




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import (
      AutoProcessor,
      BertTokenizerFast,
      BlipImageProcessor,
      GPTaTokenizer,
      InstructBlipProcessor,
      PreTrainedTokenizerFast,
  )







@require_vision
class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     str			=						tempfile.mkdtemp()

           _A							:     List[Any]			=						BlipImageProcessor()
           _A							:     Optional[Any]			=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)
           _A							:     Tuple			=						BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)

           _A							:     Any			=						InstructBlipProcessor(_a		,     _a		,     _a				)

           processor.save_pretrained(self.tmpdirname				)

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).tokenizer

     def 					a__	(  self		,     **_a				)						->							int:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).image_processor

     def 					a__	(  self		,     **_a				)						->							Any:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).qformer_tokenizer

     def 					a__	(  self				)						->							int:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     int			=						[np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

           _A							:     Any			=						[Image.fromarray(np.moveaxis(_a		,     0		,     -1				)				) for x in image_inputs]

           return image_inputs

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Tuple			=						InstructBlipProcessor(
               tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()		,     qformer_tokenizer=self.get_qformer_tokenizer()		,     )
           processor.save_pretrained(self.tmpdirname				)

           _A							:     Union[str, Any]			=						self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)"""				)
           _A							:     Tuple			=						self.get_image_processor(do_normalize=_a		,     padding_value=1.0				)

           _A							:     Tuple			=						InstructBlipProcessor.from_pretrained(
               self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=_a		,     padding_value=1.0				)

           self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,     _a				)

           self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,     _a				)
           self.assertIsInstance(processor.qformer_tokenizer		,     _a				)

     def 					a__	(  self				)						->							Dict:
           _A							:     int			=						self.get_image_processor()
           _A							:     Dict			=						self.get_tokenizer()
           _A							:     Dict			=						self.get_qformer_tokenizer()

           _A							:     int			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Optional[Any]			=						self.prepare_image_inputs()

           _A							:     List[Any]			=						image_processor(_a		,     return_tensors="""np"""				)
           _A							:     str			=						processor(images=_a		,     return_tensors="""np"""				)

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

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Optional[int]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()
           _A							:     List[Any]			=						self.get_qformer_tokenizer()

           _A							:     List[Any]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Optional[int]			=						"""lower newer"""

           _A							:     List[str]			=						processor(text=_a				)

           _A							:     str			=						tokenizer(_a		,     return_token_type_ids=_a				)
           _A							:     List[str]			=						qformer_tokenizer(_a		,     return_token_type_ids=_a				)

           for key in encoded_tokens.keys():
                 self.assertListEqual(encoded_tokens[key]		,     encoded_processor[key]				)

           for key in encoded_tokens_qformer.keys():
                 self.assertListEqual(encoded_tokens_qformer[key]		,     encoded_processor["""qformer_""" + key]				)

     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()
           _A							:     Dict			=						self.get_qformer_tokenizer()

           _A							:     List[str]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Tuple			=						"""lower newer"""
           _A							:     Optional[int]			=						self.prepare_image_inputs()

           _A							:     Any			=						processor(text=_a		,     images=_a				)

           self.assertListEqual(
               list(inputs.keys()				)		,     ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,     )

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

     def 					a__	(  self				)						->							Dict:
           _A							:     Dict			=						self.get_image_processor()
           _A							:     Any			=						self.get_tokenizer()
           _A							:     str			=						self.get_qformer_tokenizer()

           _A							:     int			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Tuple			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:     List[str]			=						processor.batch_decode(_a				)
           _A							:     Optional[Any]			=						tokenizer.batch_decode(_a				)

           self.assertListEqual(_a		,     _a				)



     def 					a__	(  self				)						->							str:
           _A							:     List[Any]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()
           _A							:     int			=						self.get_qformer_tokenizer()

           _A							:     List[Any]			=						InstructBlipProcessor(
               tokenizer=_a		,     image_processor=_a		,     qformer_tokenizer=_a				)

           _A							:     Dict			=						"""lower newer"""
           _A							:     int			=						self.prepare_image_inputs()

           _A							:     int			=						processor(text=_a		,     images=_a				)

           self.assertListEqual(
               list(inputs.keys()				)		,     ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,     )






'''simple docstring'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



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

            # mark node as explored
						explored.add(lowerCAmelCase_  )

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		_a     :      Optional[int]        =							[start]
		_a     :      Dict        =							set(lowerCAmelCase_  )
		# Keep tab on distances from `start` node.
		_a     :      Dict        =							{start: 0, target: -1}
		while queue:
				_a     :      List[str]        =							queue.pop(0  )
				if node == target:
						_a     :      Any        =							(
						    dist[node] if dist[target] == -1 else min(dist[target]						,			dist[node]  )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								_a     :      Any        =							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




