
from __future__ import annotations

from collections.abc import Callable

a													=			list[list[float | int]]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			len(_A       )
						_UpperCAmelCase      =			[[0 for _ in range(size + 1       )] for _ in range(_A       )]
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42

						for row in range(_A       ):
												for col in range(_A       ):
																		_UpperCAmelCase      =			matrix[row][col]

												_UpperCAmelCase      =			vector[row][0]

						_UpperCAmelCase      =			0
						_UpperCAmelCase      =			0
						while row < size and col < size:
												# pivoting
												_UpperCAmelCase      =			max((abs(augmented[rowa][col]       ), rowa) for rowa in range(_A			,     _A       )       )[
												    1
												]
												if augmented[pivot_row][col] == 0:
																		col += 1
																		continue
												else:
																		_UpperCAmelCase      =			augmented[pivot_row], augmented[row]

												for rowa in range(row + 1			,     _A       ):
																		_UpperCAmelCase      =			augmented[rowa][col] / augmented[row][col]
																		_UpperCAmelCase      =			0
																		for cola in range(col + 1			,     size + 1       ):
																								augmented[rowa][cola] -= augmented[row][cola] * ratio

												row += 1
												col += 1

						# back substitution
						for col in range(1			,     _A       ):
												for row in range(_A       ):
																		_UpperCAmelCase      =			augmented[row][col] / augmented[col][col]
																		for cola in range(_A			,     size + 1       ):
																								augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
						return [
						    [round(augmented[row][size] / augmented[row][row]			,     1_0       )] for row in range(_A       )
						]




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
						_UpperCAmelCase      =			len(_A       )
						_UpperCAmelCase      =			[[0 for _ in range(_A       )] for _ in range(_A       )]
						_UpperCAmelCase      =			[[0] for _ in range(_A       )]
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42

						for x_val, y_val in enumerate(_A       ):
												for col in range(_A       ):
																		_UpperCAmelCase      =			(x_val + 1) ** (size - col - 1)
												_UpperCAmelCase      =			y_val

						_UpperCAmelCase      =			solve(_A			,     _A       )

						def interpolated_func(snake_case       ) -> int:
												return sum(
												    round(coeffs[x_val][0]       ) * (var ** (size - x_val - 1))
												    for x_val in range(_A       )       )

						return interpolated_func




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						return (
						    1
						    - variable
						    + variable**2
						    - variable**3
						    + variable**4
						    - variable**5
						    + variable**6
						    - variable**7
						    + variable**8
						    - variable**9
						    + variable**1_0
						)




def        _SCREAMING_SNAKE_CASE      (		snake_case = question_function			,     snake_case = 1_0       )						->				str:
						_UpperCAmelCase      =			[func(_A       ) for x_val in range(1			,     order + 1       )]

						_UpperCAmelCase      =			[
						    interpolate(data_points[:max_coeff]       ) for max_coeff in range(1			,     order + 1       )
						]

						_UpperCAmelCase      =			0
						_UpperCAmelCase      =			42
						_UpperCAmelCase      =			42

						for poly in polynomials:
												_UpperCAmelCase      =			1
												while func(_A       ) == poly(_A       ):
																		x_val += 1

												ret += poly(_A       )

						return ret


if __name__ == "__main__":
				print(F'{solution() = }')





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a													=			logging.get_logger("transformers.models.speecht5")

a													=			{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
a													=			{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a													=			{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
a													=			{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
a													=			{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a													=			{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a													=			{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
a													=			{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a													=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			[]
a													=			[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			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      =			value
						elif weight_type == "weight_g":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_v":
												_UpperCAmelCase      =			value
						elif weight_type == "bias":
												_UpperCAmelCase      =			value
						elif weight_type == "running_mean":
												_UpperCAmelCase      =			value
						elif weight_type == "running_var":
												_UpperCAmelCase      =			value
						elif weight_type == "num_batches_tracked":
												_UpperCAmelCase      =			value
						else:
												_UpperCAmelCase      =			value

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						for key in ignore_keys:
												if key.endswith(""".*"""       ):
																		if name.startswith(key[:-1]       ):
																								return True
												elif ".*." in key:
																		_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			[]

						if task == "s2t":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2T
												_UpperCAmelCase      =			IGNORE_KEYS_S2T
						elif task == "t2s":
												_UpperCAmelCase      =			None
												_UpperCAmelCase      =			MAPPING_T2S
												_UpperCAmelCase      =			IGNORE_KEYS_T2S
						elif task == "s2s":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2S
												_UpperCAmelCase      =			IGNORE_KEYS_S2S
						else:
												raise ValueError(f"Unsupported task: {task}"       )

						for name, value in fairseq_dict.items():
												if should_ignore(snake_case			,     snake_case       ):
																		logger.info(f"{name} was ignored"       )
																		continue

												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																														if prefix in name and suffix in name:
																																				_UpperCAmelCase      =			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				_UpperCAmelCase      =			"""weight"""
																														elif "running_mean" in name:
																																				_UpperCAmelCase      =			"""running_mean"""
																														elif "running_var" in name:
																																				_UpperCAmelCase      =			"""running_var"""
																														elif "num_batches_tracked" in name:
																																				_UpperCAmelCase      =			"""num_batches_tracked"""
																														else:
																																				_UpperCAmelCase      =			None
																														set_recursively(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
						_UpperCAmelCase      =			name.split("""."""       )
						_UpperCAmelCase      =			int(items[0]       )
						_UpperCAmelCase      =			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      =			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      =			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."       )
																		_UpperCAmelCase      =			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."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						else:
												unused_weights.append(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=None			,     )						->				Union[str, Any]:
						if config_path is not None:
												_UpperCAmelCase      =			SpeechTaConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			SpeechTaConfig()

						if task == "s2t":
												_UpperCAmelCase      =			config.max_text_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToText(snake_case       )
						elif task == "t2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			6_0_0
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForTextToSpeech(snake_case       )
						elif task == "s2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToSpeech(snake_case       )
						else:
												raise ValueError(f"Unknown task name: {task}"       )

						if vocab_path:
												_UpperCAmelCase      =			SpeechTaTokenizer(snake_case			,     model_max_length=config.max_text_positions       )

												# Mask token behaves like a normal word, i.e. include the space before it
												_UpperCAmelCase      =			AddedToken("""<mask>"""			,     lstrip=snake_case			,     rstrip=snake_case       )
												_UpperCAmelCase      =			mask_token
												tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
												tokenizer.add_tokens(["""<ctc_blank>"""]       )

						_UpperCAmelCase      =			SpeechTaFeatureExtractor()
						_UpperCAmelCase      =			SpeechTaProcessor(tokenizer=snake_case			,     feature_extractor=snake_case       )
						processor.save_pretrained(snake_case       )

						_UpperCAmelCase      =			torch.load(snake_case       )
						recursively_load_weights(fairseq_checkpoint["""model"""]			,     snake_case			,     snake_case       )

						model.save_pretrained(snake_case       )

						if repo_id:
												print("""Pushing to the hub..."""       )
												processor.push_to_hub(snake_case       )
												model.push_to_hub(snake_case       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				parser.add_argument(
				    "--task",
				    default="s2t",
				    type=str,
				    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
				)
				parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
				)

				a													=			parser.parse_args()
				convert_speechta_checkpoint(
				    args.task,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.vocab_path,
				    args.push_to_hub,
				)