
"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: Tuple            =			KandinskyVaaInpaintPipeline
		A_		: Any            =			['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_		: int            =			[
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_		: Dict            =			[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_		: int            =			False

		@property
		def 							__lowerCamelCase							(   self				):
			return 32

		@property
		def 							__lowerCamelCase							(   self				):
			return 32

		@property
		def 							__lowerCamelCase							(   self				):
			return self.time_input_dim

		@property
		def 							__lowerCamelCase							(   self				):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(   self				):
			return 1_00

		@property
		def 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)

			__lowerCAmelCase    :		Any		    =   {
			    'in_channels': 9,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': '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': 'image_proj',
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': None,
			}

			__lowerCAmelCase    :		Any		    =   UNetaDConditionModel(**_SCREAMING_SNAKE_CASE				)
			return model

		@property
		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Optional[int]		    =   VQModel(**self.dummy_movq_kwargs				)
			return model

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.dummy_unet
			__lowerCAmelCase    :		List[str]		    =   self.dummy_movq

			__lowerCAmelCase    :		int		    =   DDIMScheduler(
			    num_train_timesteps=10_00 ,	beta_schedule='linear' ,	beta_start=0.0_0085 ,	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 ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

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

			if str(_SCREAMING_SNAKE_CASE				).startswith('mps'				):
				__lowerCAmelCase    :		int		    =   torch.manual_seed(_SCREAMING_SNAKE_CASE				)
			else:
				__lowerCAmelCase    :		Union[str, Any]		    =   torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   {
			    '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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   'cpu'

			__lowerCAmelCase    :		List[str]		    =   self.get_dummy_components()

			__lowerCAmelCase    :		str		    =   self.pipeline_class(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   pipe.to(_SCREAMING_SNAKE_CASE				)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		Dict		    =   output.images

			__lowerCAmelCase    :		List[str]		    =   pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				) ,	return_dict=_SCREAMING_SNAKE_CASE ,	)[0]

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

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

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

			__lowerCAmelCase    :		Tuple		    =   np.array(
			    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]				)

			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 							__lowerCamelCase							(   self				):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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

		def 							__lowerCamelCase							(   self				):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

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

			__lowerCAmelCase    :		int		    =   'a hat'

			__lowerCAmelCase    :		Optional[Any]		    =   KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior' ,	torch_dtype=torch.floataa				)
			pipe_prior.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint' ,	torch_dtype=torch.floataa				)
			__lowerCAmelCase    :		Dict		    =   pipeline.to(_SCREAMING_SNAKE_CASE				)
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   torch.Generator(device='cpu'				).manual_seed(0				)
			__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   pipe_prior(
			    _SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	num_inference_steps=5 ,	negative_prompt='' ,	).to_tuple()

			__lowerCAmelCase    :		Tuple		    =   pipeline(
			    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' ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   output.images[0]

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

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase__           =	"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__           =	direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCamelCase__           =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase__           =	{
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   False
	for attribute in attributes:
		for modeling_source in source_strings:
			# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
			if (
			    F"config.{attribute}" in modeling_source
			    or F"getattr(config, \"{attribute}\"" in modeling_source
			    or F"getattr(self.config, \"{attribute}\"" in modeling_source
			):
				__lowerCAmelCase    :		Union[str, Any]		    =   True
			# Deal with multi-line cases
			elif (
			    re.search(
			        rF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" ,						_UpperCamelCase ,						)
			    is not None
			):
				__lowerCAmelCase    :		Optional[Any]		    =   True
			# `SequenceSummary` is called with `SequenceSummary(config)`
			elif attribute in [
			    "summary_type",
			    "summary_use_proj",
			    "summary_activation",
			    "summary_last_dropout",
			    "summary_proj_to_labels",
			    "summary_first_dropout",
			]:
				if "SequenceSummary" in modeling_source:
					__lowerCAmelCase    :		int		    =   True
			if attribute_used:
				break
		if attribute_used:
			break

    # common and important attributes, even if they do not always appear in the modeling files
	__lowerCAmelCase    :		Optional[Any]		    =   [
	    'bos_index',
	    'eos_index',
	    'pad_index',
	    'unk_index',
	    'mask_index',
	    'image_size',
	    'use_cache',
	    'out_features',
	    'out_indices',
	]
	__lowerCAmelCase    :		Tuple		    =   ['encoder_no_repeat_ngram_size']

	# Special cases to be allowed
	__lowerCAmelCase    :		Dict		    =   True
	if not attribute_used:
		__lowerCAmelCase    :		int		    =   False
		for attribute in attributes:
			# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
			if attribute in ["is_encoder_decoder"] and default_value is True:
				__lowerCAmelCase    :		int		    =   True
			elif attribute in ["tie_word_embeddings"] and default_value is False:
				__lowerCAmelCase    :		Dict		    =   True

			# Allow cases without checking the default value in the configuration class
			elif attribute in attributes_to_allow + attributes_used_in_generation:
				__lowerCAmelCase    :		Tuple		    =   True
			elif attribute.endswith('_token_id'	):
				__lowerCAmelCase    :		int		    =   True

			# configuration class specific cases
			if not case_allowed:
				__lowerCAmelCase    :		Dict		    =   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ ,						[]	)
				__lowerCAmelCase    :		Any		    =   allowed_cases is True or attribute in allowed_cases

	return attribute_used or case_allowed
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   dict(inspect.signature(config_class.__init__	).parameters	)
	__lowerCAmelCase    :		Tuple		    =   [x for x in list(signature.keys()	) if x not in ['self', 'kwargs']]
	__lowerCAmelCase    :		Tuple		    =   [signature[param].default for param in parameter_names]

	# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
	# as one variant is used, the test should pass
	__lowerCAmelCase    :		List[Any]		    =   {}
	if len(config_class.attribute_map	) > 0:
		__lowerCAmelCase    :		Union[str, Any]		    =   {v: k for k, v in config_class.attribute_map.items()}

	# Get the path to modeling source files
	__lowerCAmelCase    :		Dict		    =   inspect.getsourcefile(_UpperCamelCase	)
	__lowerCAmelCase    :		List[str]		    =   os.path.dirname(_UpperCamelCase	)
	# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
	__lowerCAmelCase    :		Tuple		    =   [os.path.join(_UpperCamelCase ,						_UpperCamelCase	) for fn in os.listdir(_UpperCamelCase	) if fn.startswith('modeling_'	)]

	# Get the source code strings
	__lowerCAmelCase    :		str		    =   []
	for path in modeling_paths:
		if os.path.isfile(_UpperCamelCase	):
			with open(_UpperCamelCase	) as fp:
				modeling_sources.append(fp.read()	)

	__lowerCAmelCase    :		Optional[int]		    =   []
	for config_param, default_value in zip(_UpperCamelCase ,						_UpperCamelCase	):
		# `attributes` here is all the variant names for `config_param`
		__lowerCAmelCase    :		Optional[int]		    =   [config_param]
		# some configuration classes have non-empty `attribute_map`, and both names could be used in the
		# corresponding modeling files. As long as one of them appears, it is fine.
		if config_param in reversed_attribute_map:
			attributes.append(reversed_attribute_map[config_param]	)

		if not check_attribute_being_used(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
			unused_attributes.append(attributes[0]	)

	return sorted(_UpperCamelCase	)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   {}
	for _config_class in list(CONFIG_MAPPING.values()	):
		# Skip deprecated models
		if "models.deprecated" in _config_class.__module__:
			continue
		# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
		__lowerCAmelCase    :		int		    =   [
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class	) ,						lambda _UpperCamelCase	: inspect.isclass(_UpperCamelCase	)
		        and issubclass(_UpperCamelCase ,						_UpperCamelCase	)
		        and inspect.getmodule(_UpperCamelCase	) == inspect.getmodule(_config_class	) ,						)
		]
		for config_class in config_classes_in_module:
			__lowerCAmelCase    :		List[Any]		    =   check_config_attributes_being_used(_UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
				__lowerCAmelCase    :		str		    =   unused_attributes

	if len(_UpperCamelCase	) > 0:
		__lowerCAmelCase    :		int		    =   'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
		for name, attributes in configs_with_unused_attributes.items():
			error += F"{name}: {attributes}\n"

		raise ValueError(_UpperCamelCase	)


if __name__ == "__main__":
							check_config_attributes()