
"""simple docstring"""







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__        =		{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
lowerCamelCase__        =		{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
lowerCamelCase__        =		{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__        =		{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
lowerCamelCase__        =		{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
lowerCamelCase__        =		{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


lowerCamelCase__        =		{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase__        =		{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase__        =		{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}




class 			A__	(     _lowerCamelCase):
		A_						:	Union[str, Any]           =     VOCAB_FILES_NAMES
		A_						:	int           =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	List[str]           =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	Any           =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 			A__	(     _lowerCamelCase):
		A_						:	Optional[int]           =     VOCAB_FILES_NAMES
		A_						:	List[Any]           =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	Tuple           =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	int           =     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



lowerCamelCase__        =		collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

lowerCamelCase__        =		collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


lowerCamelCase__        =		R"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """




@add_start_docstrings(_lowerCamelCase)
class 			A__	:

		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			if titles is None and texts is None:
				return super().__call__(
				    _SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			elif titles is None or texts is None:
				__lowerCAmelCase      :						Optional[Any]						      =				titles if texts is None else texts
				return super().__call__(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				titles if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [titles]
			__lowerCAmelCase      :						Optional[Any]						      =				texts if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [texts]
			__lowerCAmelCase      :						List[Any]						      =				len(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				questions if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [questions] * n_passages
			if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
				raise ValueError(
				    f"There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE			)} titles and {len(_SCREAMING_SNAKE_CASE			)} texts."			)
			__lowerCAmelCase      :						Tuple						      =				super().__call__(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE			)['input_ids']
			__lowerCAmelCase      :						Optional[int]						      =				super().__call__(_SCREAMING_SNAKE_CASE	,		add_special_tokens=_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE			)['input_ids']
			__lowerCAmelCase      :						List[str]						      =				{
			    'input_ids': [
			        (encoded_question_and_title + encoded_text)[:max_length]
			        if max_length is not None and truncation
			        else encoded_question_and_title + encoded_text
			        for encoded_question_and_title, encoded_text in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			    ]
			}
			if return_attention_mask is not False:
				__lowerCAmelCase      :						Tuple						      =				[]
				for input_ids in encoded_inputs["input_ids"]:
					attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
				__lowerCAmelCase      :						int						      =				attention_mask
			return self.pad(_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 16	,		_SCREAMING_SNAKE_CASE = 64	,		_SCREAMING_SNAKE_CASE = 4	,		):
			__lowerCAmelCase      :						Any						      =				reader_input['input_ids']
			__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Tuple						      =				reader_output[:3]
			__lowerCAmelCase      :						Tuple						      =				len(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				sorted(range(_SCREAMING_SNAKE_CASE			)	,		reverse=_SCREAMING_SNAKE_CASE	,		key=relevance_logits.__getitem__			)
			__lowerCAmelCase      :						List[DPRReaderOutput]						      =				[]
			for doc_id in sorted_docs:
				__lowerCAmelCase      :						Union[str, Any]						      =				list(input_ids[doc_id]			)
				# assuming question & title information is at the beginning of the sequence
				__lowerCAmelCase      :						Optional[int]						      =				sequence_ids.index(self.sep_token_id	,		2			) + 1  # second sep id
				if sequence_ids[-1] == self.pad_token_id:
					__lowerCAmelCase      :						List[str]						      =				sequence_ids.index(self.pad_token_id			)
				else:
					__lowerCAmelCase      :						Any						      =				len(_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Union[str, Any]						      =				self._get_best_spans(
				    start_logits=start_logits[doc_id][passage_offset:sequence_len]	,		end_logits=end_logits[doc_id][passage_offset:sequence_len]	,		max_answer_length=_SCREAMING_SNAKE_CASE	,		top_spans=_SCREAMING_SNAKE_CASE	,		)
				for start_index, end_index in best_spans:
					start_index += passage_offset
					end_index += passage_offset
					nbest_spans_predictions.append(
					    DPRSpanPrediction(
					        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]	,		relevance_score=relevance_logits[doc_id]	,		doc_id=_SCREAMING_SNAKE_CASE	,		start_index=_SCREAMING_SNAKE_CASE	,		end_index=_SCREAMING_SNAKE_CASE	,		text=self.decode(sequence_ids[start_index : end_index + 1]			)	,		)			)
				if len(_SCREAMING_SNAKE_CASE			) >= num_spans:
					break
			return nbest_spans_predictions[:num_spans]







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						List[Any]						      =				[]
			for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE			):
				for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
					scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
			__lowerCAmelCase      :						List[Any]						      =				sorted(_SCREAMING_SNAKE_CASE	,		key=lambda _SCREAMING_SNAKE_CASE			: x[1]	,		reverse=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				[]
			for (start_index, end_index), score in scores:
				if start_index > end_index:
					raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
				__lowerCAmelCase      :						Union[str, Any]						      =				end_index - start_index + 1
				if length > max_answer_length:
					raise ValueError(f"Span is too long: {length} > {max_answer_length}"			)
				if any(
				    start_index <= prev_start_index <= prev_end_index <= end_index
				    or prev_start_index <= start_index <= end_index <= prev_end_index
				    for (prev_start_index, prev_end_index) in chosen_span_intervals			):
					continue
				chosen_span_intervals.append((start_index, end_index)			)

				if len(_SCREAMING_SNAKE_CASE			) == top_spans:
					break
			return chosen_span_intervals




@add_end_docstrings(_lowerCamelCase)
class 			A__	(     _lowerCamelCase	,					_lowerCamelCase):
		A_						:	Optional[int]           =     VOCAB_FILES_NAMES
		A_						:	int           =     READER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	List[Any]           =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	Optional[int]           =     READER_PRETRAINED_INIT_CONFIGURATION
		A_						:	Dict           =     ['input_ids', 'attention_mask']

"""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_						:	List[Any]           =     KandinskyVaaInpaintPipeline
		A_						:	Optional[int]           =     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:	Any           =     [
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:	Any           =     [
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:	Any           =     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      :						Optional[int]						      =				{
			    '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[Any]						      =				VQModel(**self.dummy_movq_kwargs			)
			return model

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.dummy_unet
			__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_movq

			__lowerCAmelCase      :						Tuple						      =				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      :						str						      =				{
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

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

			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						Optional[int]						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						List[Any]						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						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      :						List[Any]						      =				'cpu'

			__lowerCAmelCase      :						Dict						      =				self.get_dummy_components()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

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

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

			__lowerCAmelCase      :						str						      =				'a hat'

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

			__lowerCAmelCase      :						Optional[int]						      =				KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'	,		torch_dtype=torch.floataa			)
			__lowerCAmelCase      :						Tuple						      =				pipeline.to(_SCREAMING_SNAKE_CASE			)
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				torch.Generator(device='cpu'			).manual_seed(0			)
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				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      :						Optional[Any]						      =				output.images[0]

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

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)