




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       )		->							int:
           _A							:  Tuple    =		tempfile.mkdtemp()

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

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

           processor.save_pretrained(self.tmpdirname       )

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

     def       a__			(						self	,  **_a       )		->							Union[str, Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname	,  **_a       ).image_processor

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

     def       a__			(						self       )		->							List[Any]:
           shutil.rmtree(self.tmpdirname       )

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

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

           return image_inputs

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

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

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

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

           _A							:  Union[str, Any]    =		self.prepare_image_inputs()

           _A							:  str    =		image_processor(_a	,  return_tensors="""np"""       )
           _A							:  Optional[int]    =		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       )		->							Any:
           _A							:  Optional[int]    =		self.get_image_processor()
           _A							:  Tuple    =		self.get_tokenizer()
           _A							:  Any    =		self.get_qformer_tokenizer()

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

           _A							:  Union[str, Any]    =		"""lower newer"""

           _A							:  Dict    =		processor(text=_a       )

           _A							:  Optional[int]    =		tokenizer(_a	,  return_token_type_ids=_a       )
           _A							:  int    =		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       )		->							Tuple:
           _A							:  Dict    =		self.get_image_processor()
           _A							:  str    =		self.get_tokenizer()
           _A							:  List[Any]    =		self.get_qformer_tokenizer()

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

           _A							:  Any    =		"""lower newer"""
           _A							:  Union[str, Any]    =		self.prepare_image_inputs()

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

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

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

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

           self.assertListEqual(_a	,  _a       )



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

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

           _A							:  Union[str, Any]    =		"""lower newer"""
           _A							:  Any    =		self.prepare_image_inputs()

           _A							:  List[Any]    =		processor(text=_a	,  images=_a       )

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







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

