
"""simple docstring"""

import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class __lowerCAmelCase						( snake_case_     ,    unittest.TestCase     ):
     lowercase		  =		KandinskyPriorPipeline
     lowercase		  =		['prompt']
     lowercase		  =		['prompt', 'negative_prompt']
     lowercase		  =		[
         'num_images_per_prompt',
         'generator',
         'num_inference_steps',
         'latents',
         'negative_prompt',
         'guidance_scale',
         'output_type',
         'return_dict',
     ]
     lowercase		  =		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'''




      __UpperCamelCase							    =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
      return tokenizer





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      torch.manual_seed(0 )
      __UpperCamelCase							    =			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(lowerCamelCase_ )





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      torch.manual_seed(0 )

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

      __UpperCamelCase							    =			PriorTransformer(**lowerCamelCase_ )
      # 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
      __UpperCamelCase							    =			nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      torch.manual_seed(0 )
      __UpperCamelCase							    =			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      ,	)

      __UpperCamelCase							    =			CLIPVisionModelWithProjection(lowerCamelCase_ )
      return model





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			CLIPImageProcessor(
          crop_size=224      ,	do_center_crop=lowerCamelCase_      ,	do_normalize=lowerCamelCase_      ,	do_resize=lowerCamelCase_      ,	image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,	image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,	resample=3      ,	size=224      ,	)

      return image_processor





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.dummy_prior
      __UpperCamelCase							    =			self.dummy_image_encoder
      __UpperCamelCase							    =			self.dummy_text_encoder
      __UpperCamelCase							    =			self.dummy_tokenizer
      __UpperCamelCase							    =			self.dummy_image_processor

      __UpperCamelCase							    =			UnCLIPScheduler(
          variance_type='fixed_small_log'      ,	prediction_type='sample'      ,	num_train_timesteps=1000      ,	clip_sample=lowerCamelCase_      ,	clip_sample_range=1_0.0      ,	)

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

      return components





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(lowerCamelCase_ ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(lowerCamelCase_ )
      else:
       __UpperCamelCase							    =			torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
      __UpperCamelCase							    =			{
          'prompt': 'horse',
          'generator': generator,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			'cpu'

      __UpperCamelCase							    =			self.get_dummy_components()

      __UpperCamelCase							    =			self.pipeline_class(**lowerCamelCase_ )
      __UpperCamelCase							    =			pipe.to(lowerCamelCase_ )

      pipe.set_progress_bar_config(disable=lowerCamelCase_ )

      __UpperCamelCase							    =			pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
      __UpperCamelCase							    =			output.image_embeds

      __UpperCamelCase							    =			pipe(
          **self.get_dummy_inputs(lowerCamelCase_ )      ,	return_dict=lowerCamelCase_      ,	)[0]

      __UpperCamelCase							    =			image[0, -10:]
      __UpperCamelCase							    =			image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      __UpperCamelCase							    =			np.array(
          [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

      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'''




      __UpperCamelCase							    =			torch_device == 'cpu'
      __UpperCamelCase							    =			True
      __UpperCamelCase							    =			False

      self._test_inference_batch_single_identical(
          test_max_difference=lowerCamelCase_      ,	relax_max_difference=lowerCamelCase_      ,	test_mean_pixel_difference=lowerCamelCase_      ,	)







     @skip_mps
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			torch_device == 'cpu'
      __UpperCamelCase							    =			False

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











"""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))

