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


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[Any]       =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    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"
        ),
    },
}
UpperCamelCase     :    Any       =			{
    "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"
        ),
    },
}
UpperCamelCase     :    Optional[Any]       =			{
    "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"
        ),
    },
}

UpperCamelCase     :    int       =			{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
UpperCamelCase     :    Optional[int]       =			{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
UpperCamelCase     :    Optional[Any]       =			{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


UpperCamelCase     :    Tuple       =			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase     :    Any       =			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase     :    str       =			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						DPRContextEncoderTokenizer


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						DPRQuestionEncoderTokenizer


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

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


UpperCamelCase     :    Tuple       =			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(__SCREAMING_SNAKE_CASE      )
class __lowerCAmelCase		:





     def __call__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      if titles is None and texts is None:
       return super().__call__(
           __UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase     ,					return_attention_mask=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      elif titles is None or texts is None:
       __UpperCamelCase      =	titles if texts is None else texts
       return super().__call__(
           __UpperCAmelCase     ,					__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase     ,					return_attention_mask=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      __UpperCamelCase      =	titles if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [titles]
      __UpperCamelCase      =	texts if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [texts]
      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	questions if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else [questions] * n_passages
      assert len(__UpperCAmelCase				) == len(
          __UpperCAmelCase				), F'There should be as many titles than texts but got {len(__UpperCAmelCase				)} titles and {len(__UpperCAmelCase				)} texts.'
      __UpperCamelCase      =	super().__call__(__UpperCAmelCase     ,					__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase				)['input_ids']
      __UpperCamelCase      =	super().__call__(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase				)['input_ids']
      __UpperCamelCase      =	{
          '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(__UpperCAmelCase     ,					__UpperCAmelCase				)
          ]
      }
      if return_attention_mask is not False:
       __UpperCamelCase      =	[]
       for input_ids in encoded_inputs["input_ids"]:
        attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
       __UpperCamelCase      =	attention_mask
      return self.pad(__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 16     ,					__UpperCAmelCase = 64     ,					__UpperCAmelCase = 4     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	reader_input['input_ids']
      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	reader_output[:3]
      __UpperCamelCase      =	len(__UpperCAmelCase				)
      __UpperCamelCase      =	sorted(range(__UpperCAmelCase				)     ,					reverse=__UpperCAmelCase     ,					key=relevance_logits.__getitem__				)
      __UpperCamelCase      =	[]
      for doc_id in sorted_docs:
       __UpperCamelCase      =	list(input_ids[doc_id]				)
       # assuming question & title information is at the beginning of the sequence
       __UpperCamelCase      =	sequence_ids.index(self.sep_token_id     ,					2				) + 1  # second sep id
       if sequence_ids[-1] == self.pad_token_id:
        __UpperCamelCase      =	sequence_ids.index(self.pad_token_id				)
       else:
        __UpperCamelCase      =	len(__UpperCAmelCase				)

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







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	[]
      for start_index, start_score in enumerate(__UpperCAmelCase				):
       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)				)
      __UpperCamelCase      =	sorted(__UpperCAmelCase     ,					key=lambda __UpperCAmelCase				: x[1]     ,					reverse=__UpperCAmelCase				)
      __UpperCamelCase      =	[]
      for (start_index, end_index), score in scores:
       assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
       __UpperCamelCase      =	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(__UpperCAmelCase				) == top_spans:
        break
      return chosen_span_intervals


@add_end_docstrings(__SCREAMING_SNAKE_CASE      )
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						READER_PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						READER_PRETRAINED_INIT_CONFIGURATION
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						DPRReaderTokenizer





"""simple docstring"""

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


UpperCamelCase     :    List[Any]       =			False

@skip_mps
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						StableDiffusionAttendAndExcitePipeline
     lowercase						       =						False
     lowercase						       =						TEXT_TO_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}      )
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	UNetaDConditionModel(
          block_out_channels=(32, 64)     ,					layers_per_block=1     ,					sample_size=32     ,					in_channels=4     ,					out_channels=4     ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,					cross_attention_dim=32     ,					attention_head_dim=(2, 4)     ,					use_linear_projection=__UpperCAmelCase     ,					)
      __UpperCamelCase      =	DDIMScheduler(
          beta_start=0.0_0_0_8_5     ,					beta_end=0.0_1_2     ,					beta_schedule='scaled_linear'     ,					clip_sample=__UpperCAmelCase     ,					set_alpha_to_one=__UpperCAmelCase     ,					)
      torch.manual_seed(0				)
      __UpperCamelCase      =	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				)
      __UpperCamelCase      =	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=512     ,					)
      __UpperCamelCase      =	CLIPTextModel(__UpperCAmelCase				)
      __UpperCamelCase      =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

      __UpperCamelCase      =	{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }

      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      if str(__UpperCAmelCase				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      __UpperCamelCase      =	__UpperCamelCase      =	{
          'prompt': 'a cat and a frog',
          'token_indices': [2, 5],
          'generator': generator,
          'num_inference_steps': 1,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
          'max_iter_to_alter': 2,
          'thresholds': {0: 0.7},
      }
      return inputs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'cpu'

      __UpperCamelCase      =	self.get_dummy_components()
      __UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	self.get_dummy_inputs(__UpperCAmelCase				)
      __UpperCamelCase      =	pipe(**__UpperCAmelCase				).images
      __UpperCamelCase      =	image[0, -3:, -3:, -1]

      self.assertEqual(image.shape     ,					(1, 64, 64, 3)				)
      __UpperCamelCase      =	np.array(
          [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]				)
      __UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
      self.assertLessEqual(__UpperCAmelCase     ,					1E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_consistent(batch_sizes=[1, 2]				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_single_identical(batch_size=2     ,					expected_max_diff=7E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_local(expected_max_difference=5E-4				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_optional_components(expected_max_difference=4E-4				)


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





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch.manual_seed(51				)

      __UpperCamelCase      =	StableDiffusionAttendAndExcitePipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'     ,					safety_checker=__UpperCAmelCase     ,					torch_dtype=torch.floataa				)
      pipe.to('cuda'				)

      __UpperCamelCase      =	'a painting of an elephant with glasses'
      __UpperCamelCase      =	[5, 7]

      __UpperCamelCase      =	pipe(
          prompt=__UpperCAmelCase     ,					token_indices=__UpperCAmelCase     ,					guidance_scale=7.5     ,					generator=__UpperCAmelCase     ,					num_inference_steps=5     ,					max_iter_to_alter=5     ,					output_type='numpy'     ,					).images[0]

      __UpperCamelCase      =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'				)
      assert np.abs((expected_image - image).max()				) < 5E-1



