


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__  :Any   			=							logging.get_logger(__name__)

lowerCAmelCase__  :Union[str, Any]   			=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__  :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"
        ),
    },
}
lowerCAmelCase__  :Dict   			=							{
    "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__  :str   			=							{
    "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__  :Dict   			=							{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
lowerCAmelCase__  :Optional[Any]   			=							{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
lowerCAmelCase__  :Dict   			=							{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


lowerCAmelCase__  :List[Any]   			=							{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase__  :Dict   			=							{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase__  :Dict   			=							{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class 		__a      (				_a			):
		_a	:  Any				=	VOCAB_FILES_NAMES
		_a	:  Optional[int]				=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		_a	:  str				=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a	:  str				=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 		__a      (				_a			):
		_a	:  Optional[int]				=	VOCAB_FILES_NAMES
		_a	:  Any				=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		_a	:  str				=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a	:  Union[str, Any]				=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



lowerCAmelCase__  :Optional[int]   			=							collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCAmelCase__  :Any   			=							collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCAmelCase__  :str   			=							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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(_a			)
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			,    ) ->       BatchEncoding:




									"""simple docstring"""





									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:
																_UpperCAmelCase 		=			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			,    )
									_UpperCAmelCase 		=			titles if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) else [titles]
									_UpperCAmelCase 		=			texts if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) else [texts]
									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			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.'''						)
									_UpperCAmelCase 		=			super().__call__(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    truncation=_SCREAMING_SNAKE_CASE						)['input_ids']
									_UpperCAmelCase 		=			super().__call__(_SCREAMING_SNAKE_CASE			,    add_special_tokens=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    truncation=_SCREAMING_SNAKE_CASE						)['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(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									    ]
									}
									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(_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    max_length=_SCREAMING_SNAKE_CASE			,    return_tensors=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = 16			,    _SCREAMING_SNAKE_CASE = 64			,    _SCREAMING_SNAKE_CASE = 4			,    ) ->       List[DPRSpanPrediction]:




									"""simple docstring"""





									_UpperCAmelCase 		=			reader_input['input_ids']
									_UpperCAmelCase 		=			reader_output[:3]
									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			sorted(range(_SCREAMING_SNAKE_CASE						)			,    reverse=_SCREAMING_SNAKE_CASE			,    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(_SCREAMING_SNAKE_CASE						)

																_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=_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 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    ) ->       List[DPRSpanPrediction]:




									"""simple docstring"""





									_UpperCAmelCase 		=			[]
									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)						)
									_UpperCAmelCase 		=			sorted(_SCREAMING_SNAKE_CASE			,    key=lambda _SCREAMING_SNAKE_CASE						: x[1]			,    reverse=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			[]
									for (start_index, end_index), score in scores:
																if start_index > end_index:
																							raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''						)
																_UpperCAmelCase 		=			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(_a			)
class 		__a      (				_a    ,      _a			):
		_a	:  Union[str, Any]				=	VOCAB_FILES_NAMES
		_a	:  Any				=	READER_PRETRAINED_VOCAB_FILES_MAP
		_a	:  Dict				=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a	:  Optional[int]				=	READER_PRETRAINED_INIT_CONFIGURATION
		_a	:  str				=	["""input_ids""", """attention_mask"""]




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		1
           _lowercase       :							Any      		=		3
           _lowercase       :							Tuple      		=		(32, 32)

           _lowercase       :							Tuple      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		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'),      cross_attention_dim=32,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							str      		=		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,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		RobertaSeriesConfig(
               hidden_size=32,      project_dim=32,      intermediate_size=37,      layer_norm_eps=1E-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=50_06,      )
           return RobertaSeriesModelWithTransformation(lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           def extract(*lowerCamelCase,      **lowerCamelCase):






               class _lowerCamelCase:
                      def __init__(					self)       ->   Optional[Any]:




                          """simple docstring"""

                          _lowercase       :							Optional[int]      		=		torch.ones([0])




                      def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




                          """simple docstring"""

                          self.pixel_values.to(lowerCamelCase)
                          return self


               return Out()

           return extract
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Union[str, Any]      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.dummy_vae
           _lowercase       :							List[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Any      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Tuple      		=		77

           _lowercase       :							int      		=		self.dummy_image.to(lowerCamelCase)
           _lowercase       :							int      		=		init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[Any]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Dict      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Any      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      )

           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Optional[Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							int      		=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Tuple      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							str      		=		self.dummy_vae
           _lowercase       :							Optional[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Optional[Any]      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Optional[Any]      		=		77

           _lowercase       :							str      		=		self.dummy_image.to(lowerCamelCase)

           # put models in fp16
           _lowercase       :							List[str]      		=		unet.half()
           _lowercase       :							List[Any]      		=		vae.half()
           _lowercase       :							Any      		=		bert.half()

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[str]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Any      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Optional[Any]      		=		torch.manual_seed(0)
           _lowercase       :							Union[str, Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      ).images

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




       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           # resize to resolution that is divisible by 8 but not 16 or 32
           _lowercase       :							str      		=		init_image.resize((7_60, 5_04))

           _lowercase       :							Optional[int]      		=		'BAAI/AltDiffusion'
           _lowercase       :							str      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							Any      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[str]      		=		output.images[0]

           _lowercase       :							Tuple      		=		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           _lowercase       :							Optional[Any]      		=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0])

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






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							str      		=		init_image.resize((7_68, 5_12))
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

           _lowercase       :							str      		=		'BAAI/AltDiffusion'
           _lowercase       :							Optional[Any]      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							int      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Union[str, Any]      		=		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image).max() < 1E-2


