import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase      		=					{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[int]		,    lowercase_:						Optional[Any]		,    lowercase_:						Union[str, Any]		,    lowercase_:						Optional[Any]					):
       for attribute in key.split('.'					):
              _UpperCamelCase  :							Tuple	    =	getattr(lowercase_		,    lowercase_					)

       if weight_type is not None:
              _UpperCamelCase  :							Union[str, Any]	    =	getattr(lowercase_		,    lowercase_					).shape
       else:
              _UpperCamelCase  :							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":
              _UpperCamelCase  :							Dict	    =	value
       elif weight_type == "weight_g":
              _UpperCamelCase  :							List[str]	    =	value
       elif weight_type == "weight_v":
              _UpperCamelCase  :							str	    =	value
       elif weight_type == "bias":
              _UpperCamelCase  :							Optional[Any]	    =	value
       else:
              _UpperCamelCase  :							Optional[int]	    =	value

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


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[str]		,    lowercase_:						str					):
       _UpperCamelCase  :							Optional[Any]	    =	[]
       _UpperCamelCase  :							int	    =	fairseq_model.state_dict()

       _UpperCamelCase  :							Tuple	    =	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _UpperCamelCase  :							Tuple	    =	False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowercase_		,    lowercase_		,    lowercase_		,    lowercase_		,    hf_model.config.feat_extract_norm == 'group'		,    )
                     _UpperCamelCase  :							Tuple	    =	True
              else:
                     for key, mapped_key in MAPPING.items():
                            _UpperCamelCase  :							Tuple	    =	'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                            if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
                                   _UpperCamelCase  :							str	    =	True
                                   if "*" in mapped_key:
                                          _UpperCamelCase  :							Optional[int]	    =	name.split(lowercase_					)[0].split('.'					)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          _UpperCamelCase  :							List[str]	    =	mapped_key.replace('*'		,    lowercase_					)
                                   if "weight_g" in name:
                                          _UpperCamelCase  :							Union[str, Any]	    =	'weight_g'
                                   elif "weight_v" in name:
                                          _UpperCamelCase  :							str	    =	'weight_v'
                                   elif "weight" in name:
                                          _UpperCamelCase  :							Tuple	    =	'weight'
                                   elif "bias" in name:
                                          _UpperCamelCase  :							str	    =	'bias'
                                   else:
                                          _UpperCamelCase  :							Dict	    =	None
                                   set_recursively(lowercase_		,    lowercase_		,    lowercase_		,    lowercase_		,    lowercase_					)
                            continue
              if not is_used:
                     unused_weights.append(lowercase_					)

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


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[Any]		,    lowercase_:						Tuple		,    lowercase_:						str		,    lowercase_:						Any		,    lowercase_:						Tuple					):
       _UpperCamelCase  :							Optional[int]	    =	full_name.split('conv_layers.'					)[-1]
       _UpperCamelCase  :							str	    =	name.split('.'					)
       _UpperCamelCase  :							Optional[Any]	    =	int(items[0]					)
       _UpperCamelCase  :							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."
                     )
                     _UpperCamelCase  :							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."
                     )
                     _UpperCamelCase  :							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."
                     )
                     _UpperCamelCase  :							Optional[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."
                     )
                     _UpperCamelCase  :							Union[str, Any]	    =	value
                     logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
       else:
              unused_weights.append(lowercase_					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Dict					):
       _UpperCamelCase  :							List[str]	    =	SEWDConfig()
       if is_finetuned:
              _UpperCamelCase  :							Tuple	    =	model.wav_encoder.wav_model.cfg
       else:
              _UpperCamelCase  :							Optional[int]	    =	model.cfg

       _UpperCamelCase  :							int	    =	fs_config.conv_bias
       _UpperCamelCase  :							List[str]	    =	eval(fs_config.conv_feature_layers					)
       _UpperCamelCase  :							str	    =	[x[0] for x in conv_layers]
       _UpperCamelCase  :							str	    =	[x[1] for x in conv_layers]
       _UpperCamelCase  :							List[Any]	    =	[x[2] for x in conv_layers]
       _UpperCamelCase  :							Optional[Any]	    =	'gelu'
       _UpperCamelCase  :							List[Any]	    =	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       _UpperCamelCase  :							List[Any]	    =	0.0
       _UpperCamelCase  :							str	    =	fs_config.activation_fn.name
       _UpperCamelCase  :							int	    =	fs_config.encoder_embed_dim
       _UpperCamelCase  :							Optional[Any]	    =	0.02
       _UpperCamelCase  :							Union[str, Any]	    =	fs_config.encoder_ffn_embed_dim
       _UpperCamelCase  :							int	    =	1e-5
       _UpperCamelCase  :							Any	    =	fs_config.encoder_layerdrop
       _UpperCamelCase  :							Optional[Any]	    =	fs_config.encoder_attention_heads
       _UpperCamelCase  :							Tuple	    =	fs_config.conv_pos_groups
       _UpperCamelCase  :							int	    =	fs_config.conv_pos
       _UpperCamelCase  :							int	    =	len(lowercase_					)
       _UpperCamelCase  :							Dict	    =	fs_config.encoder_layers
       _UpperCamelCase  :							str	    =	fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       _UpperCamelCase  :							List[Any]	    =	fs_config.max_position_embeddings
       _UpperCamelCase  :							List[Any]	    =	fs_config.position_buckets
       _UpperCamelCase  :							Any	    =	fs_config.share_att_key
       _UpperCamelCase  :							str	    =	fs_config.relative_attention
       _UpperCamelCase  :							Optional[Any]	    =	fs_config.position_biased_input
       _UpperCamelCase  :							List[str]	    =	tuple(fs_config.pos_att_type.split('|'					)					)
       _UpperCamelCase  :							Optional[Any]	    =	fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              _UpperCamelCase  :							Union[str, Any]	    =	model.cfg
              _UpperCamelCase  :							Tuple	    =	fs_config.final_dropout
              _UpperCamelCase  :							int	    =	fs_config.layerdrop
       _UpperCamelCase  :							Tuple	    =	fs_config.activation_dropout
       _UpperCamelCase  :							Optional[int]	    =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       _UpperCamelCase  :							Optional[int]	    =	fs_config.attention_dropout
       _UpperCamelCase  :							Optional[Any]	    =	fs_config.dropout_input
       _UpperCamelCase  :							Optional[int]	    =	fs_config.dropout
       _UpperCamelCase  :							Optional[Any]	    =	fs_config.mask_channel_length
       _UpperCamelCase  :							Any	    =	fs_config.mask_channel_prob
       _UpperCamelCase  :							List[str]	    =	fs_config.mask_length
       _UpperCamelCase  :							str	    =	fs_config.mask_prob

       _UpperCamelCase  :							Dict	    =	'Wav2Vec2FeatureExtractor'
       _UpperCamelCase  :							str	    =	'Wav2Vec2CTCTokenizer'

       return config


@torch.no_grad()
def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						List[str]		,    lowercase_:						int=None		,    lowercase_:						Optional[Any]=None		,    lowercase_:						Union[str, Any]=True					):

       if is_finetuned:
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]		,    arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
       else:
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

       if config_path is not None:
              _UpperCamelCase  :							List[str]	    =	SEWDConfig.from_pretrained(lowercase_					)
       else:
              _UpperCamelCase  :							List[Any]	    =	convert_config(model[0]		,    lowercase_					)
       _UpperCamelCase  :							Optional[Any]	    =	model[0].eval()

       _UpperCamelCase  :							Any	    =	True if config.feat_extract_norm == 'layer' else False
       _UpperCamelCase  :							Optional[Any]	    =	WavaVecaFeatureExtractor(
           feature_size=1		,    sampling_rate=16000		,    padding_value=0		,    do_normalize=lowercase_		,    return_attention_mask=lowercase_		,    )

       if is_finetuned:
              if dict_path:
                     _UpperCamelCase  :							List[Any]	    =	Dictionary.load(lowercase_					)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _UpperCamelCase  :							Optional[Any]	    =	target_dict.pad_index
                     _UpperCamelCase  :							Optional[Any]	    =	target_dict.bos_index
                     _UpperCamelCase  :							List[str]	    =	target_dict.pad_index
                     _UpperCamelCase  :							Optional[int]	    =	target_dict.bos_index
                     _UpperCamelCase  :							List[str]	    =	target_dict.eos_index
                     _UpperCamelCase  :							Union[str, Any]	    =	len(target_dict.symbols					)
                     _UpperCamelCase  :							int	    =	os.path.join(lowercase_		,    'vocab.json'					)
                     if not os.path.isdir(lowercase_					):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase_					)					)
                            return
                     os.makedirs(lowercase_		,    exist_ok=lowercase_					)
                     with open(lowercase_		,    'w'		,    encoding='utf-8'					) as vocab_handle:
                            json.dump(target_dict.indices		,    lowercase_					)
                     _UpperCamelCase  :							Union[str, Any]	    =	WavaVecaCTCTokenizer(
                         lowercase_		,    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=lowercase_		,    )
                     _UpperCamelCase  :							Dict	    =	WavaVecaProcessor(feature_extractor=lowercase_		,    tokenizer=lowercase_					)
                     processor.save_pretrained(lowercase_					)

              _UpperCamelCase  :							Optional[Any]	    =	SEWDForCTC(lowercase_					)
       else:
              _UpperCamelCase  :							Tuple	    =	SEWDModel(lowercase_					)
              feature_extractor.save_pretrained(lowercase_					)

       recursively_load_weights(lowercase_		,    lowercase_		,    lowercase_					)

       hf_model.save_pretrained(lowercase_					)


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(
     '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 UpperCAmelCase      		=					parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )










"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								UpperCamelCase =						[]
								for img in imgs:
										UpperCamelCase ,					UpperCamelCase =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										UpperCamelCase =						np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										UpperCamelCase =						size * 1.0 / min(__a ,				__a		)
										if h < w:
												UpperCamelCase ,					UpperCamelCase =						size, scale * w
										else:
												UpperCamelCase ,					UpperCamelCase =						scale * h, size
										if max(__a ,				__a		) > self.max_size:
												UpperCamelCase =						self.max_size * 1.0 / max(__a ,				__a		)
												UpperCamelCase =						newh * scale
												UpperCamelCase =						neww * scale
										UpperCamelCase =						int(neww + 0.5		)
										UpperCamelCase =						int(newh + 0.5		)

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

								return torch.stack(__a		), torch.tensor(__a		)




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										UpperCamelCase =						torch.true_divide(__a ,				__a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		assert torch.isfinite(_SCREAMING_SNAKE_CASE     ).all(), "Box tensor contains infinite or NaN!"
		UpperCamelCase ,					UpperCamelCase =						box_size
		tensor[:, 0].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 1].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 2].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 3].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )






