
'''simple docstring'''
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 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_						,				A_=99						,				A_=13						,				A_=16						,				A_=7						,				A_=True						,				A_=True						,				A_=True						,				A_=False						,				A_=True						,				A_=2						,				A_=32						,				A_=4						,				A_=4						,				A_=30						,				A_=0						,				A_=1						,				A_=2						,				A_=None						,				)->		str:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     decoder_seq_length
											# For common tests
											UpperCamelCase   			=     self.decoder_seq_length
											UpperCamelCase   			=     is_training
											UpperCamelCase   			=     use_attention_mask
											UpperCamelCase   			=     use_labels

											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     decoder_layers
											UpperCamelCase   			=     decoder_layers
											UpperCamelCase   			=     decoder_ffn_dim
											UpperCamelCase   			=     decoder_attention_heads
											UpperCamelCase   			=     decoder_attention_heads
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     decoder_start_token_id
											UpperCamelCase   			=     use_cache
											UpperCamelCase   			=     max_position_embeddings

											UpperCamelCase   			=     None
											UpperCamelCase   			=     decoder_seq_length
											UpperCamelCase   			=     2
											UpperCamelCase   			=     1







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

											UpperCamelCase   			=     None
											if self.use_attention_mask:
																		UpperCamelCase   			=     ids_tensor([self.batch_size, self.decoder_seq_length]						,				vocab_size=2     )

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

											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_						,				A_						,				)->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     True
											UpperCamelCase   			=     TrOCRDecoder(config=A_     ).to(A_     ).eval()
											UpperCamelCase   			=     input_ids[:2]

											input_ids[input_ids == 0] += 1
											# first forward pass
											UpperCamelCase   			=     model(A_						,				use_cache=A_     )
											UpperCamelCase   			=     model(A_     )
											UpperCamelCase   			=     model(A_						,				use_cache=A_     )

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

											UpperCamelCase   			=     outputs['past_key_values']

											# create hypothetical next token and extent to next_input_ids
											UpperCamelCase   			=     ids_tensor((2, 1)						,				config.vocab_size - 1     ) + 1

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

											UpperCamelCase   			=     model(A_     )['last_hidden_state']
											UpperCamelCase   			=     model(A_						,				past_key_values=A_     )['last_hidden_state']

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

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







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self.prepare_config_and_inputs()
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     config_and_inputs

											UpperCamelCase   			=     {'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
				lowerCAmelCase_     			=							(TrOCRForCausalLM,) if is_torch_available() else ()
				lowerCAmelCase_     			=							{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
				lowerCAmelCase_     			=							True
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     TrOCRStandaloneDecoderModelTester(self						,				is_training=A_     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											self.config_tester.run_common_tests()







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											return







				@unittest.skip('The model doesn\'t support left padding'     )  # and it's not used enough to be worth fixing :)
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											pass






'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

    # in case there's no path between the 2 nodes
							return []







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Tuple):
							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							UpperCamelCase   			=     [start]
							UpperCamelCase   			=     set(A)
							# Keep tab on distances from `start` node.
							UpperCamelCase   			=     {start: 0, target: -1}
							while queue:
														UpperCamelCase   			=     queue.pop(0)
														if node == target:
																					UpperCamelCase   			=     (
																					    dist[node] if dist[target] == -1 else min(dist[target]		,     dist[node])
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(A)
																												queue.append(A)
																												UpperCamelCase   			=     dist[node] + 1
							return dist[target]


if __name__ == "__main__":
						print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
						print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4




