import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     __UpperCAmelCase(    A__      ,	unittest.TestCase				):






 """simple docstring"""




 __magic_name__          =   KandinskyVaaPriorEmbaEmbPipeline
 __magic_name__          =   ["""prompt""", """image"""]
 __magic_name__          =   ["""prompt""", """image"""]
 __magic_name__          =   [
     """num_images_per_prompt""",
     """strength""",
     """generator""",
     """num_inference_steps""",
     """latents""",
     """negative_prompt""",
     """guidance_scale""",
     """output_type""",
     """return_dict""",
 ]
 __magic_name__          =   False
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return 32
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return 32
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return self.time_input_dim
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return self.time_input_dim * 4
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return 100
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
        return tokenizer
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        torch.manual_seed(0      )
        A_							:       Optional[int]	      =				CLIPTextConfig(
            bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=self.text_embedder_hidden_size ,       projection_dim=self.text_embedder_hidden_size ,       intermediate_size=37 ,       layer_norm_eps=1e-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1000 ,       )
        return CLIPTextModelWithProjection(__magic_name__      )
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        torch.manual_seed(0      )

        A_							:       Any	      =				{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        A_							:       str	      =				PriorTransformer(**__magic_name__      )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        A_							:       Dict	      =				nn.Parameter(torch.ones(model.clip_std.shape      )      )
        return model
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        torch.manual_seed(0      )
        A_							:       List[str]	      =				CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size ,       image_size=224 ,       projection_dim=self.text_embedder_hidden_size ,       intermediate_size=37 ,       num_attention_heads=4 ,       num_channels=3 ,       num_hidden_layers=5 ,       patch_size=14 ,       )

        A_							:       Union[str, Any]	      =				CLIPVisionModelWithProjection(__magic_name__      )
        return model
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				CLIPImageProcessor(
            crop_size=224 ,       do_center_crop=__magic_name__ ,       do_normalize=__magic_name__ ,       do_resize=__magic_name__ ,       image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,       image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,       resample=3 ,       size=224 ,       )

        return image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				self.dummy_prior
        A_							:       Any	      =				self.dummy_image_encoder
        A_							:       Dict	      =				self.dummy_text_encoder
        A_							:       str	      =				self.dummy_tokenizer
        A_							:       Union[str, Any]	      =				self.dummy_image_processor

        A_							:       List[Any]	      =				UnCLIPScheduler(
            variance_type='''fixed_small_log''' ,       prediction_type='''sample''' ,       num_train_timesteps=1000 ,       clip_sample=__magic_name__ ,       clip_sample_range=10.0 ,       )

        A_							:       str	      =				{
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''scheduler''': scheduler,
            '''image_processor''': image_processor,
        }

        return components
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=0      ):

        """simple docstring"""

        if str(__magic_name__      ).startswith('''mps'''      ):
               A_							:       Optional[Any]	      =				torch.manual_seed(__magic_name__      )
        else:
               A_							:       Dict	      =				torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )

        A_							:       str	      =				floats_tensor((1, 3, 64, 64) ,       rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        A_							:       Optional[Any]	      =				image.cpu().permute(0 ,       2 ,       3 ,       1      )[0]
        A_							:       Dict	      =				Image.fromarray(np.uinta(__magic_name__      )      ).convert('''RGB'''      ).resize((256, 256)      )

        A_							:       Optional[int]	      =				{
            '''prompt''': '''horse''',
            '''image''': init_image,
            '''strength''': 0.5,
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				'''cpu'''

        A_							:       Any	      =				self.get_dummy_components()

        A_							:       List[Any]	      =				self.pipeline_class(**__magic_name__      )
        A_							:       Dict	      =				pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        A_							:       List[str]	      =				pipe(**self.get_dummy_inputs(__magic_name__      )      )
        A_							:       List[Any]	      =				output.image_embeds

        A_							:       Dict	      =				pipe(
            **self.get_dummy_inputs(__magic_name__      ) ,       return_dict=__magic_name__ ,       )[0]

        A_							:       Dict	      =				image[0, -10:]
        A_							:       Optional[int]	      =				image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        A_							:       Optional[int]	      =				np.array(
            [
                0.1_07_12_84,
                1.3_33_02_71,
                0.61_26_02_23,
                -0.6_69_10_65,
                -0.3_84_68_52,
                -1.0_30_36_61,
                0.22_71_61_11,
                0.03_34_89_01,
                0.30_04_06_75,
                -0.24_80_50_29,
            ]      )

        assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
 @skip_mps
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				torch_device == '''cpu'''
        A_							:       int	      =				True
        A_							:       Any	      =				False

        self._test_inference_batch_single_identical(
            test_max_difference=__magic_name__ ,       relax_max_difference=__magic_name__ ,       test_mean_pixel_difference=__magic_name__ ,       )
 @skip_mps
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				torch_device == '''cpu'''
        A_							:       Dict	      =				False

        self._test_attention_slicing_forward_pass(
            test_max_difference=__magic_name__ ,       test_mean_pixel_difference=__magic_name__ ,       )



def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

       for i in range(1  ,   len_source_seq + 1							):
              A_							:       List[Any]	      =				i * delete_cost
              A_							:       List[Any]	      =				f"""D{source_seq[i - 1]:c}"""

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       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]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            A_							:       Dict	      =				costs[i - 1][j] + delete_cost
                            A_							:       Optional[int]	      =				f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            A_							:       List[Any]	      =				costs[i][j - 1] + insert_cost
                            A_							:       Tuple	      =				f"""I{destination_seq[j - 1]:c}"""

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       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":
                _lowerCAmelCase        =       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))


