

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						(							UpperCAmelCase__    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							KandinskyImgaImgPipeline
    lowerCamelCase			=							['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    lowerCamelCase			=							[
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    lowerCamelCase			=							[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase			=							False


    @property
    def 						snake_case__     (      self :       List[str]		)->    Any:





        '''simple docstring'''







        return 3_2


    @property
    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        return 3_2


    @property
    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        return self.time_input_dim


    @property
    def 						snake_case__     (      self :       Union[str, Any]		)->    List[str]:





        '''simple docstring'''







        return self.time_input_dim * 4


    @property
    def 						snake_case__     (      self :       Optional[int]		)->    Optional[int]:





        '''simple docstring'''







        return 1_0_0


    @property
    def 						snake_case__     (      self :       str		)->    List[str]:





        '''simple docstring'''







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


    @property
    def 						snake_case__     (      self :       Optional[int]		)->    Optional[int]:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   MCLIPConfig(
            numDims=self.cross_attention_dim,transformerDimensions=self.text_embedder_hidden_size,hidden_size=self.text_embedder_hidden_size,intermediate_size=3_7,num_attention_heads=4,num_hidden_layers=5,vocab_size=1_0_0_5,)

        A__          =   MultilingualCLIP(_a		)
        A__          =   text_encoder.eval()

        return text_encoder


    @property
    def 						snake_case__     (      self :       Tuple		)->    Dict:





        '''simple docstring'''







        torch.manual_seed(0		)

        A__          =   {
            '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,
        }

        A__          =   UNetaDConditionModel(**_a		)
        return model


    @property
    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        return {
            "block_out_channels": [3_2, 6_4],
            "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": 1_2,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }


    @property
    def 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.dummy_text_encoder
        A__          =   self.dummy_tokenizer
        A__          =   self.dummy_unet
        A__          =   self.dummy_movq

        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_schedule': 'linear',
            'beta_start': 0.00_085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        A__          =   DDIMScheduler(**_a		)

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

        return components


    def 						snake_case__     (      self :       Any,lowercase_ :       List[Any],lowercase_ :       Union[str, Any]=0		)->    Dict:





        '''simple docstring'''







        A__          =   floats_tensor((1, self.cross_attention_dim),rng=random.Random(_a		)		).to(_a		)
        A__          =   floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1		)		).to(_a		)
        # create init_image
        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(_a		)		).to(_a		)
        A__          =   image.cpu().permute(0,2,3,1		)[0]
        A__          =   Image.fromarray(np.uinta(_a		)		).convert('RGB'		).resize((2_5_6, 2_5_6)		)

        if str(_a		).startswith('mps'		):
            A__          =   torch.manual_seed(_a		)
        else:
            A__          =   torch.Generator(device=_a		).manual_seed(_a		)
        A__          =   {
            'prompt': 'horse',
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 6_4,
            'width': 6_4,
            'num_inference_steps': 1_0,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()

        A__          =   self.pipeline_class(**_a		)
        A__          =   pipe.to(_a		)

        pipe.set_progress_bar_config(disable=_a		)

        A__          =   pipe(**self.get_dummy_inputs(_a		)		)
        A__          =   output.images

        A__          =   pipe(
            **self.get_dummy_inputs(_a		),return_dict=_a,)[0]

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

        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array(
            [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
        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 ):







    """simple docstring"""





    def 						snake_case__     (      self :       str		)->    List[Any]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_img2img_frog.npy'		)

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

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

        A__          =   KandinskyImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1',torch_dtype=torch.floataa		)
        A__          =   pipeline.to(_a		)

        pipeline.set_progress_bar_config(disable=_a		)

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

        A__          =   pipeline(
            _a,image=_a,image_embeds=_a,negative_image_embeds=_a,generator=_a,num_inference_steps=1_0_0,height=7_6_8,width=7_6_8,strength=0.2,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (7_6_8, 7_6_8, 3)

        assert_mean_pixel_difference(_a,_a		)







"""simple docstring"""




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_xlnet import XLNetTokenizer
else:
					lowerCAmelCase							:			int     							=							None


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Tuple     							=							{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:			Union[str, Any]     							=							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase							:			Optional[int]     							=							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowerCAmelCase							:			Union[str, Any]     							=							"""▁"""

# Segments (not really needed)
lowerCAmelCase							:			str     							=							0
lowerCAmelCase							:			Optional[int]     							=							1
lowerCAmelCase							:			Tuple     							=							2
lowerCAmelCase							:			Optional[Any]     							=							3
lowerCAmelCase							:			List[Any]     							=							4






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						"left"
				__UpperCamelCase			  =						XLNetTokenizer





				def __init__(       self		, _a=None		, _a=None		, _a=False		, _a=True		, _a=False		, _a="<s>"		, _a="</s>"		, _a="<unk>"		, _a="<sep>"		, _a="<pad>"		, _a="<cls>"		, _a="<mask>"		, _a=["<eop>", "<eod>"]		, **_a		, ):





							"""simple docstring"""
							# Mask token behave like a normal word, i.e. include the space before it
							lowerCamelCase			      =					AddedToken(_a		, lstrip=_a		, rstrip=_a ) if isinstance(_a		, _a ) else mask_token

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

							lowerCamelCase			      =					3
							lowerCamelCase			      =					do_lower_case
							lowerCamelCase			      =					remove_space
							lowerCamelCase			      =					keep_accents
							lowerCamelCase			      =					vocab_file
							lowerCamelCase			      =					False if not self.vocab_file else True





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[self.cls_token_id]
							if token_ids_a is None:
										return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[2]

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





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""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(_a ):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
										return
							lowerCamelCase			      =					os.path.join(
							    _a		, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

							return (out_vocab_file,)



