

"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase		:       List[Any]						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Tuple						    =	{
    """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""",
    """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""",
}
_UpperCAmelCase		:       Union[str, Any]						    =	[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							for attribute in key.split('.'				):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												snake_case_          =   'lm_head'

														snake_case_          =   getattr(__a       ,							__a				)

							if weight_type is not None:
														snake_case_          =   getattr(__a       ,							__a				).shape
							else:
														snake_case_          =   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":
														snake_case_          =   value
							elif weight_type == "weight_g":
														snake_case_          =   value
							elif weight_type == "weight_v":
														snake_case_          =   value
							elif weight_type == "bias":
														snake_case_          =   value
							else:
														snake_case_          =   value

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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							snake_case_          =   fairseq_model.state_dict()

							snake_case_          =   hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														snake_case_          =   False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __a       ,							__a       ,							__a       ,							__a       ,							hf_model.config.feat_extract_norm == 'group'       ,							)
																					snake_case_          =   True
														else:
																					for key, mapped_key in MAPPING.items():
																												snake_case_          =   'unispeech.' + 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]:
																																			snake_case_          =   True
																																			if "*" in mapped_key:
																																										snake_case_          =   name.split(__a				)[0].split('.'				)[-2]
																																										snake_case_          =   mapped_key.replace('*'       ,							__a				)
																																			if "weight_g" in name:
																																										snake_case_          =   'weight_g'
																																			elif "weight_v" in name:
																																										snake_case_          =   'weight_v'
																																			elif "bias" in name:
																																										snake_case_          =   'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										snake_case_          =   'weight'
																																			else:
																																										snake_case_          =   None
																																			set_recursively(__a       ,							__a       ,							__a       ,							__a       ,							__a       ,							__a				)
																												continue
														if not is_used:
																					unused_weights.append(__a				)

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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   full_name.split('conv_layers.'				)[-1]
							snake_case_          =   name.split('.'				)
							snake_case_          =   int(items[0]				)
							snake_case_          =   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.'''
																					)
																					snake_case_          =   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.'''
																					)
																					snake_case_          =   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."
																					)
																					snake_case_          =   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.'''
																					)
																					snake_case_          =   value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
							else:
														unused_weights.append(__a				)





@torch.no_grad()
def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							UpperCamelCase__=True				):







							'''simple docstring'''






							if config_path is not None:
														snake_case_          =   UniSpeechConfig.from_pretrained(__a				)
							else:
														snake_case_          =   UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					snake_case_          =   Dictionary.load_from_json(__a				)

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

																					# fairseq has the <pad> and <s> switched
																					snake_case_          =   42
																					snake_case_          =   43
																					with open(__a       ,							'w'       ,							encoding='utf-8'				) as vocab_handle:
																												json.dump(__a       ,							__a				)
																					snake_case_          =   WavaVecaPhonemeCTCTokenizer(
																					    __a       ,							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=__a       ,							)
																					snake_case_          =   True if config.feat_extract_norm == 'layer' else False
																					snake_case_          =   WavaVecaFeatureExtractor(
																					    feature_size=1       ,							sampling_rate=16000       ,							padding_value=0       ,							do_normalize=__a       ,							return_attention_mask=__a       ,							)
																					snake_case_          =   WavaVecaProcessor(feature_extractor=__a       ,							tokenizer=__a				)
																					processor.save_pretrained(__a				)

														snake_case_          =   UniSpeechForCTC(__a				)
							else:
														snake_case_          =   UniSpeechForPreTraining(__a				)

							if is_finetuned:
														snake_case_          =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,							arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				), 'w2v_path': checkpoint_path}				)
							else:
														snake_case_          =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

							snake_case_          =   model[0].eval()

							recursively_load_weights(__a       ,							__a       ,							__a				)

							hf_unispeech.save_pretrained(__a				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Union[str, Any]						    =	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"""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							create_all_state(1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							[]       ,							UpperCamelCase__				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):







							'''simple docstring'''






							if level == 0:
														total_list.append(current_list[:]				)
														return

							for i in range(UpperCamelCase__       ,							total_number - level + 2				):
														current_list.append(UpperCamelCase__				)
														create_all_state(i + 1       ,							UpperCamelCase__       ,							level - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														current_list.pop()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for i in total_list:
														print(*UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	4
					_UpperCAmelCase		:       Tuple						    =	2
					_UpperCAmelCase		:       Optional[int]						    =	generate_all_combinations(n, k)
					print_all_state(total_list)




