






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


  @property
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    (32, 32)

         _UpperCAmelCase							=    floats_tensor((batch_size, num_channels) + sizes       ,				rng=random.Random(0							)							).to(lowerCamelCase							)
         return image


  @property
  def  lowerCamelCase       (      self		:			List[Any]							)						->	str:


         """simple docstring"""
         torch.manual_seed(0							)
         _UpperCAmelCase							=    UNetaDConditionModel(
             block_out_channels=(32, 32, 64)       ,				layers_per_block=2       ,				sample_size=32       ,				in_channels=7       ,				out_channels=4       ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")       ,				up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")       ,				cross_attention_dim=32       ,				attention_head_dim=8       ,				use_linear_projection=lowerCamelCase       ,				only_cross_attention=(True, True, False)       ,				num_class_embeds=100       ,				)
         return model


  @property
  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         torch.manual_seed(0							)
         _UpperCAmelCase							=    AutoencoderKL(
             block_out_channels=[32, 32, 64]       ,				in_channels=3       ,				out_channels=3       ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,				latent_channels=4       ,				)
         return model


  @property
  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         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       ,				)
         return CLIPTextModel(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
         _UpperCAmelCase							=    self.dummy_cond_unet_upscale
         _UpperCAmelCase							=    DDPMScheduler()
         _UpperCAmelCase							=    DDIMScheduler(prediction_type="""v_prediction"""							)
         _UpperCAmelCase							=    self.dummy_vae
         _UpperCAmelCase							=    self.dummy_text_encoder
         _UpperCAmelCase							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

         _UpperCAmelCase							=    self.dummy_image.cpu().permute(0       ,				2       ,				3       ,				1							)[0]
         _UpperCAmelCase							=    Image.fromarray(np.uinta(lowerCamelCase							)							).convert("""RGB"""							).resize((64, 64)							)

         # make sure here that pndm scheduler skips prk
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline(
             unet=lowerCamelCase       ,				low_res_scheduler=lowerCamelCase       ,				scheduler=lowerCamelCase       ,				vae=lowerCamelCase       ,				text_encoder=lowerCamelCase       ,				tokenizer=lowerCamelCase       ,				max_noise_level=350       ,				)
         _UpperCAmelCase							=    sd_pipe.to(lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase							)

         _UpperCAmelCase							=    """A painting of a squirrel eating a burger"""
         _UpperCAmelCase							=    torch.Generator(device=lowerCamelCase							).manual_seed(0							)
         _UpperCAmelCase							=    sd_pipe(
             [prompt]       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				guidance_scale=6.0       ,				noise_level=20       ,				num_inference_steps=2       ,				output_type="""np"""       ,				)

         _UpperCAmelCase							=    output.images

         _UpperCAmelCase							=    torch.Generator(device=lowerCamelCase							).manual_seed(0							)
         _UpperCAmelCase							=    sd_pipe(
             [prompt]       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				guidance_scale=6.0       ,				noise_level=20       ,				num_inference_steps=2       ,				output_type="""np"""       ,				return_dict=lowerCamelCase       ,				)[0]

         _UpperCAmelCase							=    image[0, -3:, -3:, -1]
         _UpperCAmelCase							=    image_from_tuple[0, -3:, -3:, -1]

         _UpperCAmelCase							=    low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         _UpperCAmelCase							=    np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]							)

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


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
         _UpperCAmelCase							=    self.dummy_cond_unet_upscale
         _UpperCAmelCase							=    DDPMScheduler()
         _UpperCAmelCase							=    DDIMScheduler(prediction_type="""v_prediction"""							)
         _UpperCAmelCase							=    self.dummy_vae
         _UpperCAmelCase							=    self.dummy_text_encoder
         _UpperCAmelCase							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

         _UpperCAmelCase							=    self.dummy_image.cpu().permute(0       ,				2       ,				3       ,				1							)[0]
         _UpperCAmelCase							=    Image.fromarray(np.uinta(lowerCamelCase							)							).convert("""RGB"""							).resize((64, 64)							)

         # make sure here that pndm scheduler skips prk
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline(
             unet=lowerCamelCase       ,				low_res_scheduler=lowerCamelCase       ,				scheduler=lowerCamelCase       ,				vae=lowerCamelCase       ,				text_encoder=lowerCamelCase       ,				tokenizer=lowerCamelCase       ,				max_noise_level=350       ,				)
         _UpperCAmelCase							=    sd_pipe.to(lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase							)

         _UpperCAmelCase							=    """A painting of a squirrel eating a burger"""
         _UpperCAmelCase							=    sd_pipe(
             2 * [prompt]       ,				image=2 * [low_res_image]       ,				guidance_scale=6.0       ,				noise_level=20       ,				num_inference_steps=2       ,				output_type="""np"""       ,				)
         _UpperCAmelCase							=    output.images
         assert image.shape[0] == 2

         _UpperCAmelCase							=    torch.Generator(device=lowerCamelCase							).manual_seed(0							)
         _UpperCAmelCase							=    sd_pipe(
             [prompt]       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				num_images_per_prompt=2       ,				guidance_scale=6.0       ,				noise_level=20       ,				num_inference_steps=2       ,				output_type="""np"""       ,				)
         _UpperCAmelCase							=    output.images
         assert image.shape[0] == 2





  @unittest.skipIf(torch_device != """cuda"""       ,				"""This test requires a GPU"""							)
  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.dummy_cond_unet_upscale
         _UpperCAmelCase							=    DDPMScheduler()
         _UpperCAmelCase							=    DDIMScheduler(prediction_type="""v_prediction"""							)
         _UpperCAmelCase							=    self.dummy_vae
         _UpperCAmelCase							=    self.dummy_text_encoder
         _UpperCAmelCase							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

         _UpperCAmelCase							=    self.dummy_image.cpu().permute(0       ,				2       ,				3       ,				1							)[0]
         _UpperCAmelCase							=    Image.fromarray(np.uinta(lowerCamelCase							)							).convert("""RGB"""							).resize((64, 64)							)

         # put models in fp16, except vae as it overflows in fp16
         _UpperCAmelCase							=    unet.half()
         _UpperCAmelCase							=    text_encoder.half()

         # make sure here that pndm scheduler skips prk
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline(
             unet=lowerCamelCase       ,				low_res_scheduler=lowerCamelCase       ,				scheduler=lowerCamelCase       ,				vae=lowerCamelCase       ,				text_encoder=lowerCamelCase       ,				tokenizer=lowerCamelCase       ,				max_noise_level=350       ,				)
         _UpperCAmelCase							=    sd_pipe.to(lowerCamelCase							)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase							)

         _UpperCAmelCase							=    """A painting of a squirrel eating a burger"""
         _UpperCAmelCase							=    torch.manual_seed(0							)
         _UpperCAmelCase							=    sd_pipe(
             [prompt]       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				num_inference_steps=2       ,				output_type="""np"""       ,				).images

         _UpperCAmelCase							=    low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)



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

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			str							)						->	List[str]:


         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""							)
         _UpperCAmelCase							=    load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
             """/upsampled_cat.npy"""							)

         _UpperCAmelCase							=    """stabilityai/stable-diffusion-x4-upscaler"""
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase							)
         pipe.to(lowerCamelCase							)
         pipe.set_progress_bar_config(disable=lowerCamelCase							)
         pipe.enable_attention_slicing()

         _UpperCAmelCase							=    """a cat sitting on a park bench"""

         _UpperCAmelCase							=    torch.manual_seed(0							)
         _UpperCAmelCase							=    pipe(
             prompt=lowerCamelCase       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				output_type="""np"""       ,				)
         _UpperCAmelCase							=    output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image							).max() < 1E-3


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""							)
         _UpperCAmelCase							=    load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
             """/upsampled_cat_fp16.npy"""							)

         _UpperCAmelCase							=    """stabilityai/stable-diffusion-x4-upscaler"""
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline.from_pretrained(
             lowerCamelCase       ,				torch_dtype=torch.floataa       ,				)
         pipe.to(lowerCamelCase							)
         pipe.set_progress_bar_config(disable=lowerCamelCase							)
         pipe.enable_attention_slicing()

         _UpperCAmelCase							=    """a cat sitting on a park bench"""

         _UpperCAmelCase							=    torch.manual_seed(0							)
         _UpperCAmelCase							=    pipe(
             prompt=lowerCamelCase       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				output_type="""np"""       ,				)
         _UpperCAmelCase							=    output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image							).max() < 5E-1





  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         _UpperCAmelCase							=    load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-upscale/low_res_cat.png"""							)

         _UpperCAmelCase							=    """stabilityai/stable-diffusion-x4-upscaler"""
         _UpperCAmelCase							=    StableDiffusionUpscalePipeline.from_pretrained(
             lowerCamelCase       ,				torch_dtype=torch.floataa       ,				)
         pipe.to(lowerCamelCase							)
         pipe.set_progress_bar_config(disable=lowerCamelCase							)
         pipe.enable_attention_slicing(1							)
         pipe.enable_sequential_cpu_offload()

         _UpperCAmelCase							=    """a cat sitting on a park bench"""

         _UpperCAmelCase							=    torch.manual_seed(0							)
         _UpperCAmelCase							=    pipe(
             prompt=lowerCamelCase       ,				image=lowerCamelCase       ,				generator=lowerCamelCase       ,				num_inference_steps=5       ,				output_type="""np"""       ,				)

         _UpperCAmelCase							=    torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9







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


__a:     Tuple   					=					logging.get_logger(__name__)

__a:     Optional[Any]   					=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__a:     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'''
        ),
    },
}
__a:     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'''
        ),
    },
}
__a:     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'''
        ),
    },
}

__a:     Dict   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__a:     List[str]   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__a:     Dict   					=					{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__a:     Optional[int]   					=					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Tuple   					=					{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__a:     Optional[int]   					=					{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



__a:     List[Any]   					=					collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__a:     Optional[int]   					=					collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__a:     Optional[Any]   					=					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(UpperCAmelCase     )
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __call__(      self		:			int       ,				lowerCamelCase		:			int       ,				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[int]       ,				)						->	BatchEncoding:


         """simple docstring"""
         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:
                _UpperCAmelCase							=    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       ,				)
         _UpperCAmelCase							=    titles if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [titles]
         _UpperCAmelCase							=    texts if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [texts]
         _UpperCAmelCase							=    len(lowerCamelCase							)
         _UpperCAmelCase							=    questions if not isinstance(lowerCamelCase       ,				lowerCamelCase							) else [questions] * n_passages
         if len(lowerCamelCase							) != len(lowerCamelCase							):
                raise ValueError(
                    f"""There should be as many titles than texts but got {len(lowerCamelCase							)} titles and {len(lowerCamelCase							)} texts."""							)
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""input_ids"""]
         _UpperCAmelCase							=    super().__call__(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase							)["""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(lowerCamelCase       ,				lowerCamelCase							)
             ]
         }
         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(lowerCamelCase       ,				padding=lowerCamelCase       ,				max_length=lowerCamelCase       ,				return_tensors=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			BatchEncoding       ,				lowerCamelCase		:			DPRReaderOutput       ,				lowerCamelCase		:			int = 16       ,				lowerCamelCase		:			int = 64       ,				lowerCamelCase		:			int = 4       ,				)						->	List[DPRSpanPrediction]:


         """simple docstring"""
         _UpperCAmelCase							=    reader_input["""input_ids"""]
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    reader_output[:3]
         _UpperCAmelCase							=    len(lowerCamelCase							)
         _UpperCAmelCase							=    sorted(range(lowerCamelCase							)       ,				reverse=lowerCamelCase       ,				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(lowerCamelCase							)

                _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=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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				)						->	List[DPRSpanPrediction]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         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)							)
         _UpperCAmelCase							=    sorted(lowerCamelCase       ,				key=lambda lowerCamelCase							: x[1]       ,				reverse=lowerCamelCase							)
         _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(lowerCamelCase							) == top_spans:
                       break
         return chosen_span_intervals



@add_end_docstrings(UpperCAmelCase     )
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  READER_PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  READER_PRETRAINED_INIT_CONFIGURATION
  _lowerCamelCase    =  ['''input_ids''', '''attention_mask''']