



'''simple docstring'''
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case_					:      Tuple    			=    logging.get_logger(__name__)

snake_case_					:      Any    			=    {
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
snake_case_					:      Tuple    			=    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						for attribute in key.split('.'				):
												_UpperCamelCase   :						Dict												=  getattr(UpperCAmelCase_    ,    UpperCAmelCase_				)

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

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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Optional[Any]												=  []
						_UpperCamelCase   :						List[Any]												=  fairseq_model.state_dict()

						_UpperCamelCase   :						str												=  hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase   :						Union[str, Any]												=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    hf_model.config.feat_extract_norm == 'group'    ,    )
																		_UpperCamelCase   :						List[Any]												=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase   :						Union[str, Any]												=  'unispeech_sat.' + 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]:
																														if "layer_norm_for_extract" in name and (".".join(name.split('.'				)[:-1]				) != key):
																																				# special case since naming is very similar
																																				continue
																														_UpperCamelCase   :						Any												=  True
																														if "*" in mapped_key:
																																				_UpperCamelCase   :						List[str]												=  name.split(UpperCAmelCase_				)[0].split('.'				)[-2]
																																				_UpperCamelCase   :						Dict												=  mapped_key.replace('*'    ,    UpperCAmelCase_				)
																														if "weight_g" in name:
																																				_UpperCamelCase   :						Union[str, Any]												=  'weight_g'
																														elif "weight_v" in name:
																																				_UpperCamelCase   :						Any												=  'weight_v'
																														elif "bias" in name:
																																				_UpperCamelCase   :						Dict												=  'bias'
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCamelCase   :						Dict												=  'weight'
																														else:
																																				_UpperCamelCase   :						Tuple												=  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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  full_name.split('conv_layers.'				)[-1]
						_UpperCamelCase   :						Optional[Any]												=  name.split('.'				)
						_UpperCamelCase   :						Tuple												=  int(items[0]				)
						_UpperCamelCase   :						List[str]												=  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.'				)
																		_UpperCamelCase   :						Optional[int]												=  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.'				)
																		_UpperCamelCase   :						List[str]												=  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[layer_id].layer_norm.bias.data.shape} was found.'				)
																		_UpperCamelCase   :						Union[str, Any]												=  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[layer_id].layer_norm.weight.data.shape} was found.'				)
																		_UpperCamelCase   :						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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=True				):
						if config_path is not None:
												_UpperCamelCase   :						str												=  UniSpeechSatConfig.from_pretrained(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						Optional[int]												=  UniSpeechSatConfig()

						_UpperCamelCase   :						Optional[Any]												=  ''

						if is_finetuned:
												_UpperCamelCase   :						Union[str, Any]												=  UniSpeechSatForCTC(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						Optional[int]												=  UniSpeechSatForPreTraining(UpperCAmelCase_				)

						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]    ,    arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
						_UpperCamelCase   :						str												=  model[0].eval()

						recursively_load_weights(UpperCAmelCase_    ,    UpperCAmelCase_				)

						hf_wavavec.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
					snake_case_					:      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'
					)
					snake_case_					:      Optional[int]    			=    parser.parse_args()
					convert_unispeech_sat_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


