


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case   							=						{
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case   							=						{
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


__snake_case   							=						{
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_INIT_CONFIGURATION
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     SqueezeBertTokenizer







       def __init__(			self: Dict,A_: int=None,A_: int=None,A_: List[Any]=True,A_: Any="[UNK]",A_: List[Any]="[SEP]",A_: int="[PAD]",A_: Optional[Any]="[CLS]",A_: List[str]="[MASK]",A_: int=True,A_: Dict=None,**A_: int,):






            '''simple docstring'''




            super().__init__(
                A_,tokenizer_file=A_,do_lower_case=A_,unk_token=A_,sep_token=A_,pad_token=A_,cls_token=A_,mask_token=A_,tokenize_chinese_chars=A_,strip_accents=A_,**A_,)

            __UpperCamelCase =			json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
            if (
                normalizer_state.get('lowercase',A_					) != do_lower_case
                or normalizer_state.get('strip_accents',A_					) != strip_accents
                or normalizer_state.get('handle_chinese_chars',A_					) != tokenize_chinese_chars
            ):
                 __UpperCamelCase =			getattr(A_,normalizer_state.pop('type'					)					)
                 __UpperCamelCase =			do_lower_case
                 __UpperCamelCase =			strip_accents
                 __UpperCamelCase =			tokenize_chinese_chars
                 __UpperCamelCase =			normalizer_class(**A_					)

            __UpperCamelCase =			do_lower_case







       def 		snake_case_	(			self: Dict,A_: Tuple,A_: Union[str, Any]=None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

            return output







       def 		snake_case_	(			self: Any,A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.sep_token_id]
            __UpperCamelCase =			[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 		snake_case_	(			self: Union[str, Any],A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            __UpperCamelCase =			self._tokenizer.model.save(A_,name=A_					)
            return tuple(A_					)






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     KandinskyInpaintPipeline
       _lowercase											=     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
       _lowercase											=     [
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
           """mask_image""",
       ]
       _lowercase											=     [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       _lowercase											=     False







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            return self.time_input_dim







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return self.time_input_dim * 4







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return 100







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			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,)

            __UpperCamelCase =			MultilingualCLIP(A_					)
            __UpperCamelCase =			text_encoder.eval()

            return text_encoder







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)

            __UpperCamelCase =			{
                'in_channels': 9,
                # 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 =			UNetaDConditionModel(**A_					)
            return model







       @property
       def 		snake_case_	(			self: str					):






            '''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 		snake_case_	(			self: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.dummy_text_encoder
            __UpperCamelCase =			self.dummy_tokenizer
            __UpperCamelCase =			self.dummy_unet
            __UpperCamelCase =			self.dummy_movq

            __UpperCamelCase =			DDIMScheduler(
                num_train_timesteps=1000,beta_schedule='linear',beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,clip_sample=A_,set_alpha_to_one=A_,steps_offset=1,prediction_type='epsilon',thresholding=A_,)

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

            return components







       def 		snake_case_	(			self: Tuple,A_: Optional[int],A_: Dict=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1					)					).to(A_					)
            # create init_image
            __UpperCamelCase =			floats_tensor((1, 3, 64, 64),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			image.cpu().permute(0,2,3,1					)[0]
            __UpperCamelCase =			Image.fromarray(np.uinta(A_					)					).convert('RGB'					).resize((256, 256)					)
            # create mask
            __UpperCamelCase =			np.ones((64, 64),dtype=np.floataa					)
            __UpperCamelCase =			0

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'horse',
                'image': init_image,
                'mask_image': mask,
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 64,
                'width': 64,
                'num_inference_steps': 2,
                'guidance_scale': 4.0,
                'output_type': 'np',
            }
            return inputs







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()

            __UpperCamelCase =			self.pipeline_class(**A_					)
            __UpperCamelCase =			pipe.to(A_					)

            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			pipe(**self.get_dummy_inputs(A_					)					)
            __UpperCamelCase =			output.images

            __UpperCamelCase =			pipe(
                **self.get_dummy_inputs(A_					),return_dict=A_,)[0]

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

            print(F'''image.shape {image.shape}'''					)

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

            __UpperCamelCase =			np.array(
                [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]					)

            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()}'''







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

            __UpperCamelCase =			load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
            __UpperCamelCase =			np.ones((768, 768),dtype=np.floataa					)
            __UpperCamelCase =			0

            __UpperCamelCase =			'a hat'

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

            __UpperCamelCase =			KandinskyInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-inpaint',torch_dtype=torch.floataa					)
            __UpperCamelCase =			pipeline.to(A_					)
            pipeline.set_progress_bar_config(disable=A_					)

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

            __UpperCamelCase =			pipeline(
                A_,image=A_,mask_image=A_,image_embeds=A_,negative_image_embeds=A_,generator=A_,num_inference_steps=100,height=768,width=768,output_type='np',)

            __UpperCamelCase =			output.images[0]

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

            assert_mean_pixel_difference(A_,A_					)


