


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	a			:			int												=					None

a			:			List[str]												=					logging.get_logger(__name__)
a			:			List[Any]												=					{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a			:			List[str]												=					{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a			:			Optional[int]												=					{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a			:			int												=					'''▁'''
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					VOCAB_FILES_NAMES
					__UpperCAmelCase							  =					PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase							  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase							  =					AlbertTokenizer






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							snake_case_=True			,							snake_case_=True			,							snake_case_=False			,							snake_case_="[CLS]"			,							snake_case_="[SEP]"			,							snake_case_="<unk>"			,							snake_case_="[SEP]"			,							snake_case_="<pad>"			,							snake_case_="[CLS]"			,							snake_case_="[MASK]"			,							**snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = (
								    AddedToken(UpperCamelCase_			,							lstrip=UpperCamelCase_			,							rstrip=UpperCamelCase_			,							normalized=UpperCamelCase_    )
								    if isinstance(UpperCamelCase_			,							UpperCamelCase_    )
								    else mask_token
								)

								super().__init__(
								    UpperCamelCase_			,							tokenizer_file=UpperCamelCase_			,							do_lower_case=UpperCamelCase_			,							remove_space=UpperCamelCase_			,							keep_accents=UpperCamelCase_			,							bos_token=UpperCamelCase_			,							eos_token=UpperCamelCase_			,							unk_token=UpperCamelCase_			,							sep_token=UpperCamelCase_			,							pad_token=UpperCamelCase_			,							cls_token=UpperCamelCase_			,							mask_token=UpperCamelCase_			,							**UpperCamelCase_			,							)

								__lowercase			       = do_lower_case
								__lowercase			       = remove_space
								__lowercase			       = keep_accents
								__lowercase			       = vocab_file
								__lowercase			       = False if not self.vocab_file else True






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      int:







								'''simple docstring'''

								__lowercase			       = [self.sep_token_id]
								__lowercase			       = [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = [self.sep_token_id]
								__lowercase			       = [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep    ) * [0]
								return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      Any:







								'''simple docstring'''

								if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''    )

								if not os.path.isdir(UpperCamelCase_    ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
											return
								__lowercase			       = os.path.join(
								    UpperCamelCase_			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCamelCase_    ):
											copyfile(self.vocab_file			,							UpperCamelCase_    )

								return (out_vocab_file,)




"""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, KandinskyImgaImgPipeline, 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 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim * 4



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          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=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[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,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          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  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              '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  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

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

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

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

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

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          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  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

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

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

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

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

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

