


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

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 _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	List[str]     =			CycleDiffusionPipeline
       lowercase_ :	Dict     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           """negative_prompt""",
           """height""",
           """width""",
           """negative_prompt_embeds""",
       }
       lowercase_ :	int     =			PipelineTesterMixin.required_optional_params - {"""latents"""}
       lowercase_ :	Any     =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
       lowercase_ :	Any     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
       lowercase_ :	Optional[int]     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							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'),      cross_attention_dim=32,      )
           _lowercase       :							List[str]      		=		DDIMScheduler(
               beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      beta_schedule='scaled_linear',      num_train_timesteps=10_00,      clip_sample=lowerCamelCase,      set_alpha_to_one=lowerCamelCase,      )
           torch.manual_seed(0)
           _lowercase       :							List[Any]      		=		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,      )
           torch.manual_seed(0)
           _lowercase       :							List[str]      		=		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=10_00,      )
           _lowercase       :							List[Any]      		=		CLIPTextModel(lowerCamelCase)
           _lowercase       :							List[str]      		=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _lowercase       :							int      		=		{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   str:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor((1, 3, 32, 32),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image / 2 + 0.5
           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							str      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							List[str]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							str      		=		{
               'prompt': 'An astronaut riding an elephant',
               'source_prompt': 'An astronaut riding a horse',
               'image': image,
               'generator': generator,
               'num_inference_steps': 2,
               'eta': 0.1,
               'strength': 0.8,
               'guidance_scale': 3,
               'source_guidance_scale': 1,
               'output_type': 'numpy',
           }
           return inputs
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator

           _lowercase       :							str      		=		self.get_dummy_components()
           _lowercase       :							str      		=		CycleDiffusionPipeline(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Dict      		=		pipe(**lowerCamelCase)
           _lowercase       :							Any      		=		output.images

           _lowercase       :							Any      		=		images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           _lowercase       :							Tuple      		=		np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.get_dummy_components()
           for name, module in components.items():
               if hasattr(lowerCamelCase,      'half'):
                   _lowercase       :							Optional[int]      		=		module.half()
           _lowercase       :							List[str]      		=		CycleDiffusionPipeline(**lowerCamelCase)
           _lowercase       :							int      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		output.images

           _lowercase       :							Optional[Any]      		=		images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           _lowercase       :							List[Any]      		=		np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_save_load_local()
       @unittest.skip('non-deterministic pipeline')
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_inference_batch_single_identical()
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_dict_tuple_outputs_equivalent()
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return super().test_save_load_optional_components()




       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return super().test_attention_slicing_forward_pass()






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




           """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'
               '/cycle-diffusion/black_colored_car.png')
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy')
           _lowercase       :							str      		=		init_image.resize((5_12, 5_12))

           _lowercase       :							Dict      		=		'CompVis/stable-diffusion-v1-4'
           _lowercase       :							Optional[int]      		=		DDIMScheduler.from_pretrained(lowerCamelCase,      subfolder='scheduler')
           _lowercase       :							Optional[int]      		=		CycleDiffusionPipeline.from_pretrained(
               lowerCamelCase,      scheduler=lowerCamelCase,      safety_checker=lowerCamelCase,      torch_dtype=torch.floataa,      revision='fp16')

           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A black colored car'
           _lowercase       :							Union[str, Any]      		=		'A blue colored car'

           _lowercase       :							Tuple      		=		torch.manual_seed(0)
           _lowercase       :							Tuple      		=		pipe(
               prompt=lowerCamelCase,      source_prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=1_00,      eta=0.1,      strength=0.8_5,      guidance_scale=3,      source_guidance_scale=1,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Optional[int]      		=		output.images

           # the values aren't exactly equal, but the images look the same visually
           assert np.abs(image - expected_image).max() < 5E-1




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




           """simple docstring"""

           _lowercase       :							List[Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/cycle-diffusion/black_colored_car.png')
           _lowercase       :							Union[str, Any]      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy')
           _lowercase       :							Optional[Any]      		=		init_image.resize((5_12, 5_12))

           _lowercase       :							Optional[Any]      		=		'CompVis/stable-diffusion-v1-4'
           _lowercase       :							Tuple      		=		DDIMScheduler.from_pretrained(lowerCamelCase,      subfolder='scheduler')
           _lowercase       :							Any      		=		CycleDiffusionPipeline.from_pretrained(lowerCamelCase,      scheduler=lowerCamelCase,      safety_checker=lowerCamelCase)

           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A black colored car'
           _lowercase       :							Tuple      		=		'A blue colored car'

           _lowercase       :							int      		=		torch.manual_seed(0)
           _lowercase       :							Tuple      		=		pipe(
               prompt=lowerCamelCase,      source_prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=1_00,      eta=0.1,      strength=0.8_5,      guidance_scale=3,      source_guidance_scale=1,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Tuple      		=		output.images

           assert np.abs(image - expected_image).max() < 2E-2






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


SCREAMING_SNAKE_CASE       :      Any												= logging.get_logger(__name__)

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

SCREAMING_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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      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"
        ),
    },
}

SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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






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






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


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

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


SCREAMING_SNAKE_CASE       :      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 _lowerCamelCase:
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   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:
               _lowercase       :							Dict      		=		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,      )
           _lowercase       :							Union[str, Any]      		=		titles if not isinstance(lowerCamelCase,      lowerCamelCase) else [titles]
           _lowercase       :							Tuple      		=		texts if not isinstance(lowerCamelCase,      lowerCamelCase) else [texts]
           _lowercase       :							Optional[Any]      		=		len(lowerCamelCase)
           _lowercase       :							Any      		=		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.''')
           _lowercase       :							Any      		=		super().__call__(lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							Tuple      		=		super().__call__(lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							int      		=		{
               '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:
               _lowercase       :							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])
               _lowercase       :							Union[str, Any]      		=		attention_mask
           return self.pad(lowerCamelCase,      padding=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 16,      lowerCamelCase = 64,      lowerCamelCase = 4,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

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

               _lowercase       :							Tuple      		=		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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

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


