
'''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()
lowercase					:							Dict   =      logging.get_logger(__name__)

lowercase					:							List[str]   =      {
    "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",
}
lowercase					:							int   =      [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Any:
	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
				_snake_case     		=	'lm_head'

		_snake_case     		=	getattr(__A    ,       __A			)

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

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[int]:
	_snake_case     		=	[]
	_snake_case     		=	fairseq_model.state_dict()

	_snake_case     		=	hf_model.unispeech.feature_extractor

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

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			List[str]:
	_snake_case     		=	full_name.split('conv_layers.'			)[-1]
	_snake_case     		=	name.split('.'			)
	_snake_case     		=	int(items[0]			)
	_snake_case     		=	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.'
			)
			_snake_case     		=	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.'
			)
			_snake_case     		=	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."
			)
			_snake_case     		=	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.'
			)
			_snake_case     		=	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__			(     __A    ,       __A    ,       __A=None    ,       __A=None    ,       __A=True			) ->			Dict:
	if config_path is not None:
		_snake_case     		=	UniSpeechConfig.from_pretrained(__A			)
	else:
		_snake_case     		=	UniSpeechConfig()

	if is_finetuned:
		if dict_path:
			_snake_case     		=	Dictionary.load_from_json(__A			)

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

			# fairseq has the <pad> and <s> switched
			_snake_case     		=	42
			_snake_case     		=	43
			with open(__A    ,       'w'    ,       encoding='utf-8'			) as vocab_handle:
				json.dump(__A    ,       __A			)
			_snake_case     		=	WavaVecaPhonemeCTCTokenizer(
			    __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    ,       )
			_snake_case     		=	True if config.feat_extract_norm == 'layer' else False
			_snake_case     		=	WavaVecaFeatureExtractor(
			    feature_size=1    ,       sampling_rate=16_000    ,       padding_value=0    ,       do_normalize=__A    ,       return_attention_mask=__A    ,       )
			_snake_case     		=	WavaVecaProcessor(feature_extractor=__A    ,       tokenizer=__A			)
			processor.save_pretrained(__A			)

		_snake_case     		=	UniSpeechForCTC(__A			)
	else:
		_snake_case     		=	UniSpeechForPreTraining(__A			)

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

	_snake_case     		=	model[0].eval()

	recursively_load_weights(__A    ,       __A    ,       __A			)

	hf_unispeech.save_pretrained(__A			)


if __name__ == "__main__":
					lowercase					:							Dict   =      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"
					)
					lowercase					:							Tuple   =      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 typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowercase					:							Optional[Any]   =      logging.get_logger(__name__)

lowercase					:							str   =      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase					:							Optional[Any]   =      {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase					:							List[Any]   =      {
    "facebook/blenderbot_small-90M": 512,
}


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= VOCAB_FILES_NAMES
							__lowercase   	= PRETRAINED_VOCAB_FILES_MAP
							__lowercase   	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase   	= BlenderbotSmallTokenizer


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_=False					,     lowerCAmelCase_=True					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(
								    ByteLevelBPETokenizer(
								        vocab=lowerCAmelCase_					,     merges=lowerCAmelCase_					,     add_prefix_space=lowerCAmelCase_					,     trim_offsets=lowerCAmelCase_					,     )					,     bos_token=lowerCAmelCase_					,     eos_token=lowerCAmelCase_					,     unk_token=lowerCAmelCase_					,     **lowerCAmelCase_					,     )
								_snake_case     		=	add_prefix_space


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
									return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	[self.sep_token_id]
								_snake_case     		=	[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]

