
"""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"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,  4		)}
def    __lowerCAmelCase    ():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)
def    __lowerCAmelCase    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  help='How many observations. Defaults to all.'		)
	parser.add_argument('--fp16'	,  action='store_true'		)
	parser.add_argument('--dump-args'	,  action='store_true'	,  help='print the custom hparams with the results'		)
	parser.add_argument(
	    '--info'	,  nargs='?'	,  type=_UpperCamelCase	,  const=datetime_now()	,  help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )	,  )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	if args.reference_path is None and Path(args.score_path		).exists():
		warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."		)

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError('Can\'t mix --fp16 and --device cpu'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase      :						str						      =				calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.save_path		).readlines()]
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_UpperCamelCase		)]
	__lowerCAmelCase      :						dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
	scores.update(_UpperCamelCase		)

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

	if args.score_path is not None:
		json.dump(_UpperCamelCase	,  open(args.score_path	,  'w'		)		)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)