
'''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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



