

import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					_UpperCAmelCase						:  int									=			"""platform"""
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     PegasusConfig
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     {}
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''gelu'''




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=7 ,  snake_case=True ,  snake_case=False ,  snake_case=99 ,  snake_case=32 ,  snake_case=5 ,  snake_case=4 ,  snake_case=37 ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=20 ,  snake_case=2 ,  snake_case=1 ,  snake_case=0 ,  ):
											snake_case_     							=       parent
											snake_case_     							=       batch_size
											snake_case_     							=       seq_length
											snake_case_     							=       is_training
											snake_case_     							=       use_labels
											snake_case_     							=       vocab_size
											snake_case_     							=       hidden_size
											snake_case_     							=       num_hidden_layers
											snake_case_     							=       num_attention_heads
											snake_case_     							=       intermediate_size

											snake_case_     							=       hidden_dropout_prob
											snake_case_     							=       attention_probs_dropout_prob
											snake_case_     							=       max_position_embeddings
											snake_case_     							=       eos_token_id
											snake_case_     							=       pad_token_id
											snake_case_     							=       bos_token_id




				def        a				(   self			):
											snake_case_     							=       ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size			).clip(3 ,  self.vocab_size			)
											snake_case_     							=       np.expand_dims(np.array([self.eos_token_id] * self.batch_size			) ,  1			)
											snake_case_     							=       np.concatenate([input_ids, eos_tensor] ,  axis=1			)

											snake_case_     							=       ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size			)

											snake_case_     							=       self.config_cls(
											    vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_ids=[2] ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  decoder_start_token_id=self.pad_token_id ,  **self.config_updates ,  )
											snake_case_     							=       prepare_pegasus_inputs_dict(snake_case ,  snake_case ,  snake_case			)
											return config, inputs_dict




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       20
											snake_case_     							=       model_class_name(snake_case			)

											snake_case_     							=       model.encode(inputs_dict['input_ids']			)

											snake_case_							,       snake_case_     							=       (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case_     							=       model.init_cache(decoder_input_ids.shape[0] ,  snake_case ,  snake_case			)
											snake_case_     							=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype='i4'			)

											snake_case_     							=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, :-1] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=snake_case ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'			)
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, -1:] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       model.decode(snake_case ,  snake_case			)

											snake_case_     							=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
											self.parent.assertTrue(diff < 1e-3 ,  msg=F'''Max diff is {diff}'''			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       20
											snake_case_     							=       model_class_name(snake_case			)

											snake_case_     							=       model.encode(inputs_dict['input_ids']			)

											snake_case_							,       snake_case_     							=       (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case_     							=       jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
											    ] ,  axis=-1 ,  )

											snake_case_     							=       model.init_cache(decoder_input_ids.shape[0] ,  snake_case ,  snake_case			)
											snake_case_     							=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

											snake_case_     							=       model.decode(
											    decoder_input_ids[:, :-1] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=snake_case ,  decoder_position_ids=snake_case ,  )
											snake_case_     							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'			)
											snake_case_     							=       model.decode(
											    decoder_input_ids[:, -1:] ,  snake_case ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=snake_case ,  decoder_position_ids=snake_case ,  )

											snake_case_     							=       model.decode(snake_case ,  snake_case ,  decoder_attention_mask=snake_case			)

											snake_case_     							=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
											self.parent.assertTrue(diff < 1e-3 ,  msg=F'''Max diff is {diff}'''			)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=None  ,				UpperCamelCase__=None  ,				):







							'''simple docstring'''






							if attention_mask is None:
														snake_case_     							=       np.not_equal(UpperCamelCase__  ,				config.pad_token_id ).astype(np.inta )
							if decoder_attention_mask is None:
														snake_case_     							=       np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape  ,				dtype=np.inta ),
														        np.not_equal(decoder_input_ids[:, 1:]  ,				config.pad_token_id ).astype(np.inta ),
														    ]  ,				axis=-1  ,				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}







@require_flax
class 					lowercase							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					str			     =     (
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     True
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     False
				__SCREAMING_SNAKE_CASE   :					str			     =     False
				__SCREAMING_SNAKE_CASE   :					int			     =     False




				def        a				(   self			):
											snake_case_     							=       FlaxPegasusModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case			)




				def        a				(   self			):
											self.config_tester.run_common_tests()




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__			):
																									snake_case_     							=       self._prepare_for_class(snake_case ,  snake_case			)
																									snake_case_     							=       model_class(snake_case			)

																									@jax.jit
																									def encode_jitted(snake_case ,  snake_case=None ,  **snake_case			):
																																return model.encode(input_ids=snake_case ,  attention_mask=snake_case			)

																									with self.subTest('JIT Enabled'			):
																																snake_case_     							=       encode_jitted(**snake_case			).to_tuple()

																									with self.subTest('JIT Disabled'			):
																																with jax.disable_jit():
																																							snake_case_     							=       encode_jitted(**snake_case			).to_tuple()

																									self.assertEqual(len(snake_case			) ,  len(snake_case			)			)
																									for jitted_output, output in zip(snake_case ,  snake_case			):
																																self.assertEqual(jitted_output.shape ,  output.shape			)




				def        a				(   self			):
											snake_case_							,       snake_case_     							=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__			):
																									snake_case_     							=       model_class(snake_case			)
																									snake_case_     							=       model.encode(inputs_dict['input_ids'] ,  inputs_dict['attention_mask']			)

																									snake_case_     							=       {
																									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																									    'encoder_outputs': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(snake_case ,  snake_case ,  snake_case			):
																																return model.decode(
																																    decoder_input_ids=snake_case ,  decoder_attention_mask=snake_case ,  encoder_outputs=snake_case ,  )

																									with self.subTest('JIT Enabled'			):
																																snake_case_     							=       decode_jitted(**snake_case			).to_tuple()

																									with self.subTest('JIT Disabled'			):
																																with jax.disable_jit():
																																							snake_case_     							=       decode_jitted(**snake_case			).to_tuple()

																									self.assertEqual(len(snake_case			) ,  len(snake_case			)			)
																									for jitted_output, output in zip(snake_case ,  snake_case			):
																																self.assertEqual(jitted_output.shape ,  output.shape			)




				@slow
				def        a				(   self			):
											for model_class_name in self.all_model_classes:
																		snake_case_     							=       model_class_name.from_pretrained('google/pegasus-large' ,  from_pt=snake_case			)
																		snake_case_     							=       np.ones((1, 1)			)
																		snake_case_     							=       model(snake_case			)
																		self.assertIsNotNone(snake_case			)




				@slow
				def        a				(   self			):
											snake_case_     							=       FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'			)
											snake_case_     							=       PegasusTokenizer.from_pretrained('google/pegasus-xsum'			)

											snake_case_     							=       [
											    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
											    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
											]

											snake_case_     							=       [
											    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
											    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
											]

											snake_case_     							=       tokenizer(snake_case ,  return_tensors='np' ,  truncation=snake_case ,  max_length=512 ,  padding=snake_case			)
											snake_case_     							=       model.generate(**snake_case ,  num_beams=2			).sequences
											snake_case_     							=       tokenizer.batch_decode(snake_case ,  skip_special_tokens=snake_case			)
											assert tgt_text == decoded







# 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    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






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

							for iterations in range(UpperCamelCase__ ):
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       np.dot(x.T  ,				h - y ) / y.size
														snake_case_     							=       theta - alpha * gradient  # updating the weights
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       cost_function(UpperCamelCase__  ,				UpperCamelCase__ )
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
							return theta


# In[68]:

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

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''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""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




