




"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase							:			Any     							=							logging.get_logger(__name__)

lowerCAmelCase							:			int     							=							{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase							:			Tuple     							=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def     a__ (				snake_case__				)						->					str:
			lowerCamelCase			      =					{}
			with open(lowercase_       , """r"""				) as file:
						for line_number, line in enumerate(lowercase_				):
									lowerCamelCase			      =					line.strip()
									if line:
												lowerCamelCase			      =					line.split()
												lowerCamelCase			      =					line_number
												lowerCamelCase			      =					words[0]
												lowerCamelCase			      =					value
			return result



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					str:
			for attribute in key.split("""."""				):
						lowerCamelCase			      =					getattr(lowercase_       , lowercase_				)

			lowerCamelCase			      =					None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowercase_				):
									lowerCamelCase			      =					PARAM_MAPPING[full_name.split("""."""				)[-1]]
									lowerCamelCase			      =					"""param"""

			if weight_type is not None and weight_type != "param":
						lowerCamelCase			      =					getattr(lowercase_       , lowercase_				).shape
			elif weight_type is not None and weight_type == "param":
						lowerCamelCase			      =					hf_pointer
						for attribute in hf_param_name.split("""."""				):
									lowerCamelCase			      =					getattr(lowercase_       , lowercase_				)
						lowerCamelCase			      =					shape_pointer.shape

						# let's reduce dimension
						lowerCamelCase			      =					value[0]
			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 == "param":
						for attribute in hf_param_name.split("""."""				):
									lowerCamelCase			      =					getattr(lowercase_       , lowercase_				)
						lowerCamelCase			      =					value
			else:
						lowerCamelCase			      =					value

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



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					List[str]:
			lowerCamelCase			      =					None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowercase_				):
									lowerCamelCase			      =					PARAM_MAPPING[full_name.split("""."""				)[-1]]
									lowerCamelCase			      =					"""param"""

			if weight_type is not None and weight_type != "param":
						lowerCamelCase			      =					""".""".join([key, weight_type]				)
			elif weight_type is not None and weight_type == "param":
						lowerCamelCase			      =					""".""".join([key, hf_param_name]				)
			else:
						lowerCamelCase			      =					key

			lowerCamelCase			      =					value if """lm_head""" in full_key else value[0]


lowerCAmelCase							:			Any     							=							{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}



def     a__ (				snake_case__       , snake_case__       , snake_case__=None       , snake_case__=None				)						->					str:
			lowerCamelCase			      =					False
			for key, mapped_key in MAPPING.items():
						lowerCamelCase			      =					"""wav2vec2.""" + 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]:
									lowerCamelCase			      =					True
									if "*" in mapped_key:
												lowerCamelCase			      =					name.split(lowercase_				)[0].split("""."""				)[-2]
												lowerCamelCase			      =					mapped_key.replace("""*"""       , lowercase_				)
									if "weight_g" in name:
												lowerCamelCase			      =					"""weight_g"""
									elif "weight_v" in name:
												lowerCamelCase			      =					"""weight_v"""
									elif "bias" in name:
												lowerCamelCase			      =					"""bias"""
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCamelCase			      =					"""weight"""
									else:
												lowerCamelCase			      =					None
									if hf_dict is not None:
												rename_dict(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_				)
									else:
												set_recursively(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_				)
									return is_used
			return is_used



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					[]
			lowerCamelCase			      =					fairseq_model.state_dict()

			lowerCamelCase			      =					hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase			      =					False
						if "conv_layers" in name:
									load_conv_layer(
									    lowercase_       , lowercase_       , lowercase_       , lowercase_       , hf_model.config.feat_extract_norm == """group"""       , )
									lowerCamelCase			      =					True
						else:
									lowerCamelCase			      =					load_wavaveca_layer(lowercase_       , lowercase_       , lowercase_				)
						if not is_used:
									unused_weights.append(lowercase_				)

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



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					full_name.split("""conv_layers."""				)[-1]
			lowerCamelCase			      =					name.split("""."""				)
			lowerCamelCase			      =					int(items[0]				)
			lowerCamelCase			      =					int(items[1]				)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'				)
									lowerCamelCase			      =					value
									logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'				)
									lowerCamelCase			      =					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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'				)
									lowerCamelCase			      =					value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'				)
									lowerCamelCase			      =					value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
			else:
						unused_weights.append(lowercase_				)



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None       , snake_case__=None       , snake_case__=True       , snake_case__=False				)						->					int:
			if config_path is not None:
						lowerCamelCase			      =					WavaVecaConfig.from_pretrained(lowercase_				)
			else:
						lowerCamelCase			      =					WavaVecaConfig()

			if is_seq_class:
						lowerCamelCase			      =					read_txt_into_dict(lowercase_				)
						lowerCamelCase			      =					idalabel
						lowerCamelCase			      =					WavaVecaForSequenceClassification(lowercase_				)
						lowerCamelCase			      =					WavaVecaFeatureExtractor(
						    feature_size=1       , sampling_rate=1_60_00       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
						feature_extractor.save_pretrained(lowercase_				)

			elif is_finetuned:
						if dict_path:
									lowerCamelCase			      =					Dictionary.load(lowercase_				)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase			      =					target_dict.pad_index
									lowerCamelCase			      =					target_dict.bos_index
									lowerCamelCase			      =					target_dict.eos_index
									lowerCamelCase			      =					len(target_dict.symbols				)
									lowerCamelCase			      =					os.path.join(lowercase_       , """vocab.json"""				)
									if not os.path.isdir(lowercase_				):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_				)				)
												return
									os.makedirs(lowercase_       , exist_ok=lowercase_				)
									lowerCamelCase			      =					target_dict.indices

									# fairseq has the <pad> and <s> switched
									lowerCamelCase			      =					0
									lowerCamelCase			      =					1
									with open(lowercase_       , """w"""       , encoding="""utf-8"""				) as vocab_handle:
												json.dump(lowercase_       , lowercase_				)
									lowerCamelCase			      =					WavaVecaCTCTokenizer(
									    lowercase_       , 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=lowercase_       , )
									lowerCamelCase			      =					True if config.feat_extract_norm == """layer""" else False
									lowerCamelCase			      =					WavaVecaFeatureExtractor(
									    feature_size=1       , sampling_rate=1_60_00       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
									lowerCamelCase			      =					WavaVecaProcessor(feature_extractor=lowercase_       , tokenizer=lowercase_				)
									processor.save_pretrained(lowercase_				)

						lowerCamelCase			      =					WavaVecaForCTC(lowercase_				)
			else:
						lowerCamelCase			      =					WavaVecaForPreTraining(lowercase_				)

			if is_finetuned or is_seq_class:
						lowerCamelCase			      =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
			else:
						lowerCamelCase			      =					argparse.Namespace(task="""audio_pretraining"""				)
						lowerCamelCase			      =					fairseq.tasks.setup_task(lowercase_				)

						lowerCamelCase			      =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       , task=lowercase_				)

			lowerCamelCase			      =					model[0].eval()

			recursively_load_weights(lowercase_       , lowercase_       , not is_finetuned				)

			hf_wavavec.save_pretrained(lowercase_				)


if __name__ == "__main__":
					lowerCAmelCase							:			Optional[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"""
					)
					parser.add_argument(
					    """--is_seq_class""",
					    action="""store_true""",
					    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
					)
					lowerCAmelCase							:			Union[str, Any]     							=							parser.parse_args()

					lowerCAmelCase							:			Tuple     							=							not args.not_finetuned and not args.is_seq_class
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.dict_path,
					    is_finetuned,
					    args.is_seq_class,
					)






from math import factorial

def 						UpperCamelCase					(lowercase_: int = 20					)     ->      int:
	A__							:    Union[str, Any]        =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
	# 2, 3,...
	A__							:    Tuple        =		n // 2

	return int(factorial(lowercase_					) / (factorial(lowercase_					) * factorial(n - k					))					)


if __name__ == "__main__":
	import sys

	if len(sys.argv) == 1:
		print(solution(20))
	else:
		try:
			A_       : List[str]         =			int(sys.argv[1])
			print(solution(n))
		except ValueError:
			print('Invalid entry - please enter a number.')



