




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
				import torch


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class     A					(	unittest.TestCase					):
							lowercase_           =       ViTImageProcessor if is_vision_available() else None







							@property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							Any:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       (3, 32, 1_28)
														_a			  =       tempfile.mkdtemp()

														# fmt: off
														_a			  =       ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
														# fmt: on
														_a			  =       dict(zip(lowerCAmelCase_							,    range(len(lowerCAmelCase_      )      )      )      )

														_a			  =       os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['''vocab_file''']      )
														with open(self.vocab_file							,    '''w'''							,    encoding='''utf-8'''      ) as fp:
																					fp.write(json.dumps(lowerCAmelCase_      ) + '''\n'''      )

														_a			  =       {
														    '''do_normalize''': False,
														    '''do_resize''': True,
														    '''image_processor_type''': '''ViTImageProcessor''',
														    '''resample''': 3,
														    '''size''': {'''height''': 32, '''width''': 1_28},
														}
														_a			  =       os.path.join(self.tmpdirname							,    lowerCAmelCase_      )
														with open(self.image_processor_file							,    '''w'''							,    encoding='''utf-8'''      ) as fp:
																					json.dump(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str							,    **lowerCAmelCase_ :						int      )    ->							Union[str, Any]:


														"""simple docstring"""


														return MgpstrTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[str]							,    **lowerCAmelCase_ :						Dict      )    ->							Dict:


														"""simple docstring"""


														return ViTImageProcessor.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


														"""simple docstring"""


														shutil.rmtree(self.tmpdirname      )







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       np.random.randint(2_55							,    size=(3, 30, 4_00)							,    dtype=np.uinta      )

														_a			  =       Image.fromarray(np.moveaxis(lowerCAmelCase_							,    0							,    -1      )      )

														return image_input







							def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.get_tokenizer()
														_a			  =       self.get_image_processor()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )
														processor.save_pretrained(self.tmpdirname      )
														_a			  =       MgpstrProcessor.from_pretrained(self.tmpdirname							,    use_fast=lowerCAmelCase_      )

														self.assertEqual(processor.char_tokenizer.get_vocab()							,    tokenizer.get_vocab()      )
														self.assertIsInstance(processor.char_tokenizer							,    lowerCAmelCase_      )

														self.assertEqual(processor.image_processor.to_json_string()							,    image_processor.to_json_string()      )
														self.assertIsInstance(processor.image_processor							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       self.get_tokenizer()
														_a			  =       self.get_image_processor()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )
														processor.save_pretrained(self.tmpdirname      )

														_a			  =       self.get_tokenizer(bos_token='''(BOS)'''							,    eos_token='''(EOS)'''      )
														_a			  =       self.get_image_processor(do_normalize=lowerCAmelCase_							,    padding_value=1.0      )

														_a			  =       MgpstrProcessor.from_pretrained(
														    self.tmpdirname							,    bos_token='''(BOS)'''							,    eos_token='''(EOS)'''							,    do_normalize=lowerCAmelCase_							,    padding_value=1.0      )

														self.assertEqual(processor.char_tokenizer.get_vocab()							,    tokenizer_add_kwargs.get_vocab()      )
														self.assertIsInstance(processor.char_tokenizer							,    lowerCAmelCase_      )

														self.assertEqual(processor.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()      )
														self.assertIsInstance(processor.image_processor							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       self.prepare_image_inputs()

														_a			  =       image_processor(lowerCAmelCase_							,    return_tensors='''np'''      )
														_a			  =       processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

														for key in input_image_proc.keys():
																					self.assertAlmostEqual(input_image_proc[key].sum()							,    input_processor[key].sum()							,    delta=1e-2      )







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


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       '''test'''

														_a			  =       processor(text=lowerCAmelCase_      )

														_a			  =       tokenizer(lowerCAmelCase_      )
														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]							,    encoded_processor[key]      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       '''test'''
														_a			  =       self.prepare_image_inputs()

														_a			  =       processor(text=lowerCAmelCase_							,    images=lowerCAmelCase_      )

														self.assertListEqual(list(inputs.keys()      )							,    ['''pixel_values''', '''labels''']      )

														# test if it raises when no input is passed
														with pytest.raises(lowerCAmelCase_      ):
																					processor()







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


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

														_a			  =       processor.char_decode(lowerCAmelCase_      )
														_a			  =       tokenizer.batch_decode(lowerCAmelCase_      )
														_a			  =       [seq.replace(''' '''							,    ''''''      ) for seq in decoded_tok]

														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       None
														_a			  =       self.prepare_image_inputs()

														_a			  =       processor(text=lowerCAmelCase_							,    images=lowerCAmelCase_      )

														self.assertListEqual(list(inputs.keys()      )							,    processor.model_input_names      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.get_image_processor()
														_a			  =       self.get_tokenizer()

														_a			  =       MgpstrProcessor(tokenizer=lowerCAmelCase_							,    image_processor=lowerCAmelCase_      )

														_a			  =       torch.randn(1							,    27							,    38      )
														_a			  =       torch.randn(1							,    27							,    5_02_57      )
														_a			  =       torch.randn(1							,    27							,    3_05_22      )

														_a			  =       processor.batch_decode([char_input, bpe_input, wp_input]      )

														self.assertListEqual(list(results.keys()      )							,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']      )







'''simple docstring'''


_snake_case   :   Any     							=	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       set()
							# keep track of all the paths to be checked
							_a			  =       [[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
														_a			  =       queue.pop(0   )
														# get the last node from the path
														_a			  =       path[-1]
														if node not in explored:
																					_a			  =       graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												_a			  =       list(UpperCamelCase   )
																												new_path.append(UpperCamelCase   )
																												queue.append(UpperCamelCase   )
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(UpperCamelCase   )

    # in case there's no path between the 2 nodes
							return []
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


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

