






'''simple docstring'''







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

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

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

						if weight_type is not None and weight_type != "param":
												lowerCamelCase__								=	getattr(A__ ,     A__    ).shape
						elif weight_type is not None and weight_type == "param":
												lowerCamelCase__								=	hf_pointer
												for attribute in hf_param_name.split("""."""    ):
																		lowerCamelCase__								=	getattr(A__ ,     A__    )
												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(A__ ,     A__    )
												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		:      Tuple ,     __lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      Union[str, Any]    ):
						lowerCamelCase__								=	None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(A__    ):
																		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     :   int	      =		{
    '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		:      List[str] ,     __lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      List[Any]=None ,     __lowerCAmelCase		:      Optional[int]=None    ):
						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(A__    )[0].split("""."""    )[-2]
																								lowerCamelCase__								=	mapped_key.replace("""*""" ,     A__    )
																		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(A__ ,     A__ ,     A__ ,     A__ ,     A__    )
																		else:
																								set_recursively(A__ ,     A__ ,     A__ ,     A__ ,     A__    )
																		return is_used
						return is_used
def     A__      (					__lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      List[Any]    ):
						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(
																		    A__ ,     A__ ,     A__ ,     A__ ,     hf_model.config.feat_extract_norm == """group""" ,     )
																		lowerCamelCase__								=	True
												else:
																		lowerCamelCase__								=	load_wavaveca_layer(A__ ,     A__ ,     A__    )
												if not is_used:
																		unused_weights.append(A__    )

						logger.warning(F'''Unused weights: {unused_weights}'''    )
def     A__      (					__lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      int ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      int    ):
						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(A__    )
@torch.no_grad()
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Optional[int]=None ,     __lowerCAmelCase		:      Tuple=None ,     __lowerCAmelCase		:      str=True ,     __lowerCAmelCase		:      List[Any]=False    ):
						if config_path is not None:
												lowerCamelCase__								=	WavaVecaConfig.from_pretrained(A__    )
						else:
												lowerCamelCase__								=	WavaVecaConfig()

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

						elif is_finetuned:
												if dict_path:
																		lowerCamelCase__								=	Dictionary.load(A__    )

																		# 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(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__    )
																		lowerCamelCase__								=	target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		lowerCamelCase__								=	0
																		lowerCamelCase__								=	1
																		with open(A__ ,     """w""" ,     encoding="""utf-8"""    ) as vocab_handle:
																								json.dump(A__ ,     A__    )
																		lowerCamelCase__								=	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__ ,     )
																		lowerCamelCase__								=	True if config.feat_extract_norm == """layer""" else False
																		lowerCamelCase__								=	WavaVecaFeatureExtractor(
																		    feature_size=1 ,     sampling_rate=1_6000 ,     padding_value=0 ,     do_normalize=A__ ,     return_attention_mask=A__ ,     )
																		lowerCamelCase__								=	WavaVecaProcessor(feature_extractor=A__ ,     tokenizer=A__    )
																		processor.save_pretrained(A__    )

												lowerCamelCase__								=	WavaVecaForCTC(A__    )
						else:
												lowerCamelCase__								=	WavaVecaForPreTraining(A__    )

						if is_finetuned or is_seq_class:
												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(A__    )

												lowerCamelCase__								=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=A__    )

						lowerCamelCase__								=	model[0].eval()

						recursively_load_weights(A__ ,     A__ ,     not is_finetuned    )

						hf_wavavec.save_pretrained(A__    )


if __name__ == "__main__":
							UpperCamelCase     :   List[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(
							    '--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     :   Tuple	      =		parser.parse_args()

							UpperCamelCase     :   Dict	      =		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,
							)





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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__				:	Tuple					       =					logging.get_logger(__name__)


if is_vision_available():
      import PIL





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["pixel_values"]



       def __init__(						self				:				List[str]		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				bool = True		,				__a				:				Union[int, float] = 1 / 255		,				__a				:				bool = True		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = True		,				**__a				:				str		,				)					->None:
          super().__init__(**__a							)
          lowerCamelCase_						:   Any		= size if size is not None else {"""shortest_edge""": 224}
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          lowerCamelCase_						:   Any		= get_size_dict(__a		,				default_to_square=__a		,				param_name="""crop_size"""							)

          lowerCamelCase_						:   List[Any]		= do_resize
          lowerCamelCase_						:   str		= size
          lowerCamelCase_						:   Any		= resample
          lowerCamelCase_						:   List[Any]		= do_center_crop
          lowerCamelCase_						:   Any		= crop_size
          lowerCamelCase_						:   List[Any]		= do_rescale
          lowerCamelCase_						:   Tuple		= rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize
          lowerCamelCase_						:   Any		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowerCamelCase_						:   int		= image_std if image_std is not None else OPENAI_CLIP_STD
          lowerCamelCase_						:   Tuple		= do_convert_rgb



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->np.ndarray:
          lowerCamelCase_						:   Optional[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          if "shortest_edge" not in size:
             raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
          lowerCamelCase_						:   Dict		= get_resize_output_image_size(__a		,				size=size["""shortest_edge"""]		,				default_to_square=__a							)
          return resize(__a		,				size=__a		,				resample=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[Any]		,				)					->np.ndarray:
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a							)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
          return center_crop(__a		,				size=(size["""height"""], size["""width"""])		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				np.ndarray		,				__a				:				Union[int, float]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->Dict:
          return rescale(__a		,				scale=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				np.ndarray		,				__a				:				Union[float, List[float]]		,				__a				:				Union[float, List[float]]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[str]		,				)					->np.ndarray:
          return normalize(__a		,				mean=__a		,				std=__a		,				data_format=__a		,				**__a							)




       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				ImageInput		,				__a				:				bool = None		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = None		,				__a				:				bool = None		,				__a				:				int = None		,				__a				:				bool = None		,				__a				:				float = None		,				__a				:				bool = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = None		,				__a				:				Optional[Union[str, TensorType]] = None		,				__a				:				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**__a				:				List[str]		,				)					->PIL.Image.Image:
          lowerCamelCase_						:   List[Any]		= do_resize if do_resize is not None else self.do_resize
          lowerCamelCase_						:   Any		= size if size is not None else self.size
          lowerCamelCase_						:   str		= get_size_dict(__a		,				param_name="""size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= resample if resample is not None else self.resample
          lowerCamelCase_						:   List[str]		= do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase_						:   List[Any]		= crop_size if crop_size is not None else self.crop_size
          lowerCamelCase_						:   Optional[int]		= get_size_dict(__a		,				param_name="""crop_size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[Any]		= do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase_						:   List[str]		= rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize if do_normalize is not None else self.do_normalize
          lowerCamelCase_						:   Dict		= image_mean if image_mean is not None else self.image_mean
          lowerCamelCase_						:   Tuple		= image_std if image_std is not None else self.image_std
          lowerCamelCase_						:   Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowerCamelCase_						:   Union[str, 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:
             raise ValueError("""Size must be specified if do_resize is True."""							)

          if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

          if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             lowerCamelCase_						:   str		= [convert_to_rgb(__a							) for image in images]

          # All transformations expect numpy arrays.
          lowerCamelCase_						:   str		= [to_numpy_array(__a							) for image in images]

          if do_resize:
             lowerCamelCase_						:   int		= [self.resize(image=__a		,				size=__a		,				resample=__a							) for image in images]

          if do_center_crop:
             lowerCamelCase_						:   Dict		= [self.center_crop(image=__a		,				size=__a							) for image in images]

          if do_rescale:
             lowerCamelCase_						:   List[str]		= [self.rescale(image=__a		,				scale=__a							) for image in images]

          if do_normalize:
             lowerCamelCase_						:   Tuple		= [self.normalize(image=__a		,				mean=__a		,				std=__a							) for image in images]

          lowerCamelCase_						:   Tuple		= [to_channel_dimension_format(__a		,				__a							) for image in images]

          lowerCamelCase_						:   Tuple		= {"""pixel_values""": images}
          return BatchFeature(data=__a		,				tensor_type=__a							)





