





'''simple docstring'''

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__A	: Optional[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""",
    """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""",
}
__A	: Optional[Any]              =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]





def 			lowerCAmelCase_						(    a					:					str   , a					:					Any   , a					:					Union[str, Any]   , a					:					int   , a					:					Union[str, Any]				):
							for attribute in key.split('.'				):
														a__            =							getattr(__lowerCAmelCase   , __lowerCAmelCase				)

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

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





def 			lowerCAmelCase_						(    a					:					int   , a					:					List[str]				):
							a__            =							[]
							a__            =							fairseq_model.state_dict()

							a__            =							hf_model.unispeech_sat.feature_extractor

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

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





def 			lowerCAmelCase_						(    a					:					List[Any]   , a					:					Optional[int]   , a					:					List[Any]   , a					:					Dict   , a					:					Any				):
							a__            =							full_name.split('conv_layers.'				)[-1]
							a__            =							name.split('.'				)
							a__            =							int(items[0]				)
							a__            =							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.'''				)
																					a__            =							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.'''				)
																					a__            =							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.'''				)
																					a__            =							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.'''				)
																					a__            =							value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
							else:
														unused_weights.append(__lowerCAmelCase				)





@torch.no_grad()
def 			lowerCAmelCase_						(    a					:					Optional[int]   , a					:					Optional[int]   , a					:					str=None   , a					:					str=None   , a					:					str=True				):
							if config_path is not None:
														a__            =							UniSpeechSatConfig.from_pretrained(__lowerCAmelCase				)
							else:
														a__            =							UniSpeechSatConfig()

							a__            =							''

							if is_finetuned:
														a__            =							UniSpeechSatForCTC(__lowerCAmelCase				)
							else:
														a__            =							UniSpeechSatForPreTraining(__lowerCAmelCase				)

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

							recursively_load_weights(__lowerCAmelCase   , __lowerCAmelCase				)

							hf_wavavec.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
		__A	: List[str]              =       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'
		)
		__A	: Dict              =       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
		)










_lowerCamelCase    :		Optional[Any]		      =    256
# Modulus to hash a string
_lowerCamelCase    :		Optional[Any]		      =    1_000_003

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     bool:
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					if p_len > t_len:
										return False

					SCREAMING_SNAKE_CASE							:     Dict						=				0
					SCREAMING_SNAKE_CASE							:     Tuple						=				0
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					# Calculating the hash of pattern and substring of text
					for i in range(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Tuple						=				(ord(pattern[i]   ) + p_hash * alphabet_size) % modulus
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(ord(text[i]   ) + text_hash * alphabet_size) % modulus
										if i == p_len - 1:
															continue
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				(modulus_power * alphabet_size) % modulus

					for i in range(0      ,	t_len - p_len + 1   ):
										if text_hash == p_hash and text[i : i + p_len] == pattern:
															return True
										if i == t_len - p_len:
															continue
										# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
										SCREAMING_SNAKE_CASE							:     int						=				(
										    (text_hash - ord(text[i]   ) * modulus_power) * alphabet_size
										    + ord(text[i + p_len]   )
										) % modulus
					return False

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Any						=				'abc1abc12'
					SCREAMING_SNAKE_CASE							:     str						=				'alskfjaldsabc1abc1abc12k23adsfabcabc'
					SCREAMING_SNAKE_CASE							:     List[str]						=				'alskfjaldsk23adsfabcabc'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   ) and not rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 2)
					SCREAMING_SNAKE_CASE							:     List[str]						=				'ABABX'
					SCREAMING_SNAKE_CASE							:     int						=				'ABABZABABYABABX'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 3)
					SCREAMING_SNAKE_CASE							:     int						=				'AAAB'
					SCREAMING_SNAKE_CASE							:     Tuple						=				'ABAAAAAB'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 4)
					SCREAMING_SNAKE_CASE							:     Tuple						=				'abcdabcy'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'abcxabcdabxabcdabcdabcy'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 5)
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'Lü'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'Lüsai'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'Lue'
					assert not rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )
					print('Success.'   )


if __name__ == "__main__":
			test_rabin_karp()