import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_UpperCAmelCase            =		[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




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




		for attribute in key.split('.'     ):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								A_		:    List[str]         =				'lm_head'

				A_		:    List[str]         =				getattr(__lowercase						,__lowercase     )

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

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




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




		A_		:    List[str]         =				[]
		A_		:    str         =				fairseq_model.state_dict()

		A_		:    int         =				hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				A_		:    Dict         =				False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowercase						,__lowercase						,__lowercase						,__lowercase						,hf_model.config.feat_extract_norm == 'group'						,)
						A_		:    Union[str, Any]         =				True
				else:
						for key, mapped_key in MAPPING.items():
								A_		:    List[Any]         =				'unispeech.' + 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_		:    Optional[int]         =				True
										if "*" in mapped_key:
												A_		:    Optional[Any]         =				name.split(__lowercase     )[0].split('.'     )[-2]
												A_		:    Dict         =				mapped_key.replace('*'						,__lowercase     )
										if "weight_g" in name:
												A_		:    Dict         =				'weight_g'
										elif "weight_v" in name:
												A_		:    str         =				'weight_v'
										elif "bias" in name:
												A_		:    List[Any]         =				'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A_		:    Dict         =				'weight'
										else:
												A_		:    List[Any]         =				None
										set_recursively(__lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase     )
								continue
				if not is_used:
						unused_weights.append(__lowercase     )

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




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




		A_		:    Any         =				full_name.split('conv_layers.'     )[-1]
		A_		:    List[Any]         =				name.split('.'     )
		A_		:    Optional[int]         =				int(items[0]     )
		A_		:    Dict         =				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.'''
						)
						A_		:    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.'''
						)
						A_		:    Dict         =				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."
						)
						A_		:    List[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.'''
						)
						A_		:    Tuple         =				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						:			Optional[Any]						,__lowercase						:			Union[str, Any]						,__lowercase						:			Union[str, Any]=None						,__lowercase						:			Optional[Any]=None						,__lowercase						:			Optional[int]=True     ):
		'''simple docstring'''




		if config_path is not None:
				A_		:    List[str]         =				UniSpeechConfig.from_pretrained(__lowercase     )
		else:
				A_		:    Tuple         =				UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						A_		:    int         =				Dictionary.load_from_json(__lowercase     )

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

						# fairseq has the <pad> and <s> switched
						A_		:    List[Any]         =				42
						A_		:    Tuple         =				43
						with open(__lowercase						,'w'						,encoding='utf-8'     ) as vocab_handle:
								json.dump(__lowercase						,__lowercase     )
						A_		:    Optional[int]         =				WavaVecaPhonemeCTCTokenizer(
						    __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_		:    Union[str, Any]         =				True if config.feat_extract_norm == 'layer' else False
						A_		:    Optional[int]         =				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_		:    Tuple         =				UniSpeechForCTC(__lowercase     )
		else:
				A_		:    Tuple         =				UniSpeechForPreTraining(__lowercase     )

		if is_finetuned:
				A_		:    int         =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     ), 'w2v_path': checkpoint_path}     )
		else:
				A_		:    Tuple         =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

		A_		:    Optional[Any]         =				model[0].eval()

		recursively_load_weights(__lowercase						,__lowercase						,__lowercase     )

		hf_unispeech.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"""
					)
					_UpperCAmelCase            =		parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







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 )






