



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			for attribute in key.split("."    ):
						A							:					List[Any]              =						getattr(_lowerCamelCase ,    _lowerCamelCase    )

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

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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[Any]              =						[]
			A							:					Optional[int]              =						fairseq_model.state_dict()

			A							:					Any              =						hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						A							:					int              =						False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    hf_model.config.feat_extract_norm == "group" ,    )
									A							:					Optional[Any]              =						True
						else:
									for key, mapped_key in MAPPING.items():
												A							:					Dict              =						"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

												if key in name or (key.split("w2v_model."    )[-1] == name.split("."    )[0] and not is_finetuned):
															A							:					str              =						True
															if "*" in mapped_key:
																		A							:					str              =						name.split(_lowerCamelCase    )[0].split("."    )[-2]
																		A							:					Tuple              =						mapped_key.replace("*" ,    _lowerCamelCase    )
															if "weight_g" in name:
																		A							:					List[str]              =						"weight_g"
															elif "weight_v" in name:
																		A							:					int              =						"weight_v"
															elif "weight" in name:
																		A							:					Tuple              =						"weight"
															elif "bias" in name:
																		A							:					int              =						"bias"
															else:
																		A							:					Dict              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						full_name.split("conv_layers."    )[-1]
			A							:					Union[str, Any]              =						name.split("."    )
			A							:					List[Any]              =						int(items[0]    )
			A							:					str              =						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."""
									)
									A							:					Any              =						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."""
									)
									A							:					Optional[Any]              =						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."
									)
									A							:					Optional[Any]              =						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."""
									)
									A							:					List[Any]              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=None ,    _lowerCamelCase=None ,    _lowerCamelCase=True    ):
			if config_path is not None:
						A							:					List[Any]              =						HubertConfig.from_pretrained(_lowerCamelCase    )
			else:
						A							:					List[Any]              =						HubertConfig()

			if is_finetuned:
						if dict_path:
									A							:					Optional[int]              =						Dictionary.load(_lowerCamelCase    )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A							:					Dict              =						target_dict.pad_index
									A							:					Any              =						target_dict.bos_index
									A							:					Tuple              =						target_dict.eos_index
									A							:					int              =						len(target_dict.symbols    )
									A							:					Tuple              =						os.path.join(_lowerCamelCase ,    "vocab.json"    )
									if not os.path.isdir(_lowerCamelCase    ):
												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCamelCase    )    )
												return
									os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
									with open(_lowerCamelCase ,    "w" ,    encoding="utf-8"    ) as vocab_handle:
												json.dump(target_dict.indices ,    _lowerCamelCase    )
									A							:					Optional[Any]              =						WavaVecaCTCTokenizer(
									    _lowerCamelCase ,    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=_lowerCamelCase ,    )
									A							:					Dict              =						True if config.feat_extract_norm == "layer" else False
									A							:					Tuple              =						WavaVecaFeatureExtractor(
									    feature_size=1 ,    sampling_rate=1_6000 ,    padding_value=0 ,    do_normalize=_lowerCamelCase ,    return_attention_mask=_lowerCamelCase ,    )
									A							:					int              =						WavaVecaProcessor(feature_extractor=_lowerCamelCase ,    tokenizer=_lowerCamelCase    )
									processor.save_pretrained(_lowerCamelCase    )

						A							:					str              =						HubertForCTC(_lowerCamelCase    )
			else:
						A							:					int              =						HubertModel(_lowerCamelCase    )

			if is_finetuned:
						A       , A       , A							:					Optional[Any]              =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path] ,    arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    )}    )
			else:
						A       , A       , A							:					List[Any]              =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

			A							:					Optional[Any]              =						model[0].eval()

			recursively_load_weights(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			hf_wavavec.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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"""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						else:
									raise ZeroDivisionError("Could not find root"    ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess    ) < precision:
									return next_guess

						A							:					int              =						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")