






"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 						a_ (      snake_case_    ,		snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							int					=      IFImgaImgPipeline
      lowerCamelCase__			:							Union[str, Any]					=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
      lowerCamelCase__			:							int					=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase__			:							Any					=      PipelineTesterMixin.required_optional_params - {'latents'}




      def      a__				(self   ):

       '''simple docstring'''



       return self._get_dummy_components()




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=0   ):

       '''simple docstring'''



       if str(lowerCamelCase_   ).startswith('mps'   ):
        lowerCamelCase__	:     Optional[int]								=		torch.manual_seed(lowerCamelCase_   )
       else:
        lowerCamelCase__	:     List[str]								=		torch.Generator(device=lowerCamelCase_   ).manual_seed(lowerCamelCase_   )

       lowerCamelCase__	:     Dict								=		floats_tensor((1, 3, 3_2, 3_2),	rng=random.Random(lowerCamelCase_   )   ).to(lowerCamelCase_   )

       lowerCamelCase__	:     List[Any]								=		{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'output_type': 'numpy',
       }

       return inputs




      def      a__				(self   ):

       '''simple docstring'''



       self._test_save_load_optional_components()




      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available(),	reason='XFormers attention is only available with CUDA and `xformers` installed',	)
      def      a__				(self   ):

       '''simple docstring'''



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )




      @unittest.skipIf(torch_device != 'cuda',	reason='float16 requires CUDA'   )
      def      a__				(self   ):

       '''simple docstring'''



       super().test_save_load_floataa(expected_max_diff=1e-1   )




      @unittest.skipIf(torch_device != 'cuda',	reason='float16 requires CUDA'   )
      def      a__				(self   ):

       '''simple docstring'''



       super().test_floataa_inference(expected_max_diff=1e-1   )




      def      a__				(self   ):

       '''simple docstring'''



       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )




      def      a__				(self   ):

       '''simple docstring'''



       self._test_save_load_local()





      def      a__				(self   ):

       '''simple docstring'''



       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2,	)









"""simple docstring"""
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  ):
 lowerCamelCase__	:     Any								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     int								=		len(_lowerCamelCase							)
 lowerCamelCase__	:     Optional[int]								=		len(_lowerCamelCase							)

 lowerCamelCase__	:     Tuple								=		[
     [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]
 lowerCamelCase__	:     Dict								=		[
     ['0' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]

 for i in range(1     ,  len_source_seq + 1							):
  lowerCamelCase__	:     Optional[int]								=		i * delete_cost
  lowerCamelCase__	:     List[str]								=		f'''D{source_seq[i - 1]:c}'''

 for i in range(1     ,  len_destination_seq + 1							):
  lowerCamelCase__	:     int								=		i * insert_cost
  lowerCamelCase__	:     str								=		f'''I{destination_seq[i - 1]:c}'''

 for i in range(1     ,  len_source_seq + 1							):
  for j in range(1     ,  len_destination_seq + 1							):
   if source_seq[i - 1] == destination_seq[j - 1]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j - 1] + copy_cost
    lowerCamelCase__	:     str								=		f'''C{source_seq[i - 1]:c}'''
   else:
    lowerCamelCase__	:     Tuple								=		costs[i - 1][j - 1] + replace_cost
    lowerCamelCase__	:     List[str]								=		f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j] + delete_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    lowerCamelCase__	:     Optional[Any]								=		costs[i][j - 1] + insert_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''I{destination_seq[j - 1]:c}'''

 return costs, ops






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   lowerCamelCase__	:     List[str]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq
  elif ops[i][j][0] == "D":
   lowerCamelCase__	:     Union[str, Any]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  _lowerCamelCase							)
   seq.append(ops[i][j]							)
   return seq
  else:
   lowerCamelCase__	:     Tuple								=		assemble_transformation(_lowerCamelCase     ,  _lowerCamelCase     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq


if __name__ == "__main__":
      A_,			A_     :					List[str]							=     compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

      A_     :					str							=     len(operations)
      A_     :					Dict							=     len(operations[0])
      A_     :					Any							=     assemble_transformation(operations, m - 1, n - 1)

      A_     :					int							=     list("Python")
      A_     :					Optional[Any]							=     0
      A_     :					Tuple							=     0

      with open("min_cost.txt", "w") as file:
            for op in sequence:
                  print("".join(string))

                  if op[0] == "C":
                        file.write("%-16s" % "Copy %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost -= 1
                  elif op[0] == "R":
                        A_     :					str							=     op[2]

                        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
                        file.write("\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write("%-16s" % "Delete %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write("%-16s" % "Insert %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2

                  i += 1

            print("".join(string))
            print("Cost: ", cost)

            file.write("\r\nMinimum cost: " + str(cost))

