

'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Optional[Any]			 =							KandinskyPipeline
   _SCREAMING_SNAKE_CASE					:  int			 =							[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							[
       '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					:  List[str]			 =							False


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return 32


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return 32


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.time_input_dim


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.time_input_dim * 4


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return 100


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Tuple      =     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
      return tokenizer


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      torch.manual_seed(0						)
      _lowercase    :   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=1005    ,       )

      _lowercase    :   int      =     MultilingualCLIP(_UpperCamelCase						)
      _lowercase    :   List[Any]      =     text_encoder.eval()

      return text_encoder


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      torch.manual_seed(0						)

      _lowercase    :   List[Any]      =     {
          "in_channels": 4,
          # 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,
      }

      _lowercase    :   Optional[Any]      =     UNetaDConditionModel(**_UpperCamelCase						)
      return model


   @property
   def 		_lowerCamelCase     (				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 		_lowerCamelCase     (				self						):





      """simple docstring"""





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


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.dummy_text_encoder
      _lowercase    :   Any      =     self.dummy_tokenizer
      _lowercase    :   List[str]      =     self.dummy_unet
      _lowercase    :   Dict      =     self.dummy_movq

      _lowercase    :   List[Any]      =     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    ,       )

      _lowercase    :   str      =     {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components


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





      """simple docstring"""





      _lowercase    :   List[Any]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
      _lowercase    :   Dict      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1						)						).to(_UpperCamelCase						)
      if str(_UpperCamelCase						).startswith("mps"						):
         _lowercase    :   List[str]      =     torch.manual_seed(_UpperCamelCase						)
      else:
         _lowercase    :   Optional[Any]      =     torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
      _lowercase    :   Union[str, Any]      =     {
          "prompt": "horse",
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     "cpu"

      _lowercase    :   int      =     self.get_dummy_components()

      _lowercase    :   Tuple      =     self.pipeline_class(**_UpperCamelCase						)
      _lowercase    :   List[Any]      =     pipe.to(_UpperCamelCase						)

      pipe.set_progress_bar_config(disable=_UpperCamelCase						)

      _lowercase    :   str      =     pipe(**self.get_dummy_inputs(_UpperCamelCase						)						)
      _lowercase    :   Optional[Any]      =     output.images

      _lowercase    :   Dict      =     pipe(
          **self.get_dummy_inputs(_UpperCamelCase						)    ,       return_dict=_UpperCamelCase    ,       )[0]

      _lowercase    :   Dict      =     image[0, -3:, -3:, -1]
      _lowercase    :   Any      =     image_from_tuple[0, -3:, -3:, -1]

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

      _lowercase    :   Optional[Any]      =     np.array(
          [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

      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()}'''




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


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





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






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_text2img_cat_fp16.npy"						)

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

      _lowercase    :   Tuple      =     KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"    ,       torch_dtype=torch.floataa						)
      _lowercase    :   Union[str, Any]      =     pipeline.to(_UpperCamelCase						)
      pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

      _lowercase    :   List[str]      =     "red cat, 4k photo"

      _lowercase    :   Union[str, Any]      =     torch.Generator(device="cuda"						).manual_seed(0						)
      _lowercase  ,   _lowercase    :   Union[str, Any]      =     pipe_prior(
          _UpperCamelCase    ,       generator=_UpperCamelCase    ,       num_inference_steps=5    ,       negative_prompt=""    ,       ).to_tuple()

      _lowercase    :   List[str]      =     torch.Generator(device="cuda"						).manual_seed(0						)
      _lowercase    :   Dict      =     pipeline(
          _UpperCamelCase    ,       image_embeds=_UpperCamelCase    ,       negative_image_embeds=_UpperCamelCase    ,       generator=_UpperCamelCase    ,       num_inference_steps=100    ,       output_type="np"    ,       )

      _lowercase    :   str      =     output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(_UpperCamelCase    ,       _UpperCamelCase						)









'''simple docstring'''
import requests

try:
    from django.contrib.auth import authenticate, login
    from django.shortcuts import redirect, render
except ImportError:
    _snake_case     						=						_snake_case     						=						_snake_case     						=						_snake_case     						=						print







def       _A	(			snake_case  )    ->			Any:
   # Enter your recaptcha secret key here
   _lowercase    :   Optional[Any]      =     "secretKey"
   _lowercase    :   Union[str, Any]      =     "https://www.google.com/recaptcha/api/siteverify"

   # when method is not POST, direct user to login page
   if request.method != "POST":
      return render(snake_case	,							"login.html"  )

   # from the frontend, get username, password, and client_key
   _lowercase    :   Optional[int]      =     request.POST.get("username"  )
   _lowercase    :   Tuple      =     request.POST.get("password"  )
   _lowercase    :   Tuple      =     request.POST.get("g-recaptcha-response"  )

   # post recaptcha response to Google's recaptcha api
   _lowercase    :   str      =     requests.post(snake_case	,							data={"secret": secret_key, "response": client_key}  )
   # if the recaptcha api verified our keys
   if response.json().get("success"	,							snake_case  ):
      # authenticate the user
      _lowercase    :   Tuple      =     authenticate(snake_case	,							username=snake_case	,							password=snake_case  )
      if user_in_database:
         login(snake_case	,							snake_case  )
         return redirect("/your-webpage"  )
   return render(snake_case	,							"login.html"  )






