




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"detr"
    snake_case__ =				["past_key_values"]
    snake_case__ =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }


    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=True       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					int=3       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=100       ,     SCREAMING_SNAKE_CASE__					:					Dict=6       ,     SCREAMING_SNAKE_CASE__					:					Dict=2_048       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=8       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=6       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=2_048       ,     SCREAMING_SNAKE_CASE__					:					Tuple=8       ,     SCREAMING_SNAKE_CASE__					:					str=0.0       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.0       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					str="relu"       ,     SCREAMING_SNAKE_CASE__					:					int=256       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.0       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.0       ,     SCREAMING_SNAKE_CASE__					:					str=0.02       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=1.0       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]="sine"       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]="resnet50"       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=False       ,     SCREAMING_SNAKE_CASE__					:					int=1       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=5       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=1       ,     SCREAMING_SNAKE_CASE__					:					str=1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=5       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.1       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       ,     )				->	str:
         if backbone_config is not None and use_timm_backbone:
              raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."       )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
                   lowerCAmelCase__          =			CONFIG_MAPPING["resnet"](out_features=["stage4"]       )
              elif isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			backbone_config.get("model_type"       )
                   lowerCAmelCase__          =			CONFIG_MAPPING[backbone_model_type]
                   lowerCAmelCase__          =			config_class.from_dict(SCREAMING_SNAKE_CASE__       )
              # set timm attributes to None
              lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			None, None, None

         lowerCAmelCase__          =			use_timm_backbone
         lowerCAmelCase__          =			backbone_config
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			num_queries
         lowerCAmelCase__          =			d_model
         lowerCAmelCase__          =			encoder_ffn_dim
         lowerCAmelCase__          =			encoder_layers
         lowerCAmelCase__          =			encoder_attention_heads
         lowerCAmelCase__          =			decoder_ffn_dim
         lowerCAmelCase__          =			decoder_layers
         lowerCAmelCase__          =			decoder_attention_heads
         lowerCAmelCase__          =			dropout
         lowerCAmelCase__          =			attention_dropout
         lowerCAmelCase__          =			activation_dropout
         lowerCAmelCase__          =			activation_function
         lowerCAmelCase__          =			init_std
         lowerCAmelCase__          =			init_xavier_std
         lowerCAmelCase__          =			encoder_layerdrop
         lowerCAmelCase__          =			decoder_layerdrop
         lowerCAmelCase__          =			encoder_layers
         lowerCAmelCase__          =			auxiliary_loss
         lowerCAmelCase__          =			position_embedding_type
         lowerCAmelCase__          =			backbone
         lowerCAmelCase__          =			use_pretrained_backbone
         lowerCAmelCase__          =			dilation
         # Hungarian matcher
         lowerCAmelCase__          =			class_cost
         lowerCAmelCase__          =			bbox_cost
         lowerCAmelCase__          =			giou_cost
         # Loss coefficients
         lowerCAmelCase__          =			mask_loss_coefficient
         lowerCAmelCase__          =			dice_loss_coefficient
         lowerCAmelCase__          =			bbox_loss_coefficient
         lowerCAmelCase__          =			giou_loss_coefficient
         lowerCAmelCase__          =			eos_coefficient
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @property
    def        a				(	self					:					Optional[int]       )				->	int:
         return self.encoder_attention_heads


    @property
    def        a				(	self					:					Tuple       )				->	int:
         return self.d_model


    @classmethod
    def        a				(	cls					:					str       ,     SCREAMING_SNAKE_CASE__					:					PretrainedConfig       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Optional[int]:
         return cls(backbone_config=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       )				->	Dict[str, any]:
         lowerCAmelCase__          =			copy.deepcopy(self.__dict__       )
         if output["backbone_config"] is not None:
              lowerCAmelCase__          =			self.backbone_config.to_dict()
         lowerCAmelCase__          =			self.__class__.model_type
         return output






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				version.parse("1.11"   )


    @property
    def        a				(	self					:					Dict       )				->	Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                 ("pixel_mask", {0: "batch"}),
             ]       )


    @property
    def        a				(	self					:					str       )				->	float:
         return 1e-5


    @property
    def        a				(	self					:					str       )				->	int:
         return 12






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCamelCase								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			{}
     with open(lowerCAmelCase_       ,    "r"      ) as file:
          for line_number, line in enumerate(lowerCAmelCase_      ):
               lowerCAmelCase__          =			line.strip()
               if line:
                    lowerCAmelCase__          =			line.split()
                    lowerCAmelCase__          =			line_number
                    lowerCAmelCase__          =			words[0]
                    lowerCAmelCase__          =			value
     return result



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     for attribute in key.split("."      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      ).shape
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			hf_pointer
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			shape_pointer.shape

          # let's reduce dimension
          lowerCAmelCase__          =			value[0]
     else:
          lowerCAmelCase__          =			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":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_g":
          lowerCAmelCase__          =			value
     elif weight_type == "weight_v":
          lowerCAmelCase__          =			value
     elif weight_type == "bias":
          lowerCAmelCase__          =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			value
     else:
          lowerCAmelCase__          =			value

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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			".".join([key, weight_type]      )
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			".".join([key, hf_param_name]      )
     else:
          lowerCAmelCase__          =			key

     lowerCAmelCase__          =			value if "lm_head" in full_key else value[0]


UpperCamelCase								=					{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     lowerCAmelCase__          =			False
     for key, mapped_key in MAPPING.items():
          lowerCAmelCase__          =			"wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
               lowerCAmelCase__          =			True
               if "*" in mapped_key:
                    lowerCAmelCase__          =			name.split(lowerCAmelCase_      )[0].split("."      )[-2]
                    lowerCAmelCase__          =			mapped_key.replace("*"       ,    lowerCAmelCase_      )
               if "weight_g" in name:
                    lowerCAmelCase__          =			"weight_g"
               elif "weight_v" in name:
                    lowerCAmelCase__          =			"weight_v"
               elif "bias" in name:
                    lowerCAmelCase__          =			"bias"
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    lowerCAmelCase__          =			"weight"
               else:
                    lowerCAmelCase__          =			None
               if hf_dict is not None:
                    rename_dict(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               else:
                    set_recursively(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               return is_used
     return is_used



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          lowerCAmelCase__          =			False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    hf_model.config.feat_extract_norm == "group"       ,    )
               lowerCAmelCase__          =			True
          else:
               lowerCAmelCase__          =			load_wavaveca_layer(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
          if not is_used:
               unused_weights.append(lowerCAmelCase_      )

     logger.warning(F'Unused weights: {unused_weights}'      )



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			full_name.split("conv_layers."      )[-1]
     lowerCAmelCase__          =			name.split("."      )
     lowerCAmelCase__          =			int(items[0]      )
     lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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.'      )
               lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Optional[int]=True       ,    lowerCAmelCase_					: List[str]=False      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaConfig()

     if is_seq_class:
          lowerCAmelCase__          =			read_txt_into_dict(lowerCAmelCase_      )
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			WavaVecaForSequenceClassification(lowerCAmelCase_      )
          lowerCAmelCase__          =			WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase_      )

     elif is_finetuned:
          if dict_path:
               lowerCAmelCase__          =			Dictionary.load(lowerCAmelCase_      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowerCAmelCase__          =			target_dict.pad_index
               lowerCAmelCase__          =			target_dict.bos_index
               lowerCAmelCase__          =			target_dict.eos_index
               lowerCAmelCase__          =			len(target_dict.symbols      )
               lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    "vocab.json"      )
               if not os.path.isdir(lowerCAmelCase_      ):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase_      )      )
                    return
               os.makedirs(lowerCAmelCase_       ,    exist_ok=lowerCAmelCase_      )
               lowerCAmelCase__          =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			WavaVecaCTCTokenizer(
                   lowerCAmelCase_       ,    unk_token=target_dict.unk_word       ,    pad_token=target_dict.pad_word       ,    bos_token=target_dict.bos_word       ,    eos_token=target_dict.eos_word       ,    word_delimiter_token="|"       ,    do_lower_case=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			True if config.feat_extract_norm == "layer" else False
               lowerCAmelCase__          =			WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,    tokenizer=lowerCAmelCase_      )
               processor.save_pretrained(lowerCAmelCase_      )

          lowerCAmelCase__          =			WavaVecaForCTC(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaForPreTraining(lowerCAmelCase_      )

     if is_finetuned or is_seq_class:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]       ,    arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     else:
          lowerCAmelCase__          =			argparse.Namespace(task="audio_pretraining"      )
          lowerCAmelCase__          =			fairseq.tasks.setup_task(lowerCAmelCase_      )

          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=lowerCAmelCase_      )

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(lowerCAmelCase_       ,    lowerCAmelCase_       ,    not is_finetuned      )

     hf_wavavec.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     parser.add_argument(
         '--is_seq_class',
         action='store_true',
         help='Whether the model to convert is a fine-tuned sequence classification model or not',
     )
     UpperCamelCase								=					parser.parse_args()

     UpperCamelCase								=					not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )
