





'''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()
__lowerCamelCase : Tuple											=			logging.get_logger(__name__)

__lowerCamelCase : 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",
    "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",
}
__lowerCamelCase : Any											=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""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
																lowercase         = "lm_head"

								lowercase         = getattr(__snake_case		,							__snake_case							)

				if weight_type is not None:
								lowercase         = getattr(__snake_case		,							__snake_case							).shape
				else:
								lowercase         = 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":
								lowercase         = value
				elif weight_type == "weight_g":
								lowercase         = value
				elif weight_type == "weight_v":
								lowercase         = value
				elif weight_type == "bias":
								lowercase         = value
				else:
								lowercase         = value

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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []
				lowercase         = fairseq_model.state_dict()

				lowercase         = hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowercase         = False
								if "conv_layers" in name:
												load_conv_layer(
												    __snake_case		,							__snake_case		,							__snake_case		,							__snake_case		,							hf_model.config.feat_extract_norm == "group"		,							)
												lowercase         = True
								else:
												for key, mapped_key in MAPPING.items():
																lowercase         = "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]:
																				lowercase         = True
																				if "*" in mapped_key:
																								lowercase         = name.split(__snake_case							)[0].split("."							)[-2]
																								lowercase         = mapped_key.replace("*"		,							__snake_case							)
																				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
																				set_recursively(__snake_case		,							__snake_case		,							__snake_case		,							__snake_case		,							__snake_case		,							__snake_case							)
																continue
								if not is_used:
												unused_weights.append(__snake_case							)

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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				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:
												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.'
												)
												lowercase         = 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.'
												)
												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:
												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."
												)
												lowercase         = 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.'
												)
												lowercase         = value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
				else:
								unused_weights.append(__snake_case							)







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							lowerCAmelCase_=True							):

				"""simple docstring"""






				if config_path is not None:
								lowercase         = UniSpeechConfig.from_pretrained(__snake_case							)
				else:
								lowercase         = UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowercase         = Dictionary.load_from_json(__snake_case							)

												# 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(__snake_case		,							"vocab.json"							)
												if not os.path.isdir(__snake_case							):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__snake_case							)							)
																return
												os.makedirs(__snake_case		,							exist_ok=__snake_case							)
												lowercase         = target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowercase         = 42
												lowercase         = 43
												with open(__snake_case		,							"w"		,							encoding="utf-8"							) as vocab_handle:
																json.dump(__snake_case		,							__snake_case							)
												lowercase         = WavaVecaPhonemeCTCTokenizer(
												    __snake_case		,							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=__snake_case		,							)
												lowercase         = True if config.feat_extract_norm == "layer" else False
												lowercase         = WavaVecaFeatureExtractor(
												    feature_size=1		,							sampling_rate=1_6000		,							padding_value=0		,							do_normalize=__snake_case		,							return_attention_mask=__snake_case		,							)
												lowercase         = WavaVecaProcessor(feature_extractor=__snake_case		,							tokenizer=__snake_case							)
												processor.save_pretrained(__snake_case							)

								lowercase         = UniSpeechForCTC(__snake_case							)
				else:
								lowercase         = UniSpeechForPreTraining(__snake_case							)

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

				lowercase         = model[0].eval()

				recursively_load_weights(__snake_case		,							__snake_case		,							__snake_case							)

				hf_unispeech.save_pretrained(__snake_case							)


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







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




