





import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_a						=   logging.get_logger("transformers.models.encodec")

_a						=   {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
_a						=   {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
_a						=   {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
_a						=   {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
_a						=   {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
_a						=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_a						=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_a						=   []
_a						=   []





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   str:







	'''simple docstring'''







	for attribute in key.split('''.'''							):
		lowerCamelCase__              =	getattr(_A ,_A							)

	if weight_type is not None:
		lowerCamelCase__              =	getattr(_A ,_A							).shape
	else:
		lowerCamelCase__              =	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":
		lowerCamelCase__              =	value
	elif weight_type == "weight_g":
		lowerCamelCase__              =	value
	elif weight_type == "weight_v":
		lowerCamelCase__              =	value
	elif weight_type == "bias":
		lowerCamelCase__              =	value
	elif weight_type == "running_mean":
		lowerCamelCase__              =	value
	elif weight_type == "running_var":
		lowerCamelCase__              =	value
	elif weight_type == "num_batches_tracked":
		lowerCamelCase__              =	value
	elif weight_type == "weight_ih_l0":
		lowerCamelCase__              =	value
	elif weight_type == "weight_hh_l0":
		lowerCamelCase__              =	value
	elif weight_type == "bias_ih_l0":
		lowerCamelCase__              =	value
	elif weight_type == "bias_hh_l0":
		lowerCamelCase__              =	value
	elif weight_type == "weight_ih_l1":
		lowerCamelCase__              =	value
	elif weight_type == "weight_hh_l1":
		lowerCamelCase__              =	value
	elif weight_type == "bias_ih_l1":
		lowerCamelCase__              =	value
	elif weight_type == "bias_hh_l1":
		lowerCamelCase__              =	value
	else:
		lowerCamelCase__              =	value

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





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	for key in ignore_keys:
		if key.endswith('''.*'''							):
			if name.startswith(key[:-1]							):
				return True
		elif ".*." in key:
			lowerCamelCase__       ,			lowerCamelCase__              =	key.split('''.*.'''							)
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	[]

	if model_name == "encodec_24khz" or "encodec_32khz":
		lowerCamelCase__              =	MAPPING_24K
	elif model_name == "encodec_48khz":
		lowerCamelCase__              =	MAPPING_48K
	else:
		raise ValueError(F'Unsupported model: {model_name}'							)

	for name, value in orig_dict.items():
		if should_ignore(_A ,_A							):
			logger.info(F'{name} was ignored'							)
			continue

		lowerCamelCase__              =	False
		for key, mapped_key in MAPPING.items():
			if "*" in key:
				lowerCamelCase__       ,			lowerCamelCase__              =	key.split('''.*.'''							)
				if prefix in name and suffix in name:
					lowerCamelCase__              =	suffix

			if key in name:
				# HACK otherwise .embed gets initialized with .embed_avg too
				if key.endswith('''embed'''							) and name.endswith('''embed_avg'''							):
					continue

				lowerCamelCase__              =	True
				if "*" in mapped_key:
					lowerCamelCase__              =	name.split(_A							)[0].split('''.'''							)[-2]
					lowerCamelCase__              =	mapped_key.replace('''*''' ,_A							)
				if "weight_g" in name:
					lowerCamelCase__              =	'''weight_g'''
				elif "weight_v" in name:
					lowerCamelCase__              =	'''weight_v'''
				elif "weight_ih_l0" in name:
					lowerCamelCase__              =	'''weight_ih_l0'''
				elif "weight_hh_l0" in name:
					lowerCamelCase__              =	'''weight_hh_l0'''
				elif "bias_ih_l0" in name:
					lowerCamelCase__              =	'''bias_ih_l0'''
				elif "bias_hh_l0" in name:
					lowerCamelCase__              =	'''bias_hh_l0'''
				elif "weight_ih_l1" in name:
					lowerCamelCase__              =	'''weight_ih_l1'''
				elif "weight_hh_l1" in name:
					lowerCamelCase__              =	'''weight_hh_l1'''
				elif "bias_ih_l1" in name:
					lowerCamelCase__              =	'''bias_ih_l1'''
				elif "bias_hh_l1" in name:
					lowerCamelCase__              =	'''bias_hh_l1'''
				elif "bias" in name:
					lowerCamelCase__              =	'''bias'''
				elif "weight" in name:
					lowerCamelCase__              =	'''weight'''
				elif "running_mean" in name:
					lowerCamelCase__              =	'''running_mean'''
				elif "running_var" in name:
					lowerCamelCase__              =	'''running_var'''
				elif "num_batches_tracked" in name:
					lowerCamelCase__              =	'''num_batches_tracked'''
				else:
					lowerCamelCase__              =	None
				set_recursively(_A ,_A ,_A ,_A ,_A							)
			continue
		if not is_used:
			unused_weights.append(_A							)

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





@torch.no_grad()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,)       ->   Tuple:







	'''simple docstring'''







	if config_path is not None:
		lowerCamelCase__              =	EncodecConfig.from_pretrained(_A							)
	else:
		lowerCamelCase__              =	EncodecConfig()

	if model_name == "encodec_24khz":
		pass  # config is already correct
	elif model_name == "encodec_32khz":
		lowerCamelCase__              =	[8, 5, 4, 4]
		lowerCamelCase__              =	[2.2]
		lowerCamelCase__              =	64
		lowerCamelCase__              =	32000
		lowerCamelCase__              =	2048
		lowerCamelCase__              =	False
		lowerCamelCase__              =	False
		lowerCamelCase__              =	False
	elif model_name == "encodec_48khz":
		lowerCamelCase__              =	[8, 5, 4, 2]
		lowerCamelCase__              =	[3.0, 6.0, 1_2.0, 2_4.0]
		lowerCamelCase__              =	48000
		lowerCamelCase__              =	2
		lowerCamelCase__              =	False
		lowerCamelCase__              =	'''time_group_norm'''
		lowerCamelCase__              =	True
		lowerCamelCase__              =	1.0
		lowerCamelCase__              =	0.0_1
	else:
		raise ValueError(F'Unknown model name: {model_name}'							)

	lowerCamelCase__              =	EncodecModel(_A							)

	lowerCamelCase__              =	EncodecFeatureExtractor(
	    feature_size=config.audio_channels ,sampling_rate=config.sampling_rate ,chunk_length_s=config.chunk_length_s ,overlap=config.overlap ,)
	feature_extractor.save_pretrained(_A							)

	lowerCamelCase__              =	torch.load(_A							)
	if "best_state" in original_checkpoint:
		# we might have a training state saved, in which case discard the yaml results and just retain the weights
		lowerCamelCase__              =	original_checkpoint['''best_state''']
	recursively_load_weights(_A ,_A ,_A							)
	model.save_pretrained(_A							)

	if repo_id:
		print('''Pushing to the hub...'''							)
		feature_extractor.push_to_hub(_A							)
		model.push_to_hub(_A							)


if __name__ == "__main__":
			_a						=   argparse.ArgumentParser()
			parser.add_argument(
			    "--model",
			    default="encodec_24khz",
			    type=str,
			    help="The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.",
			)
			parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument(
			    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
			)

			_a						=   parser.parse_args()
			convert_checkpoint(
			    args.model,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.push_to_hub,
			)








# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''en''': '''Machine learning is great, isn\'t it?''',
	    '''ru''': '''Машинное обучение - это здорово, не так ли?''',
	    '''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	lowerCamelCase__              =	{
	    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	lowerCamelCase__              =	F'{src_lang}-{tgt_lang}'

	lowerCamelCase__              =	F'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
	model_card_dir.mkdir(parents=__snake_case ,exist_ok=__snake_case							)
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	print(F'Generating {path}'							)
	with open(__snake_case ,'''w''' ,encoding='''utf-8'''							) as f:
		f.write(__snake_case							)


# make sure we are under the root of the project
_a						=   Path(__file__).resolve().parent.parent.parent
_a						=   repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			_a						=   model_cards_dir / "allenai" / model_name
			write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



