

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class        _SCREAMING_SNAKE_CASE	:
						_UpperCamelCase:Dict     =						PegasusConfig
						_UpperCamelCase:Union[str, Any]     =						{}
						_UpperCamelCase:str     =						"gelu"







						def __init__(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=13			,		_SCREAMING_SNAKE_CASE=7			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=99			,		_SCREAMING_SNAKE_CASE=32			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=4			,		_SCREAMING_SNAKE_CASE=37			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=40			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=1			,		_SCREAMING_SNAKE_CASE=0			,		)->				Tuple:
													lowerCamelCase_				     =parent
													lowerCamelCase_				     =batch_size
													lowerCamelCase_				     =seq_length
													lowerCamelCase_				     =is_training
													lowerCamelCase_				     =use_labels
													lowerCamelCase_				     =vocab_size
													lowerCamelCase_				     =hidden_size
													lowerCamelCase_				     =num_hidden_layers
													lowerCamelCase_				     =num_attention_heads
													lowerCamelCase_				     =intermediate_size

													lowerCamelCase_				     =hidden_dropout_prob
													lowerCamelCase_				     =attention_probs_dropout_prob
													lowerCamelCase_				     =max_position_embeddings
													lowerCamelCase_				     =eos_token_id
													lowerCamelCase_				     =pad_token_id
													lowerCamelCase_				     =bos_token_id







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =ids_tensor([self.batch_size, self.seq_length - 1]			,		self.vocab_size				)
													lowerCamelCase_				     =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)			,		1				)
													lowerCamelCase_				     =tf.concat([input_ids, eos_tensor]			,		axis=1				)

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

													lowerCamelCase_				     =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			,		)
													lowerCamelCase_				     =prepare_pegasus_inputs_dict(a_			,		a_			,		a_				)
													return config, inputs_dict







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				List[Any]:
													lowerCamelCase_				     =TFPegasusModel(config=a_				).get_decoder()
													lowerCamelCase_				     =inputs_dict['''input_ids''']

													lowerCamelCase_				     =input_ids[:1, :]
													lowerCamelCase_				     =inputs_dict['''attention_mask'''][:1, :]
													lowerCamelCase_				     =inputs_dict['''head_mask''']
													lowerCamelCase_				     =1

													# first forward pass
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		head_mask=a_			,		use_cache=a_				)

													lowerCamelCase_				     =outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													lowerCamelCase_				     =ids_tensor((self.batch_size, 3)			,		config.vocab_size				)
													lowerCamelCase_				     =tf.cast(ids_tensor((self.batch_size, 3)			,		2				)			,		tf.inta				)

													# append to next input_ids and
													lowerCamelCase_				     =tf.concat([input_ids, next_tokens]			,		axis=-1				)
													lowerCamelCase_				     =tf.concat([attention_mask, next_attn_mask]			,		axis=-1				)

													lowerCamelCase_				     =model(a_			,		attention_mask=a_				)[0]
													lowerCamelCase_				     =model(a_			,		attention_mask=a_			,		past_key_values=a_				)[0]

													self.parent.assertEqual(next_tokens.shape[1]			,		output_from_past.shape[1]				)

													# select random slice
													lowerCamelCase_				     =int(ids_tensor((1,)			,		output_from_past.shape[-1]				)				)
													lowerCamelCase_				     =output_from_no_past[:, -3:, random_slice_idx]
													lowerCamelCase_				     =output_from_past[:, :, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(a_			,		a_			,		rtol=1E-3				)




def     __UpperCamelCase			(	_A :				Optional[Any]	,   _A :				Tuple	,   _A :				Tuple	,   _A :				Optional[int]=None	,   _A :				Optional[Any]=None	,   _A :				Dict=None	,   _A :				Optional[Any]=None	,   _A :				List[str]=None	,   )							->Tuple:



							"""simple docstring"""





							if attention_mask is None:
														lowerCamelCase_				     =tf.cast(tf.math.not_equal(_UpperCAmelCase	,   config.pad_token_id						)	,   tf.inta						)
							if decoder_attention_mask is None:
														lowerCamelCase_				     =tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape	,   dtype=tf.inta						),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,   config.pad_token_id						)	,   tf.inta						),
														    ]	,   axis=-1	,   )
							if head_mask is None:
														lowerCamelCase_				     =tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														lowerCamelCase_				     =tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
							if cross_attn_head_mask is None:
														lowerCamelCase_				     =tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}


@require_tf
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase      ,  __UpperCamelCase      ,  unittest.TestCase):
						_UpperCamelCase:Optional[int]     =						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
						_UpperCamelCase:Union[str, Any]     =						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
						_UpperCamelCase:Any     =						(
						    {
						        "conversational": TFPegasusForConditionalGeneration,
						        "feature-extraction": TFPegasusModel,
						        "summarization": TFPegasusForConditionalGeneration,
						        "text2text-generation": TFPegasusForConditionalGeneration,
						        "translation": TFPegasusForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_UpperCamelCase:int     =						True
						_UpperCamelCase:Union[str, Any]     =						False
						_UpperCamelCase:Optional[int]     =						False







						def     _snake_case							(       self				)->				Union[str, Any]:
													lowerCamelCase_				     =TFPegasusModelTester(self				)
													lowerCamelCase_				     =ConfigTester(self			,		config_class=a_				)







						def     _snake_case							(       self				)->				List[str]:
													self.config_tester.run_common_tests()







						def     _snake_case							(       self				)->				int:
													lowerCamelCase_				     =self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*a_				)


@require_sentencepiece
@require_tokenizers
@require_tf
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase):
						_UpperCamelCase:Union[str, Any]     =						[
						    " 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:Union[str, Any]     =						[
						    "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
						    " reduce the risk of wildfires.",
						    "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
						]  # differs slightly from pytorch, likely due to numerical differences in linear layers
						_UpperCamelCase:Optional[Any]     =						"google/pegasus-xsum"







						@cached_property
						def     _snake_case							(       self				)->				str:
													return AutoTokenizer.from_pretrained(self.model_name				)







						@cached_property
						def     _snake_case							(       self				)->				List[Any]:
													lowerCamelCase_				     =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
													return model







						def     _snake_case							(       self			,		**_SCREAMING_SNAKE_CASE				)->				int:
													lowerCamelCase_				     =self.translate_src_text(**a_				)
													assert self.expected_text == generated_words







						def     _snake_case							(       self			,		**_SCREAMING_SNAKE_CASE				)->				Tuple:
													lowerCamelCase_				     =self.tokenizer(self.src_text			,		**a_			,		padding=a_			,		return_tensors="""tf"""				)
													lowerCamelCase_				     =self.model.generate(
													    model_inputs.input_ids			,		attention_mask=model_inputs.attention_mask			,		num_beams=2			,		use_cache=a_			,		)
													lowerCamelCase_				     =self.tokenizer.batch_decode(generated_ids.numpy()			,		skip_special_tokens=a_				)
													return generated_words







						@slow
						def     _snake_case							(       self				)->				int:
													self._assert_generated_batch_equal_expected()





import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	[
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					__UpperCAmelCase    :     str													=	cell.position[0]
					__UpperCAmelCase    :     List[Any]													=	cell.position[1]
					__UpperCAmelCase    :     str													=	[]
					for n in neughbour_cord:
							__UpperCAmelCase    :     Optional[Any]													=	current_x + n[0]
							__UpperCAmelCase    :     Any													=	current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
									__UpperCAmelCase    :     int													=	Cell()
									__UpperCAmelCase    :     List[str]													=	(x, y)
									__UpperCAmelCase    :     List[Any]													=	cell
									neighbours.append(a_ )
					return neighbours

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

		while _open:
				__UpperCAmelCase    :     str													=	np.argmin([n.f for n in _open]						)
				__UpperCAmelCase    :     Optional[Any]													=	_open[min_f]
				_closed.append(_open.pop(_UpperCAmelCase						)						)
				if current == goal:
						break
				for n in world.get_neigbours(_UpperCAmelCase						):
						for c in _closed:
								if c == n:
										continue
						__UpperCAmelCase    :     Optional[Any]													=	current.g + 1
						__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	n.position
						__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	goal.position
						__UpperCAmelCase    :     Union[str, Any]													=	(ya - ya) ** 2 + (xa - xa) ** 2
						__UpperCAmelCase    :     Optional[Any]													=	n.h + n.g

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	[]
		while current.parent is not None:
				path.append(current.position						)
				__UpperCAmelCase    :     int													=	current.parent
		path.append(current.position						)
		return path[::-1]


if __name__ == "__main__":
				__A   			=Gridworld()
				# Start position and goal
				__A   			=Cell()
				__A   			=(0, 0)
				__A   			=Cell()
				__A   			=(4, 4)
				print(f'''path from {start.position} to {goal.position}''')
				__A   			=astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								__A   			=1
				print(world.w)





