




"""simple docstring"""







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 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple      ,       snake_case__   : str )     ->  Union[str, Any]:


				'''simple docstring'''






				for attribute in key.split(""".""" ):
								lowerCAmelCase  =				getattr(snake_case__      ,       snake_case__ )

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

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





def 							lowercase					(snake_case__   : int      ,       snake_case__   : List[str] )     ->  Optional[Any]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple      ,       snake_case__   : Dict )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				[]

				if task == "s2t":
								lowerCAmelCase  =				hf_model.speechta.encoder.prenet.feature_encoder
								lowerCAmelCase  =				MAPPING_S2T
								lowerCAmelCase  =				IGNORE_KEYS_S2T
				elif task == "t2s":
								lowerCAmelCase  =				None
								lowerCAmelCase  =				MAPPING_T2S
								lowerCAmelCase  =				IGNORE_KEYS_T2S
				elif task == "s2s":
								lowerCAmelCase  =				hf_model.speechta.encoder.prenet.feature_encoder
								lowerCAmelCase  =				MAPPING_S2S
								lowerCAmelCase  =				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

								lowerCAmelCase  =				False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       hf_model.config.feat_extract_norm == """group"""      ,       )
												lowerCAmelCase  =				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:
																				lowerCAmelCase ,						lowerCAmelCase  =				key.split(""".*.""" )
																				if prefix in name and suffix in name:
																								lowerCAmelCase  =				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				lowerCAmelCase  =				True
																				if "*" in mapped_key:
																								lowerCAmelCase  =				name.split(snake_case__ )[0].split(""".""" )[-2]
																								lowerCAmelCase  =				mapped_key.replace("""*"""      ,       snake_case__ )
																				if "weight_g" in name:
																								lowerCAmelCase  =				"""weight_g"""
																				elif "weight_v" in name:
																								lowerCAmelCase  =				"""weight_v"""
																				elif "bias" in name:
																								lowerCAmelCase  =				"""bias"""
																				elif "weight" in name:
																								lowerCAmelCase  =				"""weight"""
																				elif "running_mean" in name:
																								lowerCAmelCase  =				"""running_mean"""
																				elif "running_var" in name:
																								lowerCAmelCase  =				"""running_var"""
																				elif "num_batches_tracked" in name:
																								lowerCAmelCase  =				"""num_batches_tracked"""
																				else:
																								lowerCAmelCase  =				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 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : List[Any]      ,       snake_case__   : str      ,       snake_case__   : List[str]      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				full_name.split("""conv_layers.""" )[-1]
				lowerCAmelCase  =				name.split(""".""" )
				lowerCAmelCase  =				int(items[0] )
				lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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.''' )
												lowerCAmelCase  =				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 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Dict      ,       snake_case__   : Optional[Any]=None      ,       snake_case__   : List[Any]=None      ,       snake_case__   : Tuple=None      ,       )     ->  List[str]:


				'''simple docstring'''






				if config_path is not None:
								lowerCAmelCase  =				SpeechTaConfig.from_pretrained(snake_case__ )
				else:
								lowerCAmelCase  =				SpeechTaConfig()

				if task == "s2t":
								lowerCAmelCase  =				config.max_text_positions
								lowerCAmelCase  =				SpeechTaForSpeechToText(snake_case__ )
				elif task == "t2s":
								lowerCAmelCase  =				1_876
								lowerCAmelCase  =				600
								lowerCAmelCase  =				config.max_speech_positions
								lowerCAmelCase  =				SpeechTaForTextToSpeech(snake_case__ )
				elif task == "s2s":
								lowerCAmelCase  =				1_876
								lowerCAmelCase  =				config.max_speech_positions
								lowerCAmelCase  =				SpeechTaForSpeechToSpeech(snake_case__ )
				else:
								raise ValueError(f'''Unknown task name: {task}''' )

				if vocab_path:
								lowerCAmelCase  =				SpeechTaTokenizer(snake_case__      ,       model_max_length=config.max_text_positions )

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

				lowerCAmelCase  =				SpeechTaFeatureExtractor()
				lowerCAmelCase  =				SpeechTaProcessor(tokenizer=snake_case__      ,       feature_extractor=snake_case__ )
				processor.save_pretrained(snake_case__ )

				lowerCAmelCase  =				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,
		)











"""simple docstring"""







from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 							lowercase					(snake_case__   : str      ,       snake_case__   : float | Decimal      ,       snake_case__   : float = 10**-10 )     ->  float:


				'''simple docstring'''






				lowerCAmelCase  =				a
				while True:
								lowerCAmelCase  =				Decimal(snake_case__ ) - (
								    Decimal(eval(snake_case__ ) ) / Decimal(eval(str(diff(snake_case__ ) ) ) )  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(snake_case__ ) ) < precision:  # noqa: S307
												return float(snake_case__ )


# 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
		print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}""")
		# Find Square Root of 5
		print(f"""The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}""")
		# Exponential Roots
		print(f"""The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}""")





