """--decoder_config_path""",
       default="""facebook/mbart-large-50-one-to-many-mmt""",
       type=str,
       help="""Path to hf decoder checkpoint config""",
   )
   parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
   parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
   parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
   parser.add_argument("""--encoder_output_dim""", default=10_24, type=int, help="""encoder output dim""")
   parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

   _A     :			Union[str, Any]  						=			parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       args.config_yaml_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       add_adapter=args.add_adapter,
       adapter_kernel_size=args.adapter_kernel_size,
       adapter_stride=args.adapter_stride,
       decoder_start_token_id=args.start_token_id,
       encoder_output_dim=args.encoder_output_dim,
   )






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}




class A    (  UpperCamelCase_       ,				UpperCamelCase_	):
      UpperCamelCase__				:    str ='focalnet'






      def __init__(   self		:	Optional[int]     ,  lowercase_		:	List[Any]=224     ,  lowercase_		:	str=4     ,  lowercase_		:	Any=3     ,  lowercase_		:	Dict=96     ,  lowercase_		:	List[str]=False     ,  lowercase_		:	Any=[192, 384, 768, 768]     ,  lowercase_		:	Dict=[2, 2, 6, 2]     ,  lowercase_		:	Optional[int]=[2, 2, 2, 2]     ,  lowercase_		:	List[Any]=[3, 3, 3, 3]     ,  lowercase_		:	Any="gelu"     ,  lowercase_		:	str=4.0     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Optional[int]=1E-4     ,  lowercase_		:	Dict=False     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Dict=False     ,  lowercase_		:	List[Any]=0.02     ,  lowercase_		:	Any=1E-5     ,  lowercase_		:	Any=32     ,  lowercase_		:	List[str]=None     ,  lowercase_		:	int=None     ,  **lowercase_		:	Optional[Any]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =image_size
             _lowerCamelCase			:						str            =patch_size
             _lowerCamelCase			:						str            =num_channels
             _lowerCamelCase			:						Tuple            =embed_dim
             _lowerCamelCase			:						Dict            =use_conv_embed
             _lowerCamelCase			:						int            =hidden_sizes
             _lowerCamelCase			:						str            =depths
             _lowerCamelCase			:						int            =focal_levels
             _lowerCamelCase			:						List[Any]            =focal_windows
             _lowerCamelCase			:						List[Any]            =hidden_act
             _lowerCamelCase			:						int            =mlp_ratio
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Optional[Any]            =drop_path_rate
             _lowerCamelCase			:						int            =use_layerscale
             _lowerCamelCase			:						str            =layerscale_value
             _lowerCamelCase			:						Dict            =use_post_layernorm
             _lowerCamelCase			:						str            =use_post_layernorm_in_modulation
             _lowerCamelCase			:						List[Any]            =normalize_modulator
             _lowerCamelCase			:						Optional[Any]            =initializer_range
             _lowerCamelCase			:						List[str]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =encoder_stride
             _lowerCamelCase			:						List[str]            =['stem'] + [F'''stage{idx}''' for idx in range(1     ,  len(self.depths					) + 1					)]
             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =get_aligned_output_features_output_indices(
                 out_features=lowercase_     ,  out_indices=lowercase_     ,  stage_names=self.stage_names					)


