

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       KandinskyInpaintPipeline
  snake_case_				       =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
  snake_case_				       =       [
      """prompt""",
      """negative_prompt""",
      """image_embeds""",
      """negative_image_embeds""",
      """image""",
      """mask_image""",
  ]
  snake_case_				       =       [
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """negative_prompt""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  snake_case_				       =       False

  @property
  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   return 32

  @property
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   return 32

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return self.time_input_dim

  @property
  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   return self.time_input_dim * 4

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return 100

  @property
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""    )
   return tokenizer

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  List[Any]					   =					MCLIPConfig(
       numDims=self.cross_attention_dim					,transformerDimensions=self.text_embedder_hidden_size					,hidden_size=self.text_embedder_hidden_size					,intermediate_size=37					,num_attention_heads=4					,num_hidden_layers=5					,vocab_size=1_005					,)

   UpperCAmelCase__					:  Optional[int]					   =					MultilingualCLIP(A    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder.eval()

   return text_encoder

  @property
  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   torch.manual_seed(0    )

   UpperCAmelCase__					:  List[str]					   =					{
       """in_channels""": 9,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 8,
       """addition_embed_type""": """text_image""",
       """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
       """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
       """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
       """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
       """layers_per_block""": 1,
       """encoder_hid_dim""": self.text_embedder_hidden_size,
       """encoder_hid_dim_type""": """text_image_proj""",
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": None,
   }

   UpperCAmelCase__					:  str					   =					UNetaDConditionModel(**A    )
   return model

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Optional[int]					   =					VQModel(**self.dummy_movq_kwargs    )
   return model

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					self.dummy_text_encoder
   UpperCAmelCase__					:  Union[str, Any]					   =					self.dummy_tokenizer
   UpperCAmelCase__					:  str					   =					self.dummy_unet
   UpperCAmelCase__					:  Union[str, Any]					   =					self.dummy_movq

   UpperCAmelCase__					:  Optional[Any]					   =					DDIMScheduler(
       num_train_timesteps=1_000					,beta_schedule="""linear"""					,beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,clip_sample=A					,set_alpha_to_one=A					,steps_offset=1					,prediction_type="""epsilon"""					,thresholding=A					,)

   UpperCAmelCase__					:  Optional[Any]					   =					{
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }

   return components

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]					,A		:			List[Any]=0    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					floats_tensor((1, self.cross_attention_dim)					,rng=random.Random(A    )    ).to(A    )
   UpperCAmelCase__					:  Tuple					   =					floats_tensor((1, self.cross_attention_dim)					,rng=random.Random(seed + 1    )    ).to(A    )
   # create init_image
   UpperCAmelCase__					:  Optional[int]					   =					floats_tensor((1, 3, 64, 64)					,rng=random.Random(A    )    ).to(A    )
   UpperCAmelCase__					:  int					   =					image.cpu().permute(0					,2					,3					,1    )[0]
   UpperCAmelCase__					:  Union[str, Any]					   =					Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    ).resize((256, 256)    )
   # create mask
   UpperCAmelCase__					:  Dict					   =					np.ones((64, 64)					,dtype=np.floataa    )
   UpperCAmelCase__					:  int					   =					0

   if str(A    ).startswith("""mps"""    ):
    UpperCAmelCase__					:  List[Any]					   =					torch.manual_seed(A    )
   else:
    UpperCAmelCase__					:  List[Any]					   =					torch.Generator(device=A    ).manual_seed(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					{
       """prompt""": """horse""",
       """image""": init_image,
       """mask_image""": mask,
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """num_inference_steps""": 2,
       """guidance_scale""": 4.0,
       """output_type""": """np""",
   }
   return inputs

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					"""cpu"""

   UpperCAmelCase__					:  Dict					   =					self.get_dummy_components()

   UpperCAmelCase__					:  Union[str, Any]					   =					self.pipeline_class(**A    )
   UpperCAmelCase__					:  Any					   =					pipe.to(A    )

   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  Union[str, Any]					   =					pipe(**self.get_dummy_inputs(A    )    )
   UpperCAmelCase__					:  Optional[int]					   =					output.images

   UpperCAmelCase__					:  Optional[Any]					   =					pipe(
       **self.get_dummy_inputs(A    )					,return_dict=A					,)[0]

   UpperCAmelCase__					:  int					   =					image[0, -3:, -3:, -1]
   UpperCAmelCase__					:  Dict					   =					image_from_tuple[0, -3:, -3:, -1]

   print(f"image.shape {image.shape}"    )

   assert image.shape == (1, 64, 64, 3)

   UpperCAmelCase__					:  Optional[int]					   =					np.array(
       [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]    )

   assert (
       np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
   ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
   ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"




  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







@slow
@require_torch_gpu
class      __lowercase			(    unittest.TestCase					):

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""    )

   UpperCAmelCase__					:  int					   =					load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
   UpperCAmelCase__					:  str					   =					np.ones((768, 768)					,dtype=np.floataa    )
   UpperCAmelCase__					:  Union[str, Any]					   =					0

   UpperCAmelCase__					:  int					   =					"""a hat"""

   UpperCAmelCase__					:  Union[str, Any]					   =					KandinskyPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-prior"""					,torch_dtype=torch.floataa    )
   pipe_prior.to(A    )

   UpperCAmelCase__					:  Union[str, Any]					   =					KandinskyInpaintPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-inpaint"""					,torch_dtype=torch.floataa    )
   UpperCAmelCase__					:  Union[str, Any]					   =					pipeline.to(A    )
   pipeline.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  int					   =					torch.Generator(device="""cpu"""    ).manual_seed(0    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					pipe_prior(
       A					,generator=A					,num_inference_steps=5					,negative_prompt=""""""					,).to_tuple()

   UpperCAmelCase__					:  Optional[int]					   =					pipeline(
       A					,image=A					,mask_image=A					,image_embeds=A					,negative_image_embeds=A					,generator=A					,num_inference_steps=100					,height=768					,width=768					,output_type="""np"""					,)

   UpperCAmelCase__					:  List[str]					   =					output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(A					,A    )








"""simple docstring"""




import argparse
import os
import re

import packaging.version


__UpperCAmelCase =				'examples/'
__UpperCAmelCase =				{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__UpperCAmelCase =				{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
__UpperCAmelCase =				'README.md'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  Dict					   =					f.read()
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					REPLACE_PATTERNS[pattern]
 UpperCAmelCase__					:  List[str]					   =					replace.replace("""VERSION"""							,    __UpperCamelCase      )
 UpperCAmelCase__					:  Tuple					   =					re_pattern.sub(__UpperCamelCase							,    __UpperCamelCase      )
 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.write(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for folder, directories, fnames in os.walk(__UpperCamelCase      ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove("""research_projects"""      )
  if "legacy" in directories:
   directories.remove("""legacy"""      )
  for fname in fnames:
   if fname.endswith(""".py"""      ):
    update_version_in_file(os.path.join(__UpperCamelCase							,    __UpperCamelCase      )							,    __UpperCamelCase							,    pattern="""examples"""      )
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=False      ):
 '''simple docstring'''





 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )
 if not patch:
  update_version_in_examples(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					"""🤗 Transformers currently provides the following architectures"""
 UpperCAmelCase__					:  Union[str, Any]					   =					"""1. Want to contribute a new model?"""
 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  str					   =					f.readlines()

 # Find the start of the list.
 UpperCAmelCase__					:  List[Any]					   =					0
 while not lines[start_index].startswith(_start_prompt      ):
  start_index += 1
 start_index += 1

 UpperCAmelCase__					:  int					   =					start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt      ):
  if lines[index].startswith("""1."""      ):
   UpperCAmelCase__					:  List[Any]					   =					lines[index].replace(
       """https://huggingface.co/docs/transformers/main/model_doc"""							,    """https://huggingface.co/docs/transformers/model_doc"""							,    )
  index += 1

 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.writelines(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 with open(REPLACE_FILES["""init"""]							,    """r"""      ) as f:
  UpperCAmelCase__					:  Optional[Any]					   =					f.read()
 UpperCAmelCase__					:  int					   =					REPLACE_PATTERNS["""init"""][0].search(__UpperCamelCase      ).groups()[0]
 return packaging.version.parse(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase=False      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					get_version()
 if patch and default_version.is_devrelease:
  raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
 if default_version.is_devrelease:
  UpperCAmelCase__					:  List[str]					   =					default_version.base_version
 elif patch:
  UpperCAmelCase__					:  Any					   =					F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  UpperCAmelCase__					:  Optional[Any]					   =					F"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 UpperCAmelCase__					:  Optional[int]					   =					input(F"Which version are you releasing? [{default_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  Union[str, Any]					   =					default_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase							,    patch=__UpperCamelCase      )
 if not patch:
  print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
  clean_main_ref_in_model_list()
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					get_version()
 UpperCAmelCase__					:  Any					   =					F"{current_version.major}.{current_version.minor + 1}.0.dev0"
 UpperCAmelCase__					:  List[Any]					   =					current_version.base_version

 # Check with the user we got that right.
 UpperCAmelCase__					:  Dict					   =					input(F"Which version are we developing now? [{dev_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  List[Any]					   =					dev_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase      )
 print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      __UpperCAmelCase =				parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()





