
"""simple docstring"""

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


UpperCamelCase			:       Tuple									=				get_logger(__name__)

class __lowerCAmelCase	:
     lowercase		     =		"dummy_data"
     lowercase		     =		"datasets"
     lowercase		     =		False





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= 0
      __UpperCamelCase       						= dataset_name
      __UpperCamelCase       						= cache_dir
      __UpperCamelCase       						= use_local_dummy_data
      __UpperCamelCase       						= config
      # download_callbacks take a single url as input
      __UpperCamelCase       						= download_callbacks or []
      # if False, it doesn't load existing files and it returns the paths of the dummy files relative
      # to the dummy_data zip file root
      __UpperCamelCase       						= load_existing_dummy_data

      # TODO(PVP, QL) might need to make this more general
      __UpperCamelCase       						= str(__UpperCAmelCase   )
      # to be downloaded
      __UpperCamelCase       						= None
      __UpperCamelCase       						= None





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if self._dummy_file is None:
       __UpperCamelCase       						= self.download_dummy_data()
      return self._dummy_file





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if self.config is not None:
       # structure is dummy / config_name / version_name
       return os.path.join('dummy'			,  self.config.name			,  self.version_name   )
      # structure is dummy / version_name
      return os.path.join('dummy'			,  self.version_name   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return os.path.join(self.dummy_data_folder			,  'dummy_data.zip'   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= (
          self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
      )

      __UpperCamelCase       						= cached_path(
          __UpperCAmelCase			,  cache_dir=self.cache_dir			,  extract_compressed_file=__UpperCAmelCase			,  force_extract=__UpperCAmelCase   )

      return os.path.join(__UpperCAmelCase			,  self.dummy_file_name   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return os.path.join(self.datasets_scripts_dir			,  self.dataset_name			,  self.dummy_zip_file   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if self._bucket_url is None:
       __UpperCamelCase       						= hf_github_url(self.dataset_name			,  self.dummy_zip_file.replace(os.sep			,  '/'   )   )
      return self._bucket_url





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if os.path.isdir(self.dummy_file   ):
       return self.dummy_file
      # else cut off path to file -> example `xsum`.
      return "/".join(self.dummy_file.replace(os.sep			,  '/'   ).split('/'   )[:-1]   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  *__UpperCAmelCase   ):

      '''simple docstring'''




      if self.load_existing_dummy_data:
       # dummy data is downloaded and tested
       __UpperCamelCase       						= self.dummy_file
      else:
       # dummy data cannot be downloaded and only the path to dummy file is returned
       __UpperCamelCase       						= self.dummy_file_name

      # special case when data_url is a dict
      if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
       return self.create_dummy_data_dict(__UpperCAmelCase			,  __UpperCAmelCase   )
      elif isinstance(__UpperCAmelCase			,  (list, tuple)   ):
       return self.create_dummy_data_list(__UpperCAmelCase			,  __UpperCAmelCase   )
      else:
       return self.create_dummy_data_single(__UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  *__UpperCAmelCase   ):

      '''simple docstring'''




      return self.download_and_extract(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.download_and_extract(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return path





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {}





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= {}
      for key, single_urls in data_url.items():
       for download_callback in self.download_callbacks:
        if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
         for single_url in single_urls:
          download_callback(__UpperCAmelCase   )
        else:
         __UpperCamelCase       						= single_urls
         download_callback(__UpperCAmelCase   )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= [os.path.join(__UpperCAmelCase			,  urllib.parse.quote_plus(Path(__UpperCAmelCase   ).name   )   ) for x in single_urls]
       else:
        __UpperCamelCase       						= single_urls
        __UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  urllib.parse.quote_plus(Path(__UpperCAmelCase   ).name   )   )
       __UpperCamelCase       						= value

      # make sure that values are unique
      if all(isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) for i in dummy_data_dict.values()   ) and len(set(dummy_data_dict.values()   )   ) < len(
          dummy_data_dict.values()   ):
       # append key to value to make its name unique
       __UpperCamelCase       						= {key: value + key for key, value in dummy_data_dict.items()}

      return dummy_data_dict





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= []
      # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
      __UpperCamelCase       						= all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'			,  __UpperCAmelCase   )   ) for url in data_url   )
      __UpperCamelCase       						= all(
          url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'   ) for url in data_url   )
      if data_url and (is_tf_records or is_pubmed_records):
       __UpperCamelCase       						= [data_url[0]] * len(__UpperCAmelCase   )
      for single_url in data_url:
       for download_callback in self.download_callbacks:
        download_callback(__UpperCAmelCase   )
       # we force the name of each key to be the last file / folder name of the url path
       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       __UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  urllib.parse.quote_plus(single_url.split('/'   )[-1]   )   )
       dummy_data_list.append(__UpperCAmelCase   )
      return dummy_data_list





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      for download_callback in self.download_callbacks:
       download_callback(__UpperCAmelCase   )
      # we force the name of each key to be the last file / folder name of the url path
      # if the url has arguments, we need to encode them with urllib.parse.quote_plus
      __UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  urllib.parse.quote_plus(data_url.split('/'   )[-1]   )   )
      if os.path.exists(__UpperCAmelCase   ) or not self.load_existing_dummy_data:
       return value
      else:
       # Backward compatibility, maybe deprecate at one point.
       # For many datasets with single url calls to dl_manager.download_and_extract,
       # the dummy_data.zip file is actually the zipped downloaded file
       # while now we expected the dummy_data.zip file to be a directory containing
       # the downloaded file.
       return path_to_dummy_data





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      def _iter_archive_members(__UpperCAmelCase   ):
       # this preserves the order of the members inside the ZIP archive
       __UpperCamelCase       						= Path(self.dummy_file   ).parent
       __UpperCamelCase       						= path.relative_to(__UpperCAmelCase   )
       with ZipFile(self.local_path_to_dummy_data   ) as zip_file:
        __UpperCamelCase       						= zip_file.namelist()
       for member in members:
        if member.startswith(relative_path.as_posix()   ):
         yield dummy_parent_path.joinpath(__UpperCAmelCase   )

      __UpperCamelCase       						= Path(__UpperCAmelCase   )
      __UpperCamelCase       						= _iter_archive_members(__UpperCAmelCase   ) if self.use_local_dummy_data else path.rglob('*'   )
      for file_path in file_paths:
       if file_path.is_file() and not file_path.name.startswith(('.', '__')   ):
        yield file_path.relative_to(__UpperCAmelCase   ).as_posix(), file_path.open('rb'   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if not isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
       __UpperCamelCase       						= [paths]
      for path in paths:
       if os.path.isfile(__UpperCAmelCase   ):
        if os.path.basename(__UpperCAmelCase   ).startswith(('.', '__')   ):
         return
        yield path
       else:
        for dirpath, dirnames, filenames in os.walk(__UpperCAmelCase   ):
         if os.path.basename(__UpperCAmelCase   ).startswith(('.', '__')   ):
          continue
         dirnames.sort()
         for filename in sorted(__UpperCAmelCase   ):
          if filename.startswith(('.', '__')   ):
           continue
          yield os.path.join(__UpperCAmelCase			,  __UpperCAmelCase   )





"""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	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "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       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= 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       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          '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       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





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

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          '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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

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

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.images

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

      __UpperCamelCase       						= image[0, -3:, -3:, -1]
      __UpperCamelCase       						= image_from_tuple[0, -3:, -3:, -1]

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

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

      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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


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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



