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            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """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            =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_		:    int         =				{}
		with open(__lowercase						,'r'     ) as file:
				for line_number, line in enumerate(__lowercase     ):
						A_		:    Any         =				line.strip()
						if line:
								A_		:    List[Any]         =				line.split()
								A_		:    int         =				line_number
								A_		:    Dict         =				words[0]
								A_		:    Optional[int]         =				value
		return result




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			int						,__lowercase						:			Tuple						,__lowercase						:			Dict						,__lowercase						:			List[str]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    Any         =				getattr(__lowercase						,__lowercase     )

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

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

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

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




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Optional[Any]						,__lowercase						:			Dict						,__lowercase						:			str						,__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    List[Any]         =				None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(__lowercase     ):
						A_		:    List[Any]         =				PARAM_MAPPING[full_name.split('.'     )[-1]]
						A_		:    Optional[int]         =				'param'

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

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


_UpperCAmelCase            =		{
    """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     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			List[Any]						,__lowercase						:			Tuple=None						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			List[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				[]
		A_		:    Union[str, Any]         =				fairseq_model.state_dict()

		A_		:    Optional[int]         =				hf_model.wavaveca.feature_extractor

		for name, value in fairseq_dict.items():
				A_		:    int         =				False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowercase						,__lowercase						,__lowercase						,__lowercase						,hf_model.config.feat_extract_norm == 'group'						,)
						A_		:    Tuple         =				True
				else:
						A_		:    Dict         =				load_wavaveca_layer(__lowercase						,__lowercase						,__lowercase     )
				if not is_used:
						unused_weights.append(__lowercase     )

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




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Tuple						,__lowercase						:			Optional[Any]						,__lowercase						:			Optional[int]						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Dict         =				full_name.split('conv_layers.'     )[-1]
		A_		:    Optional[Any]         =				name.split('.'     )
		A_		:    Optional[Any]         =				int(items[0]     )
		A_		:    Dict         =				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.'''     )
						A_		:    Union[str, 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.'''     )
						A_		:    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:
						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.'''     )
						A_		:    Optional[Any]         =				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.'''     )
						A_		:    Any         =				value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__lowercase     )




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			str						,__lowercase						:			str=None						,__lowercase						:			Optional[int]=None						,__lowercase						:			int=True						,__lowercase						:			List[Any]=False     ):
		'''simple docstring'''




		if config_path is not None:
				A_		:    Optional[Any]         =				WavaVecaConfig.from_pretrained(__lowercase     )
		else:
				A_		:    Optional[Any]         =				WavaVecaConfig()

		if is_seq_class:
				A_		:    List[Any]         =				read_txt_into_dict(__lowercase     )
				A_		:    str         =				idalabel
				A_		:    Dict         =				WavaVecaForSequenceClassification(__lowercase     )
				A_		:    List[str]         =				WavaVecaFeatureExtractor(
				    feature_size=1						,sampling_rate=1_60_00						,padding_value=0						,do_normalize=__lowercase						,return_attention_mask=__lowercase						,)
				feature_extractor.save_pretrained(__lowercase     )

		elif is_finetuned:
				if dict_path:
						A_		:    List[Any]         =				Dictionary.load(__lowercase     )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						A_		:    Optional[int]         =				target_dict.pad_index
						A_		:    Any         =				target_dict.bos_index
						A_		:    List[Any]         =				target_dict.eos_index
						A_		:    Tuple         =				len(target_dict.symbols     )
						A_		:    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     )
						A_		:    Optional[int]         =				target_dict.indices

						# fairseq has the <pad> and <s> switched
						A_		:    Any         =				0
						A_		:    Dict         =				1
						with open(__lowercase						,'w'						,encoding='utf-8'     ) as vocab_handle:
								json.dump(__lowercase						,__lowercase     )
						A_		:    Optional[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						,)
						A_		:    Optional[int]         =				True if config.feat_extract_norm == 'layer' else False
						A_		:    Union[str, Any]         =				WavaVecaFeatureExtractor(
						    feature_size=1						,sampling_rate=1_60_00						,padding_value=0						,do_normalize=__lowercase						,return_attention_mask=__lowercase						,)
						A_		:    List[str]         =				WavaVecaProcessor(feature_extractor=__lowercase						,tokenizer=__lowercase     )
						processor.save_pretrained(__lowercase     )

				A_		:    Any         =				WavaVecaForCTC(__lowercase     )
		else:
				A_		:    List[str]         =				WavaVecaForPreTraining(__lowercase     )

		if is_finetuned or is_seq_class:
				A_       ,      A_       ,      A_		:    Optional[Any]         =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
		else:
				A_		:    Dict         =				argparse.Namespace(task='audio_pretraining'     )
				A_		:    Union[str, Any]         =				fairseq.tasks.setup_task(__lowercase     )

				A_       ,      A_       ,      A_		:    Any         =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,task=__lowercase     )

		A_		:    List[str]         =				model[0].eval()

		recursively_load_weights(__lowercase						,__lowercase						,not is_finetuned     )

		hf_wavavec.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(
	    """--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            =		parser.parse_args()

	_UpperCAmelCase            =		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,
	)







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						warnings.warn(
						    'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use DonutImageProcessor instead.'		,     lowercase		,     )
						super().__init__(*lowercase		,     **lowercase )






