


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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_snake_case  :	Optional[int]			      =       logging.get_logger(__name__)

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

_snake_case  :	Union[str, Any]			      =       {
    "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"
        ),
    },
}
_snake_case  :	str			      =       {
    "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"
        ),
    },
}
_snake_case  :	Tuple			      =       {
    "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"
        ),
    },
}

_snake_case  :	List[str]			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_snake_case  :	str			      =       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_snake_case  :	Optional[int]			      =       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_snake_case  :	List[str]			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	Optional[int]			      =       {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	Tuple			      =       {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class        __magic_name__	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Optional[int]  						=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							List[str]  						=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							List[str]  						=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						DPRContextEncoderTokenizer

class        __magic_name__	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							List[Any]  						=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							int  						=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							Tuple  						=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						DPRQuestionEncoderTokenizer
_snake_case  :	Optional[Any]			      =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_snake_case  :	str			      =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_snake_case  :	Optional[Any]			      =       R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "

@add_start_docstrings(_lowerCAmelCase   )
class        __magic_name__	:




							"""simple docstring"""


							def __call__(							self :						Optional[int]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						Optional[bool] = None					,			**lowerCamelCase :						Optional[Any]					,			)			->					BatchEncoding:
														if titles is None and texts is None:
																					return super().__call__(
																					    lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase					,			return_attention_mask=lowerCamelCase					,			**lowerCamelCase					,			)
														elif titles is None or texts is None:
																					__snake_case	:    Optional[int]											=     titles if texts is None else texts
																					return super().__call__(
																					    lowerCamelCase					,			lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase					,			return_attention_mask=lowerCamelCase					,			**lowerCamelCase					,			)
														__snake_case	:    List[str]											=     titles if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [titles]
														__snake_case	:    int											=     texts if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [texts]
														__snake_case	:    str											=     len(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     questions if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [questions] * n_passages
														assert len(lowerCamelCase       ) == len(
														    lowerCamelCase       ), F'There should be as many titles than texts but got {len(lowerCamelCase       )} titles and {len(lowerCamelCase       )} texts.'
														__snake_case	:    Optional[Any]											=     super().__call__(lowerCamelCase					,			lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    Union[str, Any]											=     super().__call__(lowerCamelCase					,			add_special_tokens=lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    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(lowerCamelCase					,			lowerCamelCase       )
														    ]
														}
														if return_attention_mask is not False:
																					__snake_case	:    Optional[Any]											=     []
																					for input_ids in encoded_inputs["input_ids"]:
																												attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
																					__snake_case	:    Optional[int]											=     attention_mask
														return self.pad(lowerCamelCase					,			padding=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase       )

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

																					__snake_case	:    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=lowerCamelCase					,			top_spans=lowerCamelCase					,			)
																					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=lowerCamelCase					,			start_index=lowerCamelCase					,			end_index=lowerCamelCase					,			text=self.decode(sequence_ids[start_index : end_index + 1]       )					,			)       )
																					if len(lowerCamelCase       ) >= num_spans:
																												break
														return nbest_spans_predictions[:num_spans]






							def 					__snake_case      (							self :						int					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			)			->					List[DPRSpanPrediction]:
														__snake_case	:    int											=     []
														for start_index, start_score in enumerate(lowerCamelCase       ):
																					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)       )
														__snake_case	:    Optional[Any]											=     sorted(lowerCamelCase					,			key=lambda lowerCamelCase       : x[1]					,			reverse=lowerCamelCase       )
														__snake_case	:    str											=     []
														for (start_index, end_index), score in scores:
																					assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
																					__snake_case	:    Dict											=     end_index - start_index + 1
																					assert length <= max_answer_length, 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(lowerCamelCase       ) == top_spans:
																												break
														return chosen_span_intervals

@add_end_docstrings(_lowerCAmelCase   )
class        __magic_name__	(_lowerCAmelCase  ,       _lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Tuple  						=						READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							List[str]  						=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							Dict  						=						READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						["input_ids", "attention_mask"]
							__UpperCAmelCase					:							Dict  						=						DPRReaderTokenizer





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						StableDiffusionXLImgaImgPipeline
							__UpperCAmelCase					:							Any  						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
							__UpperCAmelCase					:							Dict  						=						PipelineTesterMixin.required_optional_params - {"latents"}
							__UpperCAmelCase					:							Union[str, Any]  						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							__UpperCAmelCase					:							Tuple  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase					:							str  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    Optional[int]											=     UNetaDConditionModel(
														    block_out_channels=(32, 64)					,			layers_per_block=2					,			sample_size=32					,			in_channels=4					,			out_channels=4					,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,			attention_head_dim=(2, 4)					,			use_linear_projection=lowerCamelCase					,			addition_embed_type="text_time"					,			addition_time_embed_dim=8					,			transformer_layers_per_block=(1, 2)					,			projection_class_embeddings_input_dim=80					,			cross_attention_dim=64					,			)
														__snake_case	:    Tuple											=     EulerDiscreteScheduler(
														    beta_start=0.0_00_85					,			beta_end=0.0_12					,			steps_offset=1					,			beta_schedule="scaled_linear"					,			timestep_spacing="leading"					,			)
														torch.manual_seed(0       )
														__snake_case	:    int											=     AutoencoderKL(
														    block_out_channels=[32, 64]					,			in_channels=3					,			out_channels=3					,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,			latent_channels=4					,			sample_size=128					,			)
														torch.manual_seed(0       )
														__snake_case	:    Optional[int]											=     CLIPTextConfig(
														    bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1000					,			hidden_act="gelu"					,			projection_dim=32					,			)
														__snake_case	:    List[str]											=     CLIPTextModel(lowerCamelCase       )
														__snake_case	:    Any											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

														__snake_case	:    List[str]											=     CLIPTextModelWithProjection(lowerCamelCase       )
														__snake_case	:    List[Any]											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

														__snake_case	:    Optional[int]											=     {
														    "unet": unet,
														    "scheduler": scheduler,
														    "vae": vae,
														    "text_encoder": text_encoder,
														    "tokenizer": tokenizer,
														    "text_encoder_2": text_encoder_a,
														    "tokenizer_2": tokenizer_a,
														    # "safety_checker": None,
														    # "feature_extractor": None,
														}
														return components

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[Any]=0       )			->					Union[str, Any]:
														__snake_case	:    int											=     floats_tensor((1, 3, 32, 32)					,			rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )
														__snake_case	:    Any											=     image / 2 + 0.5
														if str(lowerCamelCase       ).startswith("mps"       ):
																					__snake_case	:    Dict											=     torch.manual_seed(lowerCamelCase       )
														else:
																					__snake_case	:    int											=     torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     {
														    "prompt": "A painting of a squirrel eating a burger",
														    "image": image,
														    "generator": generator,
														    "num_inference_steps": 2,
														    "guidance_scale": 5.0,
														    "output_type": "numpy",
														    "strength": 0.75,
														}
														return inputs

							def 					__snake_case      (							self :						Dict       )			->					Any:
														__snake_case	:    Dict											=     "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case	:    Any											=     self.get_dummy_components()
														__snake_case	:    int											=     StableDiffusionXLImgaImgPipeline(**lowerCamelCase       )
														__snake_case	:    List[str]											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Tuple											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    Dict											=     sd_pipe(**lowerCamelCase       ).images
														__snake_case	:    Dict											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)

														__snake_case	:    str											=     np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]       )

														assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2

							def 					__snake_case      (							self :						str       )			->					Optional[Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )

							def 					__snake_case      (							self :						Any       )			->					Dict:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

							def 					__snake_case      (							self :						str       )			->					Optional[int]:
														pass






							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														__snake_case	:    str											=     self.get_dummy_components()
														__snake_case	:    List[Any]											=     StableDiffusionXLImgaImgPipeline(**lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     sd_pipe.to(lowerCamelCase       )
														__snake_case	:    int											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														# forward without prompt embeds
														__snake_case	:    List[str]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    str											=     3 * ["this is a negative prompt"]
														__snake_case	:    Any											=     negative_prompt
														__snake_case	:    Optional[Any]											=     3 * [inputs["prompt"]]

														__snake_case	:    int											=     sd_pipe(**lowerCamelCase       )
														__snake_case	:    List[Any]											=     output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														__snake_case	:    List[Any]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     3 * ["this is a negative prompt"]
														__snake_case	:    int											=     3 * [inputs.pop("prompt"       )]

														(
														    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    
														)	:    Dict											=     sd_pipe.encode_prompt(lowerCamelCase					,			negative_prompt=lowerCamelCase       )

														__snake_case	:    Tuple											=     sd_pipe(
														    **lowerCamelCase					,			prompt_embeds=lowerCamelCase					,			negative_prompt_embeds=lowerCamelCase					,			pooled_prompt_embeds=lowerCamelCase					,			negative_pooled_prompt_embeds=lowerCamelCase					,			)
														__snake_case	:    List[str]											=     output.images[0, -3:, -3:, -1]

														# make sure that it's equal
														assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4

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




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						Any					,			lowerCamelCase :						Optional[Any]="cpu"					,			lowerCamelCase :						str=torch.floataa					,			lowerCamelCase :						int=0       )			->					Dict:
														__snake_case	:    int											=     torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     np.random.RandomState(lowerCamelCase       ).standard_normal((1, 4, 64, 64)       )
														__snake_case	:    Optional[Any]											=     torch.from_numpy(lowerCamelCase       ).to(device=lowerCamelCase					,			dtype=lowerCamelCase       )
														__snake_case	:    List[str]											=     {
														    "prompt": "a photograph of an astronaut riding a horse",
														    "latents": latents,
														    "generator": generator,
														    "num_inference_steps": 3,
														    "guidance_scale": 7.5,
														    "output_type": "numpy",
														}
														return inputs






							def 					__snake_case      (							self :						str       )			->					Any:
														__snake_case	:    List[str]											=     DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"       )
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    int											=     self.get_inputs(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     pipe(**lowerCamelCase       ).images
														__snake_case	:    Any											=     image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    Optional[int]											=     np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
														assert np.abs(image_slice - expected_slice       ).max() < 7E-3

