

"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_A							=  logging.getLogger()

@unittest.skip('Temporarily disable the doc tests.'   )
@require_torch
@require_tf
@slow
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = True							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    [file for file in os.listdir(_lowerCamelCase	) if os.path.isfile(os.path.join(_lowerCamelCase							,       _lowerCamelCase	)	)]

        if identifier is not None:
              UpperCAmelCase__			:							Union[str, Any]							=    [file for file in files if identifier in file]

        if n_identifier is not None:
              if isinstance(_lowerCamelCase							,       _lowerCamelCase	):
                    for n_ in n_identifier:
                          UpperCAmelCase__			:							str							=    [file for file in files if n_ not in file]
              else:
                    UpperCAmelCase__			:							Dict							=    [file for file in files if n_identifier not in file]

        UpperCAmelCase__			:							List[str]							=    ignore_files or []
        ignore_files.append("""__init__.py"""	)
        UpperCAmelCase__			:							List[str]							=    [file for file in files if file not in ignore_files]

        for file in files:
              # Open all files
              print("""Testing"""							,       _lowerCamelCase	)

              if only_modules:
                    UpperCAmelCase__			:							str							=    file.split("""."""	)[0]
                    try:
                          UpperCAmelCase__			:							str							=    getattr(_lowerCamelCase							,       _lowerCamelCase	)
                          UpperCAmelCase__			:							Union[str, Any]							=    doctest.DocTestSuite(_lowerCamelCase	)
                          UpperCAmelCase__			:							Tuple							=    unittest.TextTestRunner().run(_lowerCamelCase	)
                          self.assertIs(len(result.failures	)							,       0	)
                    except AttributeError:
                          logger.info(F"""{module_identifier} is not a module."""	)
              else:
                    UpperCAmelCase__			:							str							=    doctest.testfile(str("""..""" / directory / file	)							,       optionflags=doctest.ELLIPSIS	)
                    self.assertIs(result.failed							,       0	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    Path("""src/transformers"""	)
        UpperCAmelCase__			:							str							=    """modeling"""
        UpperCAmelCase__			:							List[str]							=    [
            """modeling_ctrl.py""",
            """modeling_tf_ctrl.py""",
        ]
        self.analyze_directory(_lowerCamelCase							,       identifier=_lowerCamelCase							,       ignore_files=_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    Path("""src/transformers"""	)
        UpperCAmelCase__			:							Union[str, Any]							=    """tokenization"""
        self.analyze_directory(_lowerCamelCase							,       identifier=_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    Path("""src/transformers"""	)
        UpperCAmelCase__			:							List[Any]							=    """configuration"""
        self.analyze_directory(_lowerCamelCase							,       identifier=_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    Path("""src/transformers"""	)
        UpperCAmelCase__			:							Dict							=    ["""configuration""", """modeling""", """tokenization"""]
        self.analyze_directory(_lowerCamelCase							,       n_identifier=_lowerCamelCase	)




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    Path("""docs/source"""	)
        UpperCAmelCase__			:							Any							=    ["""favicon.ico"""]
        self.analyze_directory(_lowerCamelCase							,       ignore_files=_lowerCamelCase							,       only_modules=_lowerCamelCase	)




"""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       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_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',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

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



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    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=1005							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """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__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """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  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

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

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

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

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

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

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

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

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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  _a      (self	):


        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




  def  _a      (self	):


        """simple docstring"""

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

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

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

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

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

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

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

