






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__      :	List[str]       	=      logging.get_logger(__name__)

snake_case__      :	str       	=      {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
			for attribute in key.split('.'):
						UpperCamelCase_									=     getattr(__UpperCamelCase    ,      __UpperCamelCase)

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

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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
			UpperCamelCase_									=     []
			UpperCamelCase_									=     fairseq_model.state_dict()

			UpperCamelCase_									=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCamelCase_									=     False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase    ,      hf_model.config.feat_extract_norm == 'group'    ,      )
									UpperCamelCase_									=     True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCamelCase_									=     'sew.' + 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]:
															UpperCamelCase_									=     True
															if "*" in mapped_key:
																		UpperCamelCase_									=     name.split(__UpperCamelCase)[0].split('.')[-2]
																		UpperCamelCase_									=     mapped_key.replace('*'    ,      __UpperCamelCase)
															if "weight_g" in name:
																		UpperCamelCase_									=     'weight_g'
															elif "weight_v" in name:
																		UpperCamelCase_									=     'weight_v'
															elif "weight" in name:
																		UpperCamelCase_									=     'weight'
															elif "bias" in name:
																		UpperCamelCase_									=     'bias'
															else:
																		UpperCamelCase_									=     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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
			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:
									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."""
									)
									UpperCamelCase_									=     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."""
									)
									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:
									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."
									)
									UpperCamelCase_									=     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."""
									)
									UpperCamelCase_									=     value
									logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
			else:
						unused_weights.append(__UpperCamelCase)






def     _snake_case    (__lowercase    ,      __lowercase):
			UpperCamelCase_									=     SEWConfig()
			if is_finetuned:
						UpperCamelCase_									=     model.wav_encoder.wav_model.cfg
			else:
						UpperCamelCase_									=     model.cfg

			UpperCamelCase_									=     fs_config.conv_bias
			UpperCamelCase_									=     eval(fs_config.conv_feature_layers)
			UpperCamelCase_									=     [x[0] for x in conv_layers]
			UpperCamelCase_									=     [x[1] for x in conv_layers]
			UpperCamelCase_									=     [x[2] for x in conv_layers]
			UpperCamelCase_									=     'gelu'
			UpperCamelCase_									=     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			UpperCamelCase_									=     0.0
			UpperCamelCase_									=     fs_config.activation_fn.name
			UpperCamelCase_									=     fs_config.encoder_embed_dim
			UpperCamelCase_									=     0.02
			UpperCamelCase_									=     fs_config.encoder_ffn_embed_dim
			UpperCamelCase_									=     1e-5
			UpperCamelCase_									=     fs_config.encoder_layerdrop
			UpperCamelCase_									=     fs_config.encoder_attention_heads
			UpperCamelCase_									=     fs_config.conv_pos_groups
			UpperCamelCase_									=     fs_config.conv_pos
			UpperCamelCase_									=     len(__UpperCamelCase)
			UpperCamelCase_									=     fs_config.encoder_layers
			UpperCamelCase_									=     fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						UpperCamelCase_									=     model.cfg
						UpperCamelCase_									=     fs_config.final_dropout
						UpperCamelCase_									=     fs_config.layerdrop
			UpperCamelCase_									=     fs_config.activation_dropout
			UpperCamelCase_									=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			UpperCamelCase_									=     fs_config.attention_dropout
			UpperCamelCase_									=     fs_config.dropout_input
			UpperCamelCase_									=     fs_config.dropout
			UpperCamelCase_									=     fs_config.mask_channel_length
			UpperCamelCase_									=     fs_config.mask_channel_prob
			UpperCamelCase_									=     fs_config.mask_length
			UpperCamelCase_									=     fs_config.mask_prob

			UpperCamelCase_									=     'Wav2Vec2FeatureExtractor'
			UpperCamelCase_									=     'Wav2Vec2CTCTokenizer'

			return config






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=True):

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

			if config_path is not None:
						UpperCamelCase_									=     SEWConfig.from_pretrained(__UpperCamelCase)
			else:
						UpperCamelCase_									=     convert_config(model[0]    ,      __UpperCamelCase)
			UpperCamelCase_									=     model[0].eval()

			UpperCamelCase_									=     True if config.feat_extract_norm == 'layer' else False
			UpperCamelCase_									=     WavaVecaFeatureExtractor(
			    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=__UpperCamelCase    ,      return_attention_mask=__UpperCamelCase    ,      )

			if is_finetuned:
						if dict_path:
									UpperCamelCase_									=     Dictionary.load(__UpperCamelCase)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									UpperCamelCase_									=     target_dict.pad_index
									UpperCamelCase_									=     target_dict.bos_index
									UpperCamelCase_									=     target_dict.pad_index
									UpperCamelCase_									=     target_dict.bos_index
									UpperCamelCase_									=     target_dict.eos_index
									UpperCamelCase_									=     len(target_dict.symbols)
									UpperCamelCase_									=     os.path.join(__UpperCamelCase    ,      'vocab.json')
									if not os.path.isdir(__UpperCamelCase):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__UpperCamelCase))
												return
									os.makedirs(__UpperCamelCase    ,      exist_ok=__UpperCamelCase)
									with open(__UpperCamelCase    ,      'w'    ,      encoding='utf-8') as vocab_handle:
												json.dump(target_dict.indices    ,      __UpperCamelCase)
									UpperCamelCase_									=     WavaVecaCTCTokenizer(
									    __UpperCamelCase    ,      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=__UpperCamelCase    ,      )
									UpperCamelCase_									=     WavaVecaProcessor(feature_extractor=__UpperCamelCase    ,      tokenizer=__UpperCamelCase)
									processor.save_pretrained(__UpperCamelCase)

						UpperCamelCase_									=     SEWForCTC(__UpperCamelCase)
			else:
						UpperCamelCase_									=     SEWModel(__UpperCamelCase)
						feature_extractor.save_pretrained(__UpperCamelCase)

			recursively_load_weights(__UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase)

			hf_model.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
	snake_case__      :	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(
	    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	snake_case__      :	Optional[Any]       	=      parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)













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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase = "x"    ,      __lowercase = 10**-10    ,      __lowercase = 1    ,      ):
   UpperCamelCase_									=     symbols(__lowercase)
   UpperCamelCase_									=     lambdify(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     lambdify(__lowercase    ,      diff(__lowercase    ,      __lowercase))

   UpperCamelCase_									=     starting_point

   while True:
      if diff_function(__lowercase) != 0:
         UpperCamelCase_									=     prev_guess - multiplicity * func(__lowercase) / diff_function(
             __lowercase)
      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

      UpperCamelCase_									=     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", 1_0, precision=0.005)}',
 )

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





