



import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
				import torch

				from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Tuple       ,       a_							: Optional[int]       ,       a_							: Optional[int]=99       ,       a_							: Optional[int]=13       ,       a_							: List[Any]=16       ,       a_							: Optional[Any]=7       ,       a_							: Optional[int]=True       ,       a_							: int=True       ,       a_							: List[str]=True       ,       a_							: List[Any]=False       ,       a_							: int=True       ,       a_							: List[str]=2       ,       a_							: str=32       ,       a_							: Any=4       ,       a_							: List[Any]=4       ,       a_							: List[str]=30       ,       a_							: Union[str, Any]=0       ,       a_							: int=1       ,       a_							: Tuple=2       ,       a_							: str=None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	parent
					__UpperCAmelCase    :     List[str]													=	batch_size
					__UpperCAmelCase    :     Tuple													=	decoder_seq_length
					# For common tests
					__UpperCAmelCase    :     str													=	self.decoder_seq_length
					__UpperCAmelCase    :     Union[str, Any]													=	is_training
					__UpperCAmelCase    :     Tuple													=	use_attention_mask
					__UpperCAmelCase    :     Any													=	use_labels

					__UpperCAmelCase    :     List[str]													=	vocab_size
					__UpperCAmelCase    :     Union[str, Any]													=	d_model
					__UpperCAmelCase    :     List[Any]													=	d_model
					__UpperCAmelCase    :     Tuple													=	decoder_layers
					__UpperCAmelCase    :     List[Any]													=	decoder_layers
					__UpperCAmelCase    :     Optional[Any]													=	decoder_ffn_dim
					__UpperCAmelCase    :     Any													=	decoder_attention_heads
					__UpperCAmelCase    :     str													=	decoder_attention_heads
					__UpperCAmelCase    :     Optional[Any]													=	eos_token_id
					__UpperCAmelCase    :     int													=	bos_token_id
					__UpperCAmelCase    :     Optional[int]													=	pad_token_id
					__UpperCAmelCase    :     List[Any]													=	decoder_start_token_id
					__UpperCAmelCase    :     Union[str, Any]													=	use_cache
					__UpperCAmelCase    :     Optional[int]													=	max_position_embeddings

					__UpperCAmelCase    :     Union[str, Any]													=	None
					__UpperCAmelCase    :     int													=	decoder_seq_length
					__UpperCAmelCase    :     List[str]													=	2
					__UpperCAmelCase    :     Union[str, Any]													=	1




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





					__UpperCAmelCase    :     Optional[Any]													=	ids_tensor([self.batch_size, self.decoder_seq_length]       ,       self.vocab_size )

					__UpperCAmelCase    :     Optional[Any]													=	None
					if self.use_attention_mask:
							__UpperCAmelCase    :     Dict													=	ids_tensor([self.batch_size, self.decoder_seq_length]       ,       vocab_size=2 )

					__UpperCAmelCase    :     Dict													=	None
					if self.use_labels:
							__UpperCAmelCase    :     str													=	ids_tensor([self.batch_size, self.decoder_seq_length]       ,       self.vocab_size )

					__UpperCAmelCase    :     Optional[int]													=	TrOCRConfig(
					    vocab_size=self.vocab_size       ,       d_model=self.d_model       ,       decoder_layers=self.decoder_layers       ,       decoder_ffn_dim=self.decoder_ffn_dim       ,       decoder_attention_heads=self.decoder_attention_heads       ,       eos_token_id=self.eos_token_id       ,       bos_token_id=self.bos_token_id       ,       use_cache=self.use_cache       ,       pad_token_id=self.pad_token_id       ,       decoder_start_token_id=self.decoder_start_token_id       ,       max_position_embeddings=self.max_position_embeddings       ,       )

					return (config, input_ids, attention_mask, lm_labels)




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





					__UpperCAmelCase    :     Union[str, Any]													=	True
					__UpperCAmelCase    :     Dict													=	TrOCRDecoder(config=a_ ).to(a_ ).eval()
					__UpperCAmelCase    :     List[Any]													=	input_ids[:2]

					input_ids[input_ids == 0] += 1
					# first forward pass
					__UpperCAmelCase    :     Optional[int]													=	model(a_       ,       use_cache=a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	model(a_ )
					__UpperCAmelCase    :     Optional[Any]													=	model(a_       ,       use_cache=a_ )

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

					__UpperCAmelCase    :     Any													=	outputs['''past_key_values''']

					# create hypothetical next token and extent to next_input_ids
					__UpperCAmelCase    :     List[Any]													=	ids_tensor((2, 1)       ,       config.vocab_size - 1 ) + 1

					# append to next input_ids and
					__UpperCAmelCase    :     Optional[Any]													=	torch.cat([input_ids, next_tokens]       ,       dim=-1 )

					__UpperCAmelCase    :     Union[str, Any]													=	model(a_ )['''last_hidden_state''']
					__UpperCAmelCase    :     Dict													=	model(a_       ,       past_key_values=a_ )['''last_hidden_state''']

					# select random slice
					__UpperCAmelCase    :     Any													=	ids_tensor((1,)       ,       output_from_past.shape[-1] ).item()
					__UpperCAmelCase    :     str													=	output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
					__UpperCAmelCase    :     Optional[int]													=	output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					assert torch.allclose(a_       ,       a_       ,       atol=1e-3 )




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





					__UpperCAmelCase    :     str													=	self.prepare_config_and_inputs()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	config_and_inputs

					__UpperCAmelCase    :     Tuple													=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_torch
class 		UpperCAmelCase__			(			__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
			UpperCamelCase									=       (TrOCRForCausalLM,) if is_torch_available() else ()
			UpperCamelCase									=       {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
			UpperCamelCase									=       True
			UpperCamelCase									=       False




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





					__UpperCAmelCase    :     List[str]													=	TrOCRStandaloneDecoderModelTester(self       ,       is_training=a_ )
					__UpperCAmelCase    :     Dict													=	ConfigTester(self       ,       config_class=a_ )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					pass




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





					pass




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





					pass




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





					self.config_tester.run_common_tests()




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





					__UpperCAmelCase    :     Optional[int]													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past(*a_ )




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





					return




			@unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					pass










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)





