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()
__lowerCamelCase    :				List[str]							     =	logging.get_logger('''transformers.models.speecht5''')

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''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''',
}
__lowerCamelCase    :				Any							     =	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__lowerCamelCase    :				str							     =	{
    '''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''',
}
__lowerCamelCase    :				List[str]							     =	{
    '''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''',
}
__lowerCamelCase    :				Optional[int]							     =	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowerCamelCase    :				Tuple							     =	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowerCamelCase    :				Tuple							     =	{
    '''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''',
}
__lowerCamelCase    :				Union[str, Any]							     =	{
    '''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''',
}
__lowerCamelCase    :				Optional[int]							     =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase    :				Optional[int]							     =	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase    :				Dict							     =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase    :				str							     =	[]
__lowerCamelCase    :				Dict							     =	[
    '''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''',
]
__lowerCamelCase    :				List[str]							     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowerCamelCase    :				str							     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowerCamelCase    :				Optional[Any]							     =	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Optional[Any]			)							->      str:







		"""simple docstring"""





		for attribute in key.split("""."""			):
				SCREAMING_SNAKE_CASE__    							=   getattr(a_				,  a_			)

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

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[int]			)							->      int:







		"""simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Union[str, Any]			)							->      Dict:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   []

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

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

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

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

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: int			)							->      List[Any]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   full_name.split("""conv_layers."""			)[-1]
		SCREAMING_SNAKE_CASE__    							=   name.split("""."""			)
		SCREAMING_SNAKE_CASE__    							=   int(items[0]			)
		SCREAMING_SNAKE_CASE__    							=   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."""			)
						SCREAMING_SNAKE_CASE__    							=   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."""			)
						SCREAMING_SNAKE_CASE__    							=   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."""			)
						SCREAMING_SNAKE_CASE__    							=   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."""			)
						SCREAMING_SNAKE_CASE__    							=   value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
		else:
				unused_weights.append(a_			)



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Union[str, Any]=None				,  __UpperCamelCase					: Any=None				,  __UpperCamelCase					: Union[str, Any]=None				,  )							->      Optional[Any]:







		"""simple docstring"""





		if config_path is not None:
				SCREAMING_SNAKE_CASE__    							=   SpeechTaConfig.from_pretrained(a_			)
		else:
				SCREAMING_SNAKE_CASE__    							=   SpeechTaConfig()

		if task == "s2t":
				SCREAMING_SNAKE_CASE__    							=   config.max_text_positions
				SCREAMING_SNAKE_CASE__    							=   SpeechTaForSpeechToText(a_			)
		elif task == "t2s":
				SCREAMING_SNAKE_CASE__    							=   18_76
				SCREAMING_SNAKE_CASE__    							=   6_00
				SCREAMING_SNAKE_CASE__    							=   config.max_speech_positions
				SCREAMING_SNAKE_CASE__    							=   SpeechTaForTextToSpeech(a_			)
		elif task == "s2s":
				SCREAMING_SNAKE_CASE__    							=   18_76
				SCREAMING_SNAKE_CASE__    							=   config.max_speech_positions
				SCREAMING_SNAKE_CASE__    							=   SpeechTaForSpeechToSpeech(a_			)
		else:
				raise ValueError(f"""Unknown task name: {task}"""			)

		if vocab_path:
				SCREAMING_SNAKE_CASE__    							=   SpeechTaTokenizer(a_				,  model_max_length=config.max_text_positions			)

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

		SCREAMING_SNAKE_CASE__    							=   SpeechTaFeatureExtractor()
		SCREAMING_SNAKE_CASE__    							=   SpeechTaProcessor(tokenizer=a_				,  feature_extractor=a_			)
		processor.save_pretrained(a_			)

		SCREAMING_SNAKE_CASE__    							=   torch.load(a_			)
		recursively_load_weights(fairseq_checkpoint["""model"""]				,  a_				,  a_			)

		model.save_pretrained(a_			)

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


if __name__ == "__main__":
			__lowerCamelCase    :				Tuple							     =	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.'''
			)

			__lowerCamelCase    :				Any							     =	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,
			)

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
