
"""simple docstring"""







import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    """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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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__        =		[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
		for attribute in key.split('.'		):
				__lowerCAmelCase      :						str						      =				getattr(_UpperCamelCase	,  _UpperCamelCase		)

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

		logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
		__lowerCAmelCase      :						List[str]						      =				[]
		__lowerCAmelCase      :						Optional[int]						      =				fairseq_model.state_dict()

		__lowerCAmelCase      :						Union[str, Any]						      =				hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				__lowerCAmelCase      :						Optional[Any]						      =				False
				if "conv_layers" in name:
						load_conv_layer(
						    _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  hf_model.config.feat_extract_norm == 'group'	,  )
						__lowerCAmelCase      :						Any						      =				True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
										__lowerCAmelCase      :						List[Any]						      =				True
										if "*" in mapped_key:
												__lowerCAmelCase      :						List[Any]						      =				name.split(_UpperCamelCase		)[0].split('.'		)[-2]
												__lowerCAmelCase      :						Tuple						      =				mapped_key.replace('*'	,  _UpperCamelCase		)
										if "weight_g" in name:
												__lowerCAmelCase      :						Optional[int]						      =				'weight_g'
										elif "weight_v" in name:
												__lowerCAmelCase      :						Union[str, Any]						      =				'weight_v'
										elif "bias" in name and "relative_attention_bias" not in name:
												__lowerCAmelCase      :						Union[str, Any]						      =				'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__lowerCAmelCase      :						List[str]						      =				'weight'
										else:
												__lowerCAmelCase      :						Optional[Any]						      =				None

										set_recursively(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
								continue
				if not is_used:
						unused_weights.append(_UpperCamelCase		)

		logger.warning(F"Unused weights: {unused_weights}"		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				full_name.split('conv_layers.'		)[-1]
		__lowerCAmelCase      :						List[str]						      =				name.split('.'		)
		__lowerCAmelCase      :						Dict						      =				int(items[0]		)
		__lowerCAmelCase      :						List[str]						      =				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      :						List[Any]						      =				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      :						Union[str, Any]						      =				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      :						str						      =				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(_UpperCamelCase		)
@torch.no_grad()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=None		):
		# load the pre-trained checkpoints
		__lowerCAmelCase      :						int						      =				torch.load(_UpperCamelCase		)
		__lowerCAmelCase      :						Union[str, Any]						      =				WavLMConfigOrig(checkpoint['cfg']		)
		__lowerCAmelCase      :						Any						      =				WavLMOrig(_UpperCamelCase		)
		model.load_state_dict(checkpoint['model']		)
		model.eval()

		if config_path is not None:
				__lowerCAmelCase      :						Union[str, Any]						      =				WavLMConfig.from_pretrained(_UpperCamelCase		)
		else:
				__lowerCAmelCase      :						List[str]						      =				WavLMConfig()

		__lowerCAmelCase      :						List[str]						      =				WavLMModel(_UpperCamelCase		)

		recursively_load_weights(_UpperCamelCase	,  _UpperCamelCase		)

		hf_wavlm.save_pretrained(_UpperCamelCase		)


if __name__ == "__main__":
							lowerCamelCase__        =		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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCamelCase__        =		parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""







# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    'en': 'Machine learning is great, isn\'t it?',
	    'ru': 'Машинное обучение - это здорово, не так ли?',
	    'de': 'Maschinelles Lernen ist großartig, oder?',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	__lowerCAmelCase      :						Union[str, Any]						      =				{
	    'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
	    'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
	    'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
	    'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
	}
	__lowerCAmelCase      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

	__lowerCAmelCase      :						Tuple						      =				F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
	os.makedirs(_UpperCamelCase	,  exist_ok=_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				os.path.join(_UpperCamelCase	,  'README.md'		)
	print(F"Generating {path}"		)
	with open(_UpperCamelCase	,  'w'	,  encoding='utf-8'		) as f:
		f.write(_UpperCamelCase		)


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__        =		model_name.split("""-""")
							lowerCamelCase__        =		model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)