
'''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, 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__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     KandinskyInpaintPipeline
					lowerCAmelCase_        =     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					lowerCAmelCase_        =     [
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					lowerCAmelCase_        =     [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''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		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim * 4






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 1_00






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
											return tokenizer






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Optional[Any]													=       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=10_05				,							)

											lowercase_	:     int													=       MultilingualCLIP(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       text_encoder.eval()

											return text_encoder






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)

											lowercase_	:     Any													=       {
											    '''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,
											}

											lowercase_	:     int													=       UNetaDConditionModel(**__SCREAMING_SNAKE_CASE		)
											return model






					@property
					def 	_snake_case    (   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 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Tuple													=       VQModel(**self.dummy_movq_kwargs		)
											return model






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       self.dummy_text_encoder
											lowercase_	:     int													=       self.dummy_tokenizer
											lowercase_	:     Tuple													=       self.dummy_unet
											lowercase_	:     List[Any]													=       self.dummy_movq

											lowercase_	:     Dict													=       DDIMScheduler(
											    num_train_timesteps=10_00				,							beta_schedule='''linear'''				,							beta_start=0.00_085				,							beta_end=0.012				,							clip_sample=__SCREAMING_SNAKE_CASE				,							set_alpha_to_one=__SCREAMING_SNAKE_CASE				,							steps_offset=1				,							prediction_type='''epsilon'''				,							thresholding=__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     List[Any]													=       {
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''movq''': movq,
											}

											return components






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(seed + 1		)		).to(__SCREAMING_SNAKE_CASE		)
											# create init_image
											lowercase_	:     Optional[int]													=       floats_tensor((1, 3, 64, 64)				,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       image.cpu().permute(0				,							2				,							3				,							1		)[0]
											lowercase_	:     List[Any]													=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE		)		).convert('''RGB'''		).resize((2_56, 2_56)		)
											# create mask
											lowercase_	:     Tuple													=       np.ones((64, 64)				,							dtype=np.floataa		)
											lowercase_	:     Optional[int]													=       0

											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_	:     str													=       torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     str													=       torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       '''cpu'''

											lowercase_	:     Dict													=       self.get_dummy_components()

											lowercase_	:     List[str]													=       self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       pipe.to(__SCREAMING_SNAKE_CASE		)

											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_	:     List[Any]													=       output.images

											lowercase_	:     str													=       pipe(
											    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)				,							return_dict=__SCREAMING_SNAKE_CASE				,							)[0]

											lowercase_	:     Optional[Any]													=       image[0, -3:, -3:, -1]
											lowercase_	:     Tuple													=       image_from_tuple[0, -3:, -3:, -1]

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

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

											lowercase_	:     Optional[int]													=       np.array(
											    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]		)

											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		):


											"""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		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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

											lowercase_	:     Dict													=       load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
											lowercase_	:     str													=       np.ones((7_68, 7_68)				,							dtype=np.floataa		)
											lowercase_	:     List[Any]													=       0

											lowercase_	:     int													=       '''a hat'''

											lowercase_	:     int													=       KandinskyPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-prior'''				,							torch_dtype=torch.floataa		)
											pipe_prior.to(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       KandinskyInpaintPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-inpaint'''				,							torch_dtype=torch.floataa		)
											lowercase_	:     Dict													=       pipeline.to(__SCREAMING_SNAKE_CASE		)
											pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       torch.Generator(device='''cpu'''		).manual_seed(0		)
											lowercase_						, lowercase_	:     Dict													=       pipe_prior(
											    __SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=5				,							negative_prompt=''''''				,							).to_tuple()

											lowercase_	:     Dict													=       pipeline(
											    __SCREAMING_SNAKE_CASE				,							image=__SCREAMING_SNAKE_CASE				,							mask_image=__SCREAMING_SNAKE_CASE				,							image_embeds=__SCREAMING_SNAKE_CASE				,							negative_image_embeds=__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=1_00				,							height=7_68				,							width=7_68				,							output_type='''np'''				,							)

											lowercase_	:     Tuple													=       output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)







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

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


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	lowerCAmelCase           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

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

lowerCAmelCase           =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase           =		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class       A    (   A_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[f'<extra_id_{i}>' for i in range(lowerCAmelCase					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase=			len(set(filter(lambda lowerCAmelCase					: bool('extra_id_' in str(lowerCAmelCase					)					)      ,				lowerCAmelCase					)					)					)
											if extra_tokens != extra_ids:
													raise ValueError(
													    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
													    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
													    ' tokens'					)

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

									__lowercase=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
											elif init_max_model_length is None:
													warnings.warn(
													    'This tokenizer was incorrectly instantiated with a model max length of'
													    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
													    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
													    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
													    f' {pretrained_model_name_or_path} automatically truncating your input to'
													    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
													    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
													    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
													    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				lowerCAmelCase      ,				)

									return max_model_length


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									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(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
											logger.info(f'Copy vocab file to {out_vocab_file}'					)

									return (out_vocab_file,)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[self.eos_token_id]

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


							def 			_A       (self					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






