
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
		lowerCamelCase__	=							"platform"
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class        lowerCAmelCase__      :
						UpperCamelCase_							: int	=		PegasusConfig
						UpperCamelCase_							: Tuple	=		{}
						UpperCamelCase_							: Optional[int]	=		"gelu"

						def __init__(			self       ,							a       ,							a=13       ,							a=7       ,							a=True       ,							a=False       ,							a=99       ,							a=32       ,							a=5       ,							a=4       ,							a=37       ,							a=0.1       ,							a=0.1       ,							a=20       ,							a=2       ,							a=1       ,							a=0       ,							)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	parent
								_UpperCamelCase						    =	batch_size
								_UpperCamelCase						    =	seq_length
								_UpperCamelCase						    =	is_training
								_UpperCamelCase						    =	use_labels
								_UpperCamelCase						    =	vocab_size
								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	intermediate_size

								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	eos_token_id
								_UpperCamelCase						    =	pad_token_id
								_UpperCamelCase						    =	bos_token_id

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

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

								_UpperCamelCase						    =	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       ,							)
								_UpperCamelCase						    =	prepare_pegasus_inputs_dict(a       ,							a       ,							a				)
								return config, inputs_dict

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	20
								_UpperCamelCase						    =	model_class_name(a				)

								_UpperCamelCase						    =	model.encode(inputs_dict["""input_ids"""]				)

								_UpperCamelCase    ,	_UpperCamelCase						    =	(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								_UpperCamelCase						    =	model.init_cache(decoder_input_ids.shape[0]       ,							a       ,							a				)
								_UpperCamelCase						    =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,							dtype="""i4"""				)

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

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

								_UpperCamelCase						    =	model.decode(a       ,							a				)

								_UpperCamelCase						    =	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       ,							a       ,							a       ,							a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	20
								_UpperCamelCase						    =	model_class_name(a				)

								_UpperCamelCase						    =	model.encode(inputs_dict["""input_ids"""]				)

								_UpperCamelCase    ,	_UpperCamelCase						    =	(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

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

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

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

								_UpperCamelCase						    =	model.decode(a       ,							a       ,							decoder_attention_mask=a				)

								_UpperCamelCase						    =	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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=None						,  lowerCAmelCase=None						,  )		->				Optional[Any]:

		"""simple docstring"""






		if attention_mask is None:
				_UpperCamelCase						    =	np.not_equal(lowerCAmelCase						,  config.pad_token_id ).astype(np.inta )
		if decoder_attention_mask is None:
				_UpperCamelCase						    =	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        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Any	=		(
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase_							: int	=		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						UpperCamelCase_							: Any	=		True
						UpperCamelCase_							: List[Any]	=		False
						UpperCamelCase_							: List[Any]	=		False
						UpperCamelCase_							: int	=		False

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxPegasusModelTester(self				)
								_UpperCamelCase						    =	ConfigTester(self       ,							config_class=a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								self.config_tester.run_common_tests()

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(a       ,							a       ,							a				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	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(a       ,							a       ,							a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
												_UpperCamelCase						    =	self._prepare_for_class(a       ,							a				)
												_UpperCamelCase						    =	model_class(a				)

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

												with self.subTest("""JIT Enabled"""				):
														_UpperCamelCase						    =	encode_jitted(**a				).to_tuple()

												with self.subTest("""JIT Disabled"""				):
														with jax.disable_jit():
																_UpperCamelCase						    =	encode_jitted(**a				).to_tuple()

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

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
												_UpperCamelCase						    =	model_class(a				)
												_UpperCamelCase						    =	model.encode(inputs_dict["""input_ids"""]       ,							inputs_dict["""attention_mask"""]				)

												_UpperCamelCase						    =	{
												    """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(a       ,							a       ,							a				):
														return model.decode(
														    decoder_input_ids=a       ,							decoder_attention_mask=a       ,							encoder_outputs=a       ,							)

												with self.subTest("""JIT Enabled"""				):
														_UpperCamelCase						    =	decode_jitted(**a				).to_tuple()

												with self.subTest("""JIT Disabled"""				):
														with jax.disable_jit():
																_UpperCamelCase						    =	decode_jitted(**a				).to_tuple()

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

						@slow
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								for model_class_name in self.all_model_classes:
										_UpperCamelCase						    =	model_class_name.from_pretrained("""google/pegasus-large"""       ,							from_pt=a				)
										_UpperCamelCase						    =	np.ones((1, 1)				)
										_UpperCamelCase						    =	model(a				)
										self.assertIsNotNone(a				)







						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""				)
								_UpperCamelCase						    =	PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""				)

								_UpperCamelCase						    =	[
								    """ 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!\" """,
								]

								_UpperCamelCase						    =	[
								    """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.""",
								]

								_UpperCamelCase						    =	tokenizer(a       ,							return_tensors="""np"""       ,							truncation=a       ,							max_length=5_12       ,							padding=a				)
								_UpperCamelCase						    =	model.generate(**a       ,							num_beams=2				).sequences
								_UpperCamelCase						    =	tokenizer.batch_decode(a       ,							skip_special_tokens=a				)
								assert tgt_text == decoded





from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# If row is equal to the size of the board it means there are a queen in each row in
		# the current board (possible_board)
		if row == n:
				# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
				# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
				boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
				return

		# We iterate each column in the row to find all possible results in each row
		for col in range(lowerCAmelCase ):
				# We apply that we learned previously. First we check that in the current board
				# (possible_board) there are not other same value because if there is it means
				# that there are a collision in vertical. Then we apply the two formulas we
				# learned before:
				#
				# 45º: y - x = b or 45: row - col = b
				# 135º: y + x = b or row + col = b.
				#
				# And we verify if the results of this two formulas not exist in their variables
				# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
				#
				# If any or these are True it means there is a collision so we continue to the
				# next value in the for loop.
				if (
				    col in possible_board
				    or row - col in diagonal_right_collisions
				    or row + col in diagonal_left_collisions
				):
						continue

				# If it is False we call dfs function again and we update the inputs
				depth_first_search(
				    [*possible_board, col]						,  [*diagonal_right_collisions, row - col]						,  [*diagonal_left_collisions, row + col]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

		# Print all the boards
		for board in boards:
				for column in board:
						print(lowerCAmelCase )
				print("""""" )

		print(len(lowerCAmelCase )						,  """solutions were found.""" )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



