



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_   :str        =      logging.get_logger(__name__)
def        lowercase_							(A : str      ):
       snake_case__							:	Tuple   	=	SwinConfig.from_pretrained(
           'microsoft/swin-tiny-patch4-window7-224'		,					out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
       snake_case__							:	List[Any]   	=	MaskFormerConfig(backbone_config=A      )

       snake_case__							:	Union[str, Any]   	=	'huggingface/label-files'
       if "ade20k-full" in model_name:
              # this should be ok
              snake_case__							:	Dict   	=	8_4_7
              snake_case__							:	List[str]   	=	'maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              snake_case__							:	Union[str, Any]   	=	1_5_0
              snake_case__							:	Any   	=	'ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_7_1
              snake_case__							:	Union[str, Any]   	=	'maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              snake_case__							:	Dict   	=	1_3_3
              snake_case__							:	str   	=	'coco-panoptic-id2label.json'
       elif "cityscapes" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_9
              snake_case__							:	Union[str, Any]   	=	'cityscapes-id2label.json'
       elif "vistas" in model_name:
              # this should be ok
              snake_case__							:	Tuple   	=	6_5
              snake_case__							:	List[str]   	=	'mapillary-vistas-id2label.json'

       snake_case__							:	Dict   	=	json.load(open(hf_hub_download(A		,					A		,					repo_type='dataset'      )		,					'r'      )      )
       snake_case__							:	List[str]   	=	{int(A      ): v for k, v in idalabel.items()}

       return config
def        lowercase_							(A : Any      ):
       snake_case__							:	Optional[int]   	=	[]
       # stem
       # fmt: off
       rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')      )
       rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')      )
       rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')      )
       rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')      )
       # stages
       for i in range(len(config.backbone_config.depths      )      ):
              for j in range(config.backbone_config.depths[i]      ):
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')      )

              if i < 3:
                     rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')      )
              rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')      )
              rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')      )

       # FPN
       rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')      )
       rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')      )
       rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')      )
       for source_index, target_index in zip(range(3		,					0		,					-1      )		,					range(0		,					3      )      ):
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')      )
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')      )
       rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')      )
       rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')      )

       # Transformer decoder
       for idx in range(config.decoder_config.decoder_layers      ):
              # self-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')      )
              # cross-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')      )
              # MLP 1
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')      )
              # MLP 2
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')      )
              # layernorm 1 (self-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')      )
              # layernorm 2 (cross-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')      )
              # layernorm 3 (final layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')      )

       rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')      )
       rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')      )

       # heads on top
       rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')      )

       rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')      )
       rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')      )

       rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')      )
       rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')      )

       for i in range(3      ):
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')      )
       # fmt: on

       return rename_keys
def        lowercase_							(A : Tuple		,					A : Tuple		,					A : Optional[Any]      ):
       snake_case__							:	Optional[int]   	=	dct.pop(A      )
       snake_case__							:	Union[str, Any]   	=	val
def        lowercase_							(A : Optional[Any]		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
       for i in range(len(backbone_config.depths      )      ):
              snake_case__							:	Optional[int]   	=	num_features[i]
              for j in range(backbone_config.depths[i]      ):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     snake_case__							:	int   	=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
                     snake_case__							:	Tuple   	=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''      )
                     # next, add query, keys and values (in that order) to the state dict
                     snake_case__							:	str   	=	in_proj_weight[:dim, :]
                     snake_case__							:	int   	=	in_proj_bias[: dim]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         dim : dim * 2, :
                     ]
                     snake_case__							:	List[str]   	=	in_proj_bias[
                         dim : dim * 2
                     ]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         -dim :, :
                     ]
                     snake_case__							:	Dict   	=	in_proj_bias[-dim :]
                     # fmt: on
def        lowercase_							(A : List[str]		,					A : List[Any]      ):
       # fmt: off
       snake_case__							:	str   	=	config.decoder_config.hidden_size
       for idx in range(config.decoder_config.decoder_layers      ):
              # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''      )
              snake_case__							:	int   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''      )
              # next, add query, keys and values (in that order) to the state dict
              snake_case__							:	Any   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Tuple   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	List[str]   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	Dict   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	Any   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	int   	=	in_proj_bias[-hidden_size :]
              # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''      )
              snake_case__							:	List[str]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''      )
              # next, add query, keys and values (in that order) to the state dict
              snake_case__							:	Optional[int]   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Optional[Any]   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	int   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	List[str]   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	List[str]   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	str   	=	in_proj_bias[-hidden_size :]
       # fmt: on
def        lowercase_							():
       snake_case__							:	Any   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:	int   	=	Image.open(requests.get(A		,					stream=A      ).raw      )
       return im
@torch.no_grad()
def        lowercase_							(A : str		,					A : str		,					A : str		,					A : bool = False      ):
       snake_case__							:	Optional[int]   	=	get_maskformer_config(A      )

       # load original state_dict
       with open(A		,					'rb'      ) as f:
              snake_case__							:	List[Any]   	=	pickle.load(A      )
       snake_case__							:	Optional[int]   	=	data['model']

       # for name, param in state_dict.items():
       #     print(name, param.shape)

       # rename keys
       snake_case__							:	List[str]   	=	create_rename_keys(A      )
       for src, dest in rename_keys:
              rename_key(A		,					A		,					A      )
       read_in_swin_q_k_v(A		,					config.backbone_config      )
       read_in_decoder_q_k_v(A		,					A      )

       # update to torch tensors
       for key, value in state_dict.items():
              snake_case__							:	int   	=	torch.from_numpy(A      )

       # load 🤗 model
       snake_case__							:	str   	=	MaskFormerForInstanceSegmentation(A      )
       model.eval()

       for name, param in model.named_parameters():
              print(A		,					param.shape      )

       snake_case__	,				snake_case__							:	Union[str, Any]   	=	model.load_state_dict(A		,					strict=A      )
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(A      ) == 0, F'''Unexpected keys: {unexpected_keys}'''

       # verify results
       snake_case__							:	Optional[Any]   	=	prepare_img()
       if "vistas" in model_name:
              snake_case__							:	int   	=	6_5
       elif "cityscapes" in model_name:
              snake_case__							:	Dict   	=	6_5_5_3_5
       else:
              snake_case__							:	Tuple   	=	2_5_5
       snake_case__							:	Optional[int]   	=	True if 'ade' in model_name else False
       snake_case__							:	Dict   	=	MaskFormerImageProcessor(ignore_index=A		,					reduce_labels=A      )

       snake_case__							:	Any   	=	image_processor(A		,					return_tensors='pt'      )

       snake_case__							:	Any   	=	model(**A      )

       print('Logits:'		,					outputs.class_queries_logits[0, :3, :3]      )

       if model_name == "maskformer-swin-tiny-ade":
              snake_case__							:	Tuple   	=	torch.tensor(
                  [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]      )
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,					A		,					atol=1e-4      )
       print('Looks ok!'      )

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''      )
              Path(A      ).mkdir(exist_ok=A      )
              model.save_pretrained(A      )
              image_processor.save_pretrained(A      )

       if push_to_hub:
              print('Pushing model and image processor to the hub...'      )
              model.push_to_hub(F'''nielsr/{model_name}'''      )
              image_processor.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
 a_   :Optional[int]        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="maskformer-swin-tiny-ade",
     type=str,
     help=("Name of the MaskFormer model you'd like to convert",),
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
     type=str,
     help="Path to the original state dict (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 a_   :Dict        =      parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )











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()
a_   :List[Any]        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "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",
}
a_   :List[Any]        =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def        lowercase_							(A : Dict      ):
       snake_case__							:	Optional[Any]   	=	{}
       with open(A		,					'r'      ) as file:
              for line_number, line in enumerate(A      ):
                     snake_case__							:	Dict   	=	line.strip()
                     if line:
                            snake_case__							:	int   	=	line.split()
                            snake_case__							:	List[str]   	=	line_number
                            snake_case__							:	Dict   	=	words[0]
                            snake_case__							:	Optional[Any]   	=	value
       return result
def        lowercase_							(A : int		,					A : int		,					A : Optional[int]		,					A : Optional[Any]		,					A : Tuple      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Optional[int]   	=	getattr(A		,					A      )

       snake_case__							:	Union[str, Any]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	Dict   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	Union[str, Any]   	=	getattr(A		,					A      ).shape
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Optional[int]   	=	hf_pointer
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	Optional[Any]   	=	getattr(A		,					A      )
              snake_case__							:	Dict   	=	shape_pointer.shape

              # let's reduce dimension
              snake_case__							:	List[Any]   	=	value[0]
       else:
              snake_case__							:	Union[str, Any]   	=	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":
              snake_case__							:	Any   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	Any   	=	value
       elif weight_type == "bias":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	int   	=	getattr(A		,					A      )
              snake_case__							:	Optional[int]   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : Tuple		,					A : List[Any]		,					A : int		,					A : str		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	str   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	int   	=	'.'.join([key, weight_type]      )
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Any   	=	'.'.join([key, hf_param_name]      )
       else:
              snake_case__							:	Dict   	=	key

       snake_case__							:	List[str]   	=	value if 'lm_head' in full_key else value[0]


a_   :List[str]        =      {
    "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        lowercase_							(A : str		,					A : Optional[Any]		,					A : Optional[Any]=None		,					A : List[str]=None      ):
       snake_case__							:	Optional[int]   	=	False
       for key, mapped_key in MAPPING.items():
              snake_case__							:	Tuple   	=	'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]:
                     snake_case__							:	Optional[int]   	=	True
                     if "*" in mapped_key:
                            snake_case__							:	List[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                            snake_case__							:	Union[str, Any]   	=	mapped_key.replace('*'		,					A      )
                     if "weight_g" in name:
                            snake_case__							:	Tuple   	=	'weight_g'
                     elif "weight_v" in name:
                            snake_case__							:	List[str]   	=	'weight_v'
                     elif "bias" in name:
                            snake_case__							:	Dict   	=	'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            snake_case__							:	Optional[int]   	=	'weight'
                     else:
                            snake_case__							:	str   	=	None
                     if hf_dict is not None:
                            rename_dict(A		,					A		,					A		,					A		,					A      )
                     else:
                            set_recursively(A		,					A		,					A		,					A		,					A      )
                     return is_used
       return is_used
def        lowercase_							(A : Optional[Any]		,					A : Dict		,					A : Optional[int]      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	Tuple   	=	fairseq_model.state_dict()

       snake_case__							:	str   	=	hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__							:	str   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	Any   	=	True
              else:
                     snake_case__							:	Dict   	=	load_wavaveca_layer(A		,					A		,					A      )
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Dict		,					A : Optional[Any]		,					A : Tuple		,					A : str		,					A : List[str]      ):
       snake_case__							:	List[Any]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	List[str]   	=	name.split('.'      )
       snake_case__							:	List[Any]   	=	int(items[0]      )
       snake_case__							:	str   	=	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.'''      )
                     snake_case__							:	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.'''      )
                     snake_case__							:	str   	=	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.'''      )
                     snake_case__							:	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.'''      )
                     snake_case__							:	int   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Tuple=None		,					A : List[str]=None		,					A : Any=True		,					A : Optional[int]=False      ):
       if config_path is not None:
              snake_case__							:	List[Any]   	=	WavaVecaConfig.from_pretrained(A      )
       else:
              snake_case__							:	List[Any]   	=	WavaVecaConfig()

       if is_seq_class:
              snake_case__							:	Dict   	=	read_txt_into_dict(A      )
              snake_case__							:	Any   	=	idalabel
              snake_case__							:	Union[str, Any]   	=	WavaVecaForSequenceClassification(A      )
              snake_case__							:	Any   	=	WavaVecaFeatureExtractor(
                  feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
              feature_extractor.save_pretrained(A      )

       elif is_finetuned:
              if dict_path:
                     snake_case__							:	str   	=	Dictionary.load(A      )

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

                     # fairseq has the <pad> and <s> switched
                     snake_case__							:	Optional[Any]   	=	0
                     snake_case__							:	Union[str, Any]   	=	1
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(A		,					A      )
                     snake_case__							:	List[Any]   	=	WavaVecaCTCTokenizer(
                         A		,					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=A		,					)
                     snake_case__							:	str   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	Optional[Any]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	Union[str, Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	str   	=	WavaVecaForCTC(A      )
       else:
              snake_case__							:	int   	=	WavaVecaForPreTraining(A      )

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

              snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,					task=A      )

       snake_case__							:	List[Any]   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					not is_finetuned      )

       hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 a_   :List[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("--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",
 )
 a_   :str        =      parser.parse_args()

 a_   :Tuple        =      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,
 )






