





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Union[str, Any]							=   KandinskyVaaImgaImgPipeline
      A_						:    int							=   ['image_embeds', 'negative_image_embeds', 'image']
      A_						:    Optional[Any]							=   [
          'image_embeds',
          'negative_image_embeds',
          'image',
      ]
      A_						:    Optional[Any]							=   [
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      A_						:    str							=   False


      @property
      def        a			(self	:				Any				):





         """simple docstring"""



         return 32


      @property
      def        a			(self	:				int				):





         """simple docstring"""



         return 32


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





         """simple docstring"""



         return self.time_input_dim


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





         """simple docstring"""



         return self.time_input_dim * 4


      @property
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return 100


      @property
      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)

         __snake_case								=       {
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''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''': '''image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         __snake_case								=       UNetaDConditionModel(**a__				)
         return model


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





         """simple docstring"""



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


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.dummy_unet
         __snake_case								=       self.dummy_movq

         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_0_0_8_5,
             '''beta_end''': 0.0_1_2,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         __snake_case								=       DDIMScheduler(**a__				)

         __snake_case								=       {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components


      def        a			(self	:				Dict    ,      a__	:				Optional[Any]    ,      a__	:				int=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, self.text_embedder_hidden_size)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       floats_tensor((1, self.text_embedder_hidden_size)    ,      rng=random.Random(seed + 1				)				).to(
             a__				)
         # create init_image
         __snake_case								=       floats_tensor((1, 3, 64, 64)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image.cpu().permute(0    ,      2    ,      3    ,      1				)[0]
         __snake_case								=       Image.fromarray(np.uinta(a__				)				).convert('''RGB'''				).resize((256, 256)				)

         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 10,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       '''cpu'''

         __snake_case								=       self.get_dummy_components()

         __snake_case								=       self.pipeline_class(**a__				)
         __snake_case								=       pipe.to(a__				)

         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       pipe(**self.get_dummy_inputs(a__				)				)
         __snake_case								=       output.images

         __snake_case								=       pipe(
             **self.get_dummy_inputs(a__				)    ,      return_dict=a__    ,      )[0]

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

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

         __snake_case								=       np.array(
             [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''				)

         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         __snake_case								=       '''A red cartoon frog, 4k'''

         __snake_case								=       KandinskyVaaPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''    ,      torch_dtype=torch.floataa				)
         pipe_prior.to(a__				)

         __snake_case								=       KandinskyVaaImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''    ,      torch_dtype=torch.floataa				)
         __snake_case								=       pipeline.to(a__				)

         pipeline.set_progress_bar_config(disable=a__				)

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

         __snake_case								=       pipeline(
             image=a__    ,      image_embeds=a__    ,      negative_image_embeds=a__    ,      generator=a__    ,      num_inference_steps=100    ,      height=768    ,      width=768    ,      strength=0.2    ,      output_type='''np'''    ,      )

         __snake_case								=       output.images[0]

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

         assert_mean_pixel_difference(a__    ,      a__				)








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   XLMTokenizer
      A_						:    Optional[Any]							=   False


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case								=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
         __snake_case								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']				)
         with open(self.vocab_file    ,      '''w'''				) as fp:
            fp.write(json.dumps(a__				)				)
         with open(self.merges_file    ,      '''w'''				) as fp:
            fp.write('''\n'''.join(a__				)				)


      def        a			(self	:				int    ,      a__	:				Any				):





         """simple docstring"""



         __snake_case								=       '''lower newer'''
         __snake_case								=       '''lower newer'''
         return input_text, output_text


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer(self.vocab_file    ,      self.merges_file				)

         __snake_case								=       '''lower'''
         __snake_case								=       ['''low''', '''er</w>''']
         __snake_case								=       tokenizer.tokenize(a__				)
         self.assertListEqual(a__    ,      a__				)

         __snake_case								=       tokens + ['''<unk>''']
         __snake_case								=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__				)    ,      a__				)


      @slow
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''				)

         __snake_case								=       tokenizer.encode('''sequence builders'''    ,      add_special_tokens=a__				)
         __snake_case								=       tokenizer.encode('''multi-sequence build'''    ,      add_special_tokens=a__				)

         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__				)
         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__    ,      a__				)

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

