





"""simple docstring"""
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					_UpperCAmelCase   (	__snake_case     ,							unittest.TestCase ):
					UpperCamelCase      =							OpenAIGPTTokenizer
					UpperCamelCase      =							OpenAIGPTTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:int		):
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												A		=  [
												    "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>",
												]
												A		=  dict(zip(A_     ,       range(len(A_		)		)		)		)
												A		=  ["#version: 0.2", "l o", "lo w", "e r</w>", ""]

												A		=  os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES["vocab_file"]		)
												A		=  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 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:int		):
												return "lower newer", "lower newer"


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  OpenAIGPTTokenizer(self.vocab_file     ,       self.merges_file		)

												A		=  "lower"
												A		=  ["low", "er</w>"]
												A		=  tokenizer.tokenize(A_		)
												self.assertListEqual(A_     ,       A_		)

												A		=  tokens + ["<unk>"]
												A		=  [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(A_		)     ,       A_		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]=15		):




												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																										A		=  self.rust_tokenizer_class.from_pretrained(A_     ,       **A_		)

																										# Simple input
																										A		=  "This is a simple input"
																										A		=  ["This is a simple input 1", "This is a simple input 2"]
																										A		=  ("This is a simple input", "This is a pair")
																										A		=  [
																										    ("This is a simple input 1", "This is a simple input 2"),
																										    ("This is a simple pair 1", "This is a simple pair 2"),
																										]

																										# Simple input tests
																										self.assertRaises(A_     ,       tokenizer_r.encode     ,       A_     ,       max_length=A_     ,       padding="max_length"		)

																										# Simple input
																										self.assertRaises(A_     ,       tokenizer_r.encode_plus     ,       A_     ,       max_length=A_     ,       padding="max_length"		)

																										# Simple input
																										self.assertRaises(
																										    A_     ,       tokenizer_r.batch_encode_plus     ,       A_     ,       max_length=A_     ,       padding="max_length"     ,       )

																										# Pair input
																										self.assertRaises(A_     ,       tokenizer_r.encode     ,       A_     ,       max_length=A_     ,       padding="max_length"		)

																										# Pair input
																										self.assertRaises(A_     ,       tokenizer_r.encode_plus     ,       A_     ,       max_length=A_     ,       padding="max_length"		)

																										# Pair input
																										self.assertRaises(
																										    A_     ,       tokenizer_r.batch_encode_plus     ,       A_     ,       max_length=A_     ,       padding="max_length"     ,       )


					def 	lowerCamelCase       (					self			:Optional[int]		):
												pass







@require_ftfy
@require_spacy
@require_tokenizers
class 					_UpperCAmelCase   (	__snake_case ):
					pass









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 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[str]						    =   KandinskyInpaintPipeline
      _UpperCAmelCase	:List[str]						    =   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase	:Dict						    =   [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase	:Optional[int]						    =   [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase	:int						    =   False





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 32





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 100





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
       return tokenizer





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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=1005       ,							)

       UpperCamelCase	:					Optional[int]       	=   MultilingualCLIP(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   text_encoder.eval()

       return text_encoder





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )

       UpperCamelCase	:					Optional[int]       	=   {
           "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	:					List[Any]       	=   UNetaDConditionModel(**A_   )
       return model





      @property
      def        __UpperCamelCase( 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        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					List[str]       	=   VQModel(**self.dummy_movq_kwargs   )
       return model





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.dummy_text_encoder
       UpperCamelCase	:					str       	=   self.dummy_tokenizer
       UpperCamelCase	:					List[Any]       	=   self.dummy_unet
       UpperCamelCase	:					Optional[Any]       	=   self.dummy_movq

       UpperCamelCase	:					Union[str, Any]       	=   DDIMScheduler(
           num_train_timesteps=1000       ,							beta_schedule="linear"       ,							beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							steps_offset=1       ,							prediction_type="epsilon"       ,							thresholding=A_       ,							)

       UpperCamelCase	:					Optional[Any]       	=   {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




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

       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					int       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Tuple       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   {
           "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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   "cpu"

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()

       UpperCamelCase	:					str       	=   self.pipeline_class(**A_   )
       UpperCamelCase	:					Tuple       	=   pipe.to(A_   )

       pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   pipe(**self.get_dummy_inputs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   pipe(
           **self.get_dummy_inputs(A_   )       ,							return_dict=A_       ,							)[0]

       UpperCamelCase	:					List[Any]       	=   image[0, -3:, -3:, -1]
       UpperCamelCase	:					Any       	=   image_from_tuple[0, -3:, -3:, -1]

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

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

       UpperCamelCase	:					Union[str, Any]       	=   np.array(
           [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

       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        __UpperCamelCase( self   ):
       '''simple docstring'''




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



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

       UpperCamelCase	:					List[str]       	=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
       UpperCamelCase	:					Dict       	=   np.ones((768, 768)       ,							dtype=np.floataa   )
       UpperCamelCase	:					str       	=   0

       UpperCamelCase	:					List[Any]       	=   "a hat"

       UpperCamelCase	:					Tuple       	=   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"       ,							torch_dtype=torch.floataa   )
       pipe_prior.to(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"       ,							torch_dtype=torch.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   pipeline.to(A_   )
       pipeline.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[Any]       	=   torch.Generator(device="cpu"   ).manual_seed(0   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   pipe_prior(
           A_       ,							generator=A_       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

       UpperCamelCase	:					Dict       	=   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	:					List[str]       	=   output.images[0]

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

       assert_mean_pixel_difference(A_       ,							A_   )
