

"""simple docstring"""




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()
a		=logging.get_logger(__name__)

a		={
    """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""",
}
a		=[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	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
				__lowerCamelCase    :    Tuple 	=			'lm_head'

		__lowerCamelCase    :    Dict 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      )

	if weight_type is not None:
		__lowerCamelCase    :    int 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      ).shape
	else:
		__lowerCamelCase    :    Union[str, Any] 	=			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":
		__lowerCamelCase    :    Any 	=			value
	elif weight_type == "weight_g":
		__lowerCamelCase    :    Optional[Any] 	=			value
	elif weight_type == "weight_v":
		__lowerCamelCase    :    Dict 	=			value
	elif weight_type == "bias":
		__lowerCamelCase    :    List[Any] 	=			value
	else:
		__lowerCamelCase    :    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__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    str 	=			[]
	__lowerCamelCase    :    Any 	=			fairseq_model.state_dict()

	__lowerCamelCase    :    List[Any] 	=			hf_model.unispeech.feature_extractor

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

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






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






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=True      )  ->					str:
	if config_path is not None:
		__lowerCamelCase    :    int 	=			UniSpeechConfig.from_pretrained(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    Tuple 	=			UniSpeechConfig()

	if is_finetuned:
		if dict_path:
			__lowerCamelCase    :    Tuple 	=			Dictionary.load_from_json(lowerCamelCase__      )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__lowerCamelCase    :    str 	=			target_dict.pad_index
			__lowerCamelCase    :    int 	=			target_dict.bos_index
			__lowerCamelCase    :    int 	=			target_dict.eos_index
			__lowerCamelCase    :    Dict 	=			len(target_dict.symbols      )
			__lowerCamelCase    :    Optional[Any] 	=			os.path.join(lowerCamelCase__     ,			'vocab.json'      )
			if not os.path.isdir(lowerCamelCase__      ):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCamelCase__      )      )
				return
			os.makedirs(lowerCamelCase__     ,			exist_ok=lowerCamelCase__      )
			__lowerCamelCase    :    Optional[int] 	=			target_dict.indices

			# fairseq has the <pad> and <s> switched
			__lowerCamelCase    :    Dict 	=			4_2
			__lowerCamelCase    :    str 	=			4_3
			with open(lowerCamelCase__     ,			'w'     ,			encoding='utf-8'      ) as vocab_handle:
				json.dump(lowerCamelCase__     ,			lowerCamelCase__      )
			__lowerCamelCase    :    Tuple 	=			WavaVecaPhonemeCTCTokenizer(
			    lowerCamelCase__     ,			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=lowerCamelCase__     ,			)
			__lowerCamelCase    :    Tuple 	=			True if config.feat_extract_norm == 'layer' else False
			__lowerCamelCase    :    str 	=			WavaVecaFeatureExtractor(
			    feature_size=1     ,			sampling_rate=1_6_0_0_0     ,			padding_value=0     ,			do_normalize=lowerCamelCase__     ,			return_attention_mask=lowerCamelCase__     ,			)
			__lowerCamelCase    :    List[str] 	=			WavaVecaProcessor(feature_extractor=lowerCamelCase__     ,			tokenizer=lowerCamelCase__      )
			processor.save_pretrained(lowerCamelCase__      )

		__lowerCamelCase    :    Dict 	=			UniSpeechForCTC(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    Tuple 	=			UniSpeechForPreTraining(lowerCamelCase__      )

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

	__lowerCamelCase    :    List[Any] 	=			model[0].eval()

	recursively_load_weights(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	hf_unispeech.save_pretrained(lowerCamelCase__      )


if __name__ == "__main__":
					a		=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"""
					)
					a		=parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		=logging.get_logger(__name__)

a		={
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    Union[str, Any] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    str 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Optional[int] 	=			layer_norm_eps
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    Union[str, Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			qkv_bias




