



'''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()
lowercase_   :					int        =     logging.get_logger(__name__)

lowercase_   :					Union[str, 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',
}
lowercase_   :					Tuple        =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]    ):
						lowercase   =				{}
						with open(__a			,	"""r"""    ) as file:
												for line_number, line in enumerate(__a    ):
																		lowercase   =				line.strip()
																		if line:
																								lowercase   =				line.split()
																								lowercase   =				line_number
																								lowercase   =				words[0]
																								lowercase   =				value
						return result







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Dict			,	lowercase_     :   Optional[Any]			,	lowercase_     :   List[str]			,	lowercase_     :   Dict			,	lowercase_     :   str    ):
						for attribute in key.split("""."""    ):
												lowercase   =				getattr(__a			,	__a    )

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

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

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

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







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[int]			,	lowercase_     :   int			,	lowercase_     :   Optional[int]			,	lowercase_     :   Optional[Any]			,	lowercase_     :   List[Any]    ):
						lowercase   =				None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__a    ):
																		lowercase   =				PARAM_MAPPING[full_name.split("""."""    )[-1]]
																		lowercase   =				"""param"""

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

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


lowercase_   :					Dict        =     {
    '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   SCREAMING_SNAKE_CASE					(					lowercase_     :   Dict			,	lowercase_     :   int			,	lowercase_     :   int=None			,	lowercase_     :   List[str]=None    ):
						lowercase   =				False
						for key, mapped_key in MAPPING.items():
												lowercase   =				"""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]:
																		lowercase   =				True
																		if "*" in mapped_key:
																								lowercase   =				name.split(__a    )[0].split("""."""    )[-2]
																								lowercase   =				mapped_key.replace("""*"""			,	__a    )
																		if "weight_g" in name:
																								lowercase   =				"""weight_g"""
																		elif "weight_v" in name:
																								lowercase   =				"""weight_v"""
																		elif "bias" in name:
																								lowercase   =				"""bias"""
																		elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowercase   =				"""weight"""
																		else:
																								lowercase   =				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   SCREAMING_SNAKE_CASE					(					lowercase_     :   Union[str, Any]			,	lowercase_     :   List[str]			,	lowercase_     :   Dict    ):
						lowercase   =				[]
						lowercase   =				fairseq_model.state_dict()

						lowercase   =				hf_model.wavaveca.feature_extractor

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

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







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   List[Any]			,	lowercase_     :   Optional[Any]			,	lowercase_     :   Union[str, Any]			,	lowercase_     :   Optional[int]			,	lowercase_     :   List[str]    ):
						lowercase   =				full_name.split("""conv_layers."""    )[-1]
						lowercase   =				name.split("""."""    )
						lowercase   =				int(items[0]    )
						lowercase   =				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."""    )
																		lowercase   =				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."""    )
																		lowercase   =				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."""    )
																		lowercase   =				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."""    )
																		lowercase   =				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   SCREAMING_SNAKE_CASE					(					lowercase_     :   str			,	lowercase_     :   str			,	lowercase_     :   Any=None			,	lowercase_     :   str=None			,	lowercase_     :   Any=True			,	lowercase_     :   Union[str, Any]=False    ):
						if config_path is not None:
												lowercase   =				WavaVecaConfig.from_pretrained(__a    )
						else:
												lowercase   =				WavaVecaConfig()

						if is_seq_class:
												lowercase   =				read_txt_into_dict(__a    )
												lowercase   =				idalabel
												lowercase   =				WavaVecaForSequenceClassification(__a    )
												lowercase   =				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:
																		lowercase   =				Dictionary.load(__a    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase   =				target_dict.pad_index
																		lowercase   =				target_dict.bos_index
																		lowercase   =				target_dict.eos_index
																		lowercase   =				len(target_dict.symbols    )
																		lowercase   =				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    )
																		lowercase   =				target_dict.indices

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

												lowercase   =				WavaVecaForCTC(__a    )
						else:
												lowercase   =				WavaVecaForPreTraining(__a    )

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

												lowercase  , lowercase  , lowercase   =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,	task=__a    )

						lowercase   =				model[0].eval()

						recursively_load_weights(__a			,	__a			,	not is_finetuned    )

						hf_wavavec.save_pretrained(__a    )


if __name__ == "__main__":
						lowercase_   :					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''',
						)
						lowercase_   :					Tuple        =     parser.parse_args()

						lowercase_   :					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,
						)






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


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




class       lowercase_					(lowercase__   ):
				snake_case	    =['pixel_values']





				def __init__(				self       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = PILImageResampling.BILINEAR       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = 1 / 255       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> None:
											super().__init__(**lowercase_)
											a__      =size if size is not None else {'shortest_edge': 256}
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =crop_size if crop_size is not None else {'height': 224, 'width': 224}
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_resize
											a__      =size
											a__      =resample
											a__      =do_center_crop
											a__      =crop_size
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_normalize
											a__      =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__      =image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = PILImageResampling.BICUBIC       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
											a__      =get_resize_output_image_size(lowercase_       ,  size=size['shortest_edge']       ,  default_to_square=lowercase_)
											return resize(lowercase_       ,  size=lowercase_       ,  resample=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""")
											return center_crop(lowercase_       ,  size=(size['height'], size['width'])       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_)  -> np.ndarray:
											return rescale(lowercase_       ,  scale=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											return normalize(lowercase_       ,  mean=lowercase_       ,  std=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = ChannelDimension.FIRST       ,  **lowercase_       ,  )  -> Tuple:
											a__      =do_resize if do_resize is not None else self.do_resize
											a__      =size if size is not None else self.size
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =resample if resample is not None else self.resample
											a__      =do_center_crop if do_center_crop is not None else self.do_center_crop
											a__      =crop_size if crop_size is not None else self.crop_size
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_rescale if do_rescale is not None else self.do_rescale
											a__      =rescale_factor if rescale_factor is not None else self.rescale_factor
											a__      =do_normalize if do_normalize is not None else self.do_normalize
											a__      =image_mean if image_mean is not None else self.image_mean
											a__      =image_std if image_std is not None else self.image_std

											a__      =make_list_of_images(lowercase_)

											if not valid_images(lowercase_):
																		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.')

											# All transformations expect numpy arrays.
											a__      =[to_numpy_array(lowercase_) for image in images]

											if do_resize:
																		a__      =[self.resize(image=lowercase_       ,  size=lowercase_       ,  resample=lowercase_) for image in images]

											if do_center_crop:
																		a__      =[self.center_crop(image=lowercase_       ,  size=lowercase_) for image in images]

											if do_rescale:
																		a__      =[self.rescale(image=lowercase_       ,  scale=lowercase_) for image in images]

											if do_normalize:
																		a__      =[self.normalize(image=lowercase_       ,  mean=lowercase_       ,  std=lowercase_) for image in images]

											a__      =[to_channel_dimension_format(lowercase_       ,  lowercase_) for image in images]

											a__      ={'pixel_values': images}
											return BatchFeature(data=lowercase_       ,  tensor_type=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> str:
											a__      =outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(lowercase_) != len(lowercase_):
																									raise ValueError(
																									    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

																		if is_torch_tensor(lowercase_):
																									a__      =target_sizes.numpy()

																		a__      =[]

																		for idx in range(len(lowercase_)):
																									a__      =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0)       ,  size=target_sizes[idx]       ,  mode='bilinear'       ,  align_corners=lowercase_)
																									a__      =resized_logits[0].argmax(dim=0)
																									semantic_segmentation.append(lowercase_)
											else:
																		a__      =logits.argmax(dim=1)
																		a__      =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

											return semantic_segmentation

