



'''simple docstring'''



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  List[str]=9_9  ,       _lowerCAmelCase  :  Dict=1_3  ,       _lowerCAmelCase  :  Tuple=7  ,       _lowerCAmelCase  :  int=9  ,       _lowerCAmelCase  :  Dict=True  ,       _lowerCAmelCase  :  Union[str, Any]=True  ,       _lowerCAmelCase  :  Optional[Any]=False  ,       _lowerCAmelCase  :  Union[str, Any]=3_2  ,       _lowerCAmelCase  :  Optional[int]=5  ,       _lowerCAmelCase  :  Optional[Any]=4  ,       _lowerCAmelCase  :  Any=3_7  ,       _lowerCAmelCase  :  List[str]=8  ,       _lowerCAmelCase  :  Tuple=0.1  ,       _lowerCAmelCase  :  Optional[int]=0.002  ,       _lowerCAmelCase  :  Tuple=1  ,       _lowerCAmelCase  :  Dict=0  ,       _lowerCAmelCase  :  List[Any]=0  ,       _lowerCAmelCase  :  List[str]=None  ,       _lowerCAmelCase  :  int=None  ,       ):







											'''simple docstring'''

											__lowercase       	=parent
											__lowercase       	=batch_size
											__lowercase       	=encoder_seq_length
											__lowercase       	=decoder_seq_length
											# For common tests
											__lowercase       	=self.decoder_seq_length
											__lowercase       	=is_training
											__lowercase       	=use_attention_mask
											__lowercase       	=use_labels
											__lowercase       	=vocab_size
											__lowercase       	=hidden_size
											__lowercase       	=num_hidden_layers
											__lowercase       	=num_attention_heads
											__lowercase       	=d_ff
											__lowercase       	=relative_attention_num_buckets
											__lowercase       	=dropout_rate
											__lowercase       	=initializer_factor
											__lowercase       	=eos_token_id
											__lowercase       	=pad_token_id
											__lowercase       	=decoder_start_token_id
											__lowercase       	=None
											__lowercase       	=decoder_layers

				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											return TaConfig.from_pretrained('google/umt5-base')

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[Any]=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  str=None  ,       _lowerCAmelCase  :  Union[str, Any]=None  ,       _lowerCAmelCase  :  Dict=None  ,       ):







											'''simple docstring'''

											if attention_mask is None:
																		__lowercase       	=input_ids.ne(config.pad_token_id)
											if decoder_attention_mask is None:
																		__lowercase       	=decoder_input_ids.ne(config.pad_token_id)
											if head_mask is None:
																		__lowercase       	=torch.ones(config.num_hidden_layers  ,       config.num_attention_heads  ,       device=_lowerCAmelCase)
											if decoder_head_mask is None:
																		__lowercase       	=torch.ones(config.num_decoder_layers  ,       config.num_attention_heads  ,       device=_lowerCAmelCase)
											if cross_attn_head_mask is None:
																		__lowercase       	=torch.ones(
																		    config.num_decoder_layers  ,       config.num_attention_heads  ,       device=_lowerCAmelCase)
											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,
											}

				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											__lowercase       	=ids_tensor([self.batch_size, self.encoder_seq_length]  ,       self.vocab_size)
											__lowercase       	=ids_tensor([self.batch_size, self.decoder_seq_length]  ,       self.vocab_size)

											# we need to clamp the input ids here to avoid having pad token in between
											# this is because for NllbMoe the position_ids are prepared such that
											# all pad tokens have pos id = 2 and rest are between 2..seq_length
											# and the seq_length here is seq_length - num_pad_tokens
											# but when using past, there is no way of knowing if the past input ids had
											# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
											# position_ids being off by num_pad_tokens in past input
											__lowercase       	=input_ids.clamp(self.pad_token_id + 1)
											__lowercase       	=decoder_input_ids.clamp(self.pad_token_id + 1)

											__lowercase       	=self.get_config()
											__lowercase       	=config.num_attention_heads
											__lowercase       	=self.prepare_inputs_dict(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)
											return config, input_dict

				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self.prepare_config_and_inputs()
											return config, inputs_dict

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return TaConfig(
											    vocab_size=1_6_6  ,       d_model=self.hidden_size  ,       d_ff=self.d_ff  ,       d_kv=self.hidden_size // self.num_attention_heads  ,       num_layers=self.num_hidden_layers  ,       num_decoder_layers=self.decoder_layers  ,       num_heads=self.num_attention_heads  ,       relative_attention_num_buckets=self.relative_attention_num_buckets  ,       dropout_rate=self.dropout_rate  ,       initializer_factor=self.initializer_factor  ,       eos_token_id=self.eos_token_id  ,       bos_token_id=self.pad_token_id  ,       pad_token_id=self.pad_token_id  ,       decoder_start_token_id=self.decoder_start_token_id  ,       )

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return TaConfig(
											    vocab_size=self.vocab_size  ,       d_model=self.hidden_size  ,       d_ff=self.d_ff  ,       d_kv=self.hidden_size // self.num_attention_heads  ,       num_layers=self.num_hidden_layers  ,       num_decoder_layers=self.decoder_layers  ,       num_heads=self.num_attention_heads  ,       relative_attention_num_buckets=self.relative_attention_num_buckets  ,       dropout_rate=self.dropout_rate  ,       initializer_factor=self.initializer_factor  ,       eos_token_id=self.eos_token_id  ,       bos_token_id=self.pad_token_id  ,       pad_token_id=self.pad_token_id  ,       decoder_start_token_id=self.decoder_start_token_id  ,       )

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Optional[Any]  ,       _lowerCAmelCase  :  int  ,       ):







											'''simple docstring'''

											__lowercase       	=UMTaModel(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(
											    input_ids=_lowerCAmelCase  ,       decoder_input_ids=_lowerCAmelCase  ,       attention_mask=_lowerCAmelCase  ,       decoder_attention_mask=_lowerCAmelCase  ,       )
											__lowercase       	=model(input_ids=_lowerCAmelCase  ,       decoder_input_ids=_lowerCAmelCase)
											__lowercase       	=result.last_hidden_state
											__lowercase       	=result.past_key_values
											__lowercase       	=result.encoder_last_hidden_state

											self.parent.assertEqual(encoder_output.size()  ,       (self.batch_size, self.encoder_seq_length, self.hidden_size))
											self.parent.assertEqual(decoder_output.size()  ,       (self.batch_size, self.decoder_seq_length, self.hidden_size))
											# There should be `num_layers` key value embeddings stored in decoder_past
											self.parent.assertEqual(len(_lowerCAmelCase)  ,       config.num_layers)
											# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
											self.parent.assertEqual(len(decoder_past[0])  ,       4)

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  List[str]  ,       ):







											'''simple docstring'''

											__lowercase       	=UMTaModel(config=_lowerCAmelCase).get_decoder().to(_lowerCAmelCase).eval()
											# first forward pass
											__lowercase       	=model(_lowerCAmelCase  ,       use_cache=_lowerCAmelCase)
											__lowercase       	=model(_lowerCAmelCase)
											__lowercase       	=model(_lowerCAmelCase  ,       use_cache=_lowerCAmelCase)

											self.parent.assertTrue(len(_lowerCAmelCase) == len(_lowerCAmelCase))
											self.parent.assertTrue(len(_lowerCAmelCase) == len(_lowerCAmelCase) + 1)

											__lowercase	,				__lowercase       	=outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											__lowercase       	=ids_tensor((self.batch_size, 1)  ,       config.vocab_size)

											# append to next input_ids and
											__lowercase       	=torch.cat([input_ids, next_tokens]  ,       dim=-1)

											__lowercase       	=model(_lowerCAmelCase)['last_hidden_state']
											__lowercase       	=model(_lowerCAmelCase  ,       past_key_values=_lowerCAmelCase)['last_hidden_state']

											# select random slice
											__lowercase       	=ids_tensor((1,)  ,       output_from_past.shape[-1]).item()
											__lowercase       	=output_from_no_past[:, -1, random_slice_idx].detach()
											__lowercase       	=output_from_past[:, 0, random_slice_idx].detach()

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(_lowerCAmelCase  ,       _lowerCAmelCase  ,       atol=1e-3))

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  List[str]  ,       ):







											'''simple docstring'''

											__lowercase       	=UMTaModel(config=_lowerCAmelCase).to(_lowerCAmelCase).half().eval()
											__lowercase       	=model(**_lowerCAmelCase)['last_hidden_state']
											self.parent.assertFalse(torch.isnan(_lowerCAmelCase).any().item())





@require_torch
class _UpperCamelCase		(     A      ,       A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				lowerCAmelCase__					    =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
				lowerCAmelCase__					    =    (
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCAmelCase__					    =    True
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    True
				lowerCAmelCase__					    =    True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				lowerCAmelCase__					    =    [0.8, 0.9]

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=UMTaModelTester(self)

				@unittest.skip('Test has a segmentation fault on torch 1.8.0')
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											__lowercase       	=UMTaModel(config_and_inputs[0]).to(_lowerCAmelCase)
											with tempfile.TemporaryDirectory() as tmpdirname:
																		torch.onnx.export(
																		    _lowerCAmelCase  ,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,       f"""{tmpdirname}/t5_test.onnx"""  ,       export_params=_lowerCAmelCase  ,       opset_version=9  ,       input_names=['input_ids', 'decoder_input_ids']  ,       )

				@unittest.skipIf(torch_device == 'cpu'  ,       'Cant do half precision')
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											__lowercase       	=['encoder_attentions', 'decoder_attentions', 'cross_attentions']
											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											__lowercase       	=config_and_inputs[0]
											__lowercase       	=UMTaForConditionalGeneration(_lowerCAmelCase).eval()
											model.to(_lowerCAmelCase)

											__lowercase       	={
											    'head_mask': torch.zeros(config.num_layers  ,       config.num_heads  ,       device=_lowerCAmelCase),
											    'decoder_head_mask': torch.zeros(config.num_decoder_layers  ,       config.num_heads  ,       device=_lowerCAmelCase),
											    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers  ,       config.num_heads  ,       device=_lowerCAmelCase),
											}

											for attn_name, (name, mask) in zip(_lowerCAmelCase  ,       head_masking.items()):
																		__lowercase       	={name: mask}
																		# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																		if name == "head_mask":
																									__lowercase       	=torch.ones(
																									    config.num_decoder_layers  ,       config.num_heads  ,       device=_lowerCAmelCase)

																		__lowercase       	=model.generate(
																		    config_and_inputs[1]['input_ids']  ,       num_beams=1  ,       max_length=3  ,       output_attentions=_lowerCAmelCase  ,       return_dict_in_generate=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
																		# We check the state of decoder_attentions and cross_attentions just from the last step
																		__lowercase       	=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																		self.assertEqual(sum([w.sum().item() for w in attn_weights])  ,       0.0)

				@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.')
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											pass





@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				@slow
				@unittest.skip(
				    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged')
				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,       return_dict=_lowerCAmelCase).to(_lowerCAmelCase)
											__lowercase       	=AutoTokenizer.from_pretrained('google/umt5-small'  ,       use_fast=_lowerCAmelCase  ,       legacy=_lowerCAmelCase)
											__lowercase       	=[
											    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
											    'No se como puedo <extra_id_0>.',
											    'This is the reason why we <extra_id_0> them.',
											    'The <extra_id_0> walks in <extra_id_1>, seats',
											    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
											]
											__lowercase       	=tokenizer(_lowerCAmelCase  ,       return_tensors='pt'  ,       padding=_lowerCAmelCase).input_ids
											# fmt: off
											__lowercase       	=torch.tensor(
											    [
											        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
											    ])
											# fmt: on
											torch.testing.assert_allclose(_lowerCAmelCase  ,       _lowerCAmelCase)

											__lowercase       	=model.generate(input_ids.to(_lowerCAmelCase))
											__lowercase       	=[
											    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
											    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											]
											__lowercase       	=tokenizer.batch_decode(_lowerCAmelCase)
											self.assertEqual(_lowerCAmelCase  ,       _lowerCAmelCase)








'''simple docstring'''



import numpy as np





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=(0, 0)
											__lowercase       	=None
											__lowercase       	=0
											__lowercase       	=0
											__lowercase       	=0

				def __eq__(							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.position == cell.position

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											print(self.position)





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  List[str]=(5, 5)):







											'''simple docstring'''

											__lowercase       	=np.zeros(_lowerCAmelCase)
											__lowercase       	=world_size[0]
											__lowercase       	=world_size[1]

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											print(self.w)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[str]):







											'''simple docstring'''

											__lowercase       	=[
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											__lowercase       	=cell.position[0]
											__lowercase       	=cell.position[1]
											__lowercase       	=[]
											for n in neughbour_cord:
																		__lowercase       	=current_x + n[0]
																		__lowercase       	=current_y + n[1]
																		if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																									__lowercase       	=Cell()
																									__lowercase       	=(x, y)
																									__lowercase       	=cell
																									neighbours.append(_lowerCAmelCase)
											return neighbours



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=[]
							_open.append(_lowerCAmelCase					)

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

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


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



