




'''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 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       lowercase			:					Dict   		=[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=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,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''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,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							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   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

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

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

              lowerCamelCase_							=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 						__UpperCamelCase (				unittest.TestCase       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

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

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

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

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)










'''simple docstring'''




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


a_   :	Tuple							   = logging.get_logger(__name__)

a_   :	str							   = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Tuple							   = {
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

a_   :	str							   = {
    """openai/clip-vit-base-patch32""": 77,
}


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=VOCAB_FILES_NAMES
       lowercase			:					Optional[int]   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					Any   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=CLIPTokenizer






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|startoftext|>",	lowerCAmelCase="<|endoftext|>",	lowerCAmelCase="<|endoftext|>",	**lowerCAmelCase,	):



              """simple docstring"""







              super().__init__(
                  lowerCAmelCase,	lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	unk_token=lowerCAmelCase,	bos_token=lowerCAmelCase,	eos_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	**lowerCAmelCase,	)

              if not isinstance(self.backend_tokenizer.pre_tokenizer,	pre_tokenizers.Sequence						):
                     raise ValueError(
                         '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                         ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                         ''' to be compatible with this version.The easiest way to do so is'''
                         ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                         ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                         ''' transformers.'''						)

              self._wrap_decode_method_backend_tokenizer()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.backend_tokenizer.decode

              def new_decode_method(*lowerCAmelCase,	**lowerCAmelCase						):
                     lowerCamelCase_							=orig_decode_method(*lowerCAmelCase,	**lowerCAmelCase						)
                     lowerCamelCase_							=text.replace(self.backend_tokenizer.model.end_of_word_suffix,	''' '''						).strip()
                     return text

              lowerCamelCase_							=new_decode_method






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[self.eos_token_id]

              if token_ids_a is None:
                     return bos_token + token_ids_a + eos_token
              return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=[self.bos_token_id]
              lowerCamelCase_							=[self.eos_token_id]

              if token_ids_a is None:
                     return len(bos_token + token_ids_a + eos_token						) * [0]
              return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token						) * [0]






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              lowerCamelCase_							=self._tokenizer.model.save(lowerCAmelCase,	name=lowerCAmelCase						)
              return tuple(lowerCAmelCase						)




