






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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase :      Any										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """post_extract_proj""": """feature_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.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    (			snake_case_ :							Optional[int]					,  snake_case_ :							Optional[Any]					,  snake_case_ :							Optional[Any]					,  snake_case_ :							str					,  snake_case_ :							List[Any]       ):
	for attribute in key.split("."       ):
		snake_case__			:				Tuple       =  getattr(snake_case_					,  snake_case_       )

	if weight_type is not None:
		snake_case__			:				Tuple       =  getattr(snake_case_					,  snake_case_       ).shape
	else:
		snake_case__			:				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":
		snake_case__			:				List[Any]       =  value
	elif weight_type == "weight_g":
		snake_case__			:				List[str]       =  value
	elif weight_type == "weight_v":
		snake_case__			:				Any       =  value
	elif weight_type == "bias":
		snake_case__			:				List[Any]       =  value
	else:
		snake_case__			:				Union[str, Any]       =  value

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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Tuple					,  snake_case_ :							Optional[int]       ):
	snake_case__			:				Union[str, Any]       =  []
	snake_case__			:				Optional[Any]       =  fairseq_model.state_dict()

	snake_case__			:				Union[str, Any]       =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case__			:				List[str]       =  False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_					,  snake_case_					,  snake_case_					,  snake_case_					,  hf_model.config.feat_extract_norm == "group"					,  )
			snake_case__			:				Dict       =  True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case__			:				Optional[Any]       =  "sew." + 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]:
					snake_case__			:				Optional[int]       =  True
					if "*" in mapped_key:
						snake_case__			:				List[str]       =  name.split(snake_case_       )[0].split("."       )[-2]
						snake_case__			:				str       =  mapped_key.replace("*"					,  snake_case_       )
					if "weight_g" in name:
						snake_case__			:				Union[str, Any]       =  "weight_g"
					elif "weight_v" in name:
						snake_case__			:				List[Any]       =  "weight_v"
					elif "weight" in name:
						snake_case__			:				Union[str, Any]       =  "weight"
					elif "bias" in name:
						snake_case__			:				Tuple       =  "bias"
					else:
						snake_case__			:				Optional[int]       =  None
					set_recursively(snake_case_					,  snake_case_					,  snake_case_					,  snake_case_					,  snake_case_       )
				continue
		if not is_used:
			unused_weights.append(snake_case_       )

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







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]					,  snake_case_ :							Dict       ):
	snake_case__			:				Optional[Any]       =  SEWConfig()
	if is_finetuned:
		snake_case__			:				Union[str, Any]       =  model.wav_encoder.wav_model.cfg
	else:
		snake_case__			:				Dict       =  model.cfg

	snake_case__			:				Tuple       =  fs_config.conv_bias
	snake_case__			:				Tuple       =  eval(fs_config.conv_feature_layers       )
	snake_case__			:				int       =  [x[0] for x in conv_layers]
	snake_case__			:				int       =  [x[1] for x in conv_layers]
	snake_case__			:				List[Any]       =  [x[2] for x in conv_layers]
	snake_case__			:				List[Any]       =  "gelu"
	snake_case__			:				List[Any]       =  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
	snake_case__			:				List[Any]       =  0.0
	snake_case__			:				Any       =  fs_config.activation_fn.name
	snake_case__			:				int       =  fs_config.encoder_embed_dim
	snake_case__			:				Dict       =  0.02
	snake_case__			:				Optional[int]       =  fs_config.encoder_ffn_embed_dim
	snake_case__			:				Optional[Any]       =  1E-5
	snake_case__			:				Optional[int]       =  fs_config.encoder_layerdrop
	snake_case__			:				int       =  fs_config.encoder_attention_heads
	snake_case__			:				Any       =  fs_config.conv_pos_groups
	snake_case__			:				str       =  fs_config.conv_pos
	snake_case__			:				Tuple       =  len(snake_case_       )
	snake_case__			:				Tuple       =  fs_config.encoder_layers
	snake_case__			:				Any       =  fs_config.squeeze_factor

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

	snake_case__			:				List[str]       =  "Wav2Vec2FeatureExtractor"
	snake_case__			:				Tuple       =  "Wav2Vec2CTCTokenizer"

	return config







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str					,  snake_case_ :							Optional[Any]					,  snake_case_ :							Dict=None					,  snake_case_ :							Tuple=None					,  snake_case_ :							List[str]=True       ):

	if is_finetuned:
		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__,						snake_case__,						snake_case__			:				Optional[int]       =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

	if config_path is not None:
		snake_case__			:				Optional[int]       =  SEWConfig.from_pretrained(snake_case_       )
	else:
		snake_case__			:				Dict       =  convert_config(model[0]					,  snake_case_       )
	snake_case__			:				Optional[Any]       =  model[0].eval()

	snake_case__			:				str       =  True if config.feat_extract_norm == "layer" else False
	snake_case__			:				Union[str, Any]       =  WavaVecaFeatureExtractor(
	    feature_size=1					,  sampling_rate=16000					,  padding_value=0					,  do_normalize=snake_case_					,  return_attention_mask=snake_case_					,  )

	if is_finetuned:
		if dict_path:
			snake_case__			:				Dict       =  Dictionary.load(snake_case_       )

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

		snake_case__			:				Union[str, Any]       =  SEWForCTC(snake_case_       )
	else:
		snake_case__			:				Union[str, Any]       =  SEWModel(snake_case_       )
		feature_extractor.save_pretrained(snake_case_       )

	recursively_load_weights(snake_case_					,  snake_case_					,  snake_case_       )

	hf_model.save_pretrained(snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      str										=				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"""
	)
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__lowerCamelCase :      int										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Any       ):
	snake_case__			:				List[str]       =  b.T
	snake_case__			:				Union[str, Any]       =  np.sum(np.square(snake_case_       )					,  axis=1       )
	snake_case__			:				Dict       =  np.sum(np.square(snake_case_       )					,  axis=0       )
	snake_case__			:				Dict       =  np.matmul(snake_case_					,  snake_case_       )
	snake_case__			:				Any       =  aa[:, None] - 2 * ab + ba[None, :]
	return d







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]					,  snake_case_ :							Tuple       ):
	snake_case__			:				Tuple       =  x.reshape(-1					,  3       )
	snake_case__			:				int       =  squared_euclidean_distance(snake_case_					,  snake_case_       )
	return np.argmin(snake_case_					,  axis=1       )





class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  str							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  bool = True							,		__A					:  bool = True							,		**__A					:  Union[str, Any]							,		):
						super().__init__(**__A	)
						snake_case__			:				Optional[int]       =  size if size is not None else {"height": 2_5_6, "width": 2_5_6}
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						snake_case__			:				Any       =  np.array(__A	) if clusters is not None else None
						snake_case__			:				Optional[Any]       =  do_resize
						snake_case__			:				Any       =  size
						snake_case__			:				List[Any]       =  resample
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  do_color_quantize



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  int							,		):
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						if "height" not in size or "width" not in size:
							raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''	)
						return resize(
						    __A							,		size=(size["height"], size["width"])							,		resample=__A							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		):
						snake_case__			:				List[str]       =  rescale(image=__A							,		scale=1 / 1_2_7.5							,		data_format=__A	)
						snake_case__			:				List[Any]       =  image - 1
						return image






					def 	_lowercase					( self					:  Dict							,		__A					:  ImageInput							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = None							,		__A					:  bool = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  Optional[Union[str, TensorType]] = None							,		__A					:  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,		**__A					:  Optional[int]							,		):
						snake_case__			:				Any       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				Union[str, Any]       =  size if size is not None else self.size
						snake_case__			:				Union[str, Any]       =  get_size_dict(__A	)
						snake_case__			:				Optional[Any]       =  resample if resample is not None else self.resample
						snake_case__			:				Tuple       =  do_normalize if do_normalize is not None else self.do_normalize
						snake_case__			:				Optional[Any]       =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						snake_case__			:				Union[str, Any]       =  clusters if clusters is not None else self.clusters
						snake_case__			:				Union[str, Any]       =  np.array(__A	)

						snake_case__			:				Any       =  make_list_of_images(__A	)

						if not valid_images(__A	):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."	)

						if do_resize and size is None or resample is None:
							raise ValueError("Size and resample must be specified if do_resize is True."	)

						if do_color_quantize and clusters is None:
							raise ValueError("Clusters must be specified if do_color_quantize is True."	)

						# All transformations expect numpy arrays.
						snake_case__			:				Optional[Any]       =  [to_numpy_array(__A	) for image in images]

						if do_resize:
							snake_case__			:				List[str]       =  [self.resize(image=__A							,		size=__A							,		resample=__A	) for image in images]

						if do_normalize:
							snake_case__			:				Union[str, Any]       =  [self.normalize(image=__A	) for image in images]

						if do_color_quantize:
							snake_case__			:				int       =  [to_channel_dimension_format(__A							,		ChannelDimension.LAST	) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							snake_case__			:				int       =  np.array(__A	)
							snake_case__			:				Dict       =  color_quantize(__A							,		__A	).reshape(images.shape[:-1]	)

							# flatten to (batch_size, height*width)
							snake_case__			:				str       =  images.shape[0]
							snake_case__			:				str       =  images.reshape(__A							,		-1	)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							snake_case__			:				Union[str, Any]       =  list(__A	)
						else:
							snake_case__			:				Any       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				Optional[int]       =  {"input_ids": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)



