



"""simple docstring"""

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

	if is_vision_available():
		from transformers import MaskFormerImageProcessor

if is_vision_available():
	from PIL import Image




class SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase						,     _lowercase=2						,     _lowercase=True						,     _lowercase=False						,     _lowercase=10						,     _lowercase=3						,     _lowercase=32 * 4						,     _lowercase=32 * 6						,     _lowercase=4						,     _lowercase=32						,     ):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       parent
													__a		:		Union[str, Any]									=       batch_size
													__a		:		List[str]									=       is_training
													__a		:		Optional[Any]									=       use_auxiliary_loss
													__a		:		List[str]									=       num_queries
													__a		:		Tuple									=       num_channels
													__a		:		Any									=       min_size
													__a		:		int									=       max_size
													__a		:		str									=       num_labels
													__a		:		str									=       mask_feature_size



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
													    _lowercase							)

													__a		:		Dict									=       torch.ones([self.batch_size, self.min_size, self.max_size]						,     device=_lowercase							)

													__a		:		str									=       (
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,     device=_lowercase							) > 0.5
													).float()
													__a		:		Dict									=       (torch.rand((self.batch_size, self.num_labels)						,     device=_lowercase							) > 0.5).long()

													__a		:		Optional[Any]									=       self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return MaskFormerConfig.from_backbone_and_decoder_configs(
													    backbone_config=SwinConfig(
													        depths=[1, 1, 1, 1]						,     )						,     decoder_config=DetrConfig(
													        decoder_ffn_dim=128						,     num_queries=self.num_queries						,     decoder_attention_heads=2						,     d_model=self.mask_feature_size						,     )						,     mask_feature_size=self.mask_feature_size						,     fpn_feature_size=self.mask_feature_size						,     num_channels=self.num_channels						,     num_labels=self.num_labels						,     )



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a							,     __a							,     __a							,     __a		:		List[str]									=       self.prepare_config_and_inputs()
													__a		:		Dict									=       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
													return config, inputs_dict



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       output.encoder_hidden_states
													__a		:		Optional[Any]									=       output.pixel_decoder_hidden_states
													__a		:		Optional[Any]									=       output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(_lowercase							)						,     len(config.backbone_config.depths							)							)
													self.parent.assertTrue(len(_lowercase							)						,     len(config.backbone_config.depths							)							)
													self.parent.assertTrue(len(_lowercase							)						,     config.decoder_config.decoder_layers							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=False							):





													'''simple docstring'''

													with torch.no_grad():
																				__a		:		Any									=       MaskFormerModel(config=_lowercase							)
																				model.to(_lowercase							)
																				model.eval()

																				__a		:		Dict									=       model(pixel_values=_lowercase						,     pixel_mask=_lowercase							)
																				__a		:		str									=       model(_lowercase						,     output_hidden_states=_lowercase							)
													# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
													# encoder and pixel decoder
													self.parent.assertEqual(
													    output.transformer_decoder_last_hidden_state.shape						,     (self.batch_size, self.num_queries, self.mask_feature_size)						,     )
													# let's ensure the other two hidden state exists
													self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None							)
													self.parent.assertTrue(output.encoder_last_hidden_state is not None							)

													if output_hidden_states:
																				self.check_output_hidden_state(_lowercase						,     _lowercase							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       MaskFormerForInstanceSegmentation(config=_lowercase							)
													model.to(_lowercase							)
													model.eval()

													def comm_check_on_output(_lowercase							):
																				# let's still check that all the required stuff is there
																				self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None							)
																				self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None							)
																				self.parent.assertTrue(result.encoder_last_hidden_state is not None							)
																				# okay, now we need to check the logits shape
																				# due to the encoder compression, masks have a //4 spatial size
																				self.parent.assertEqual(
																				    result.masks_queries_logits.shape						,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,     )
																				# + 1 for null class
																				self.parent.assertEqual(
																				    result.class_queries_logits.shape						,     (self.batch_size, self.num_queries, self.num_labels + 1)							)

													with torch.no_grad():
																				__a		:		Optional[int]									=       model(pixel_values=_lowercase						,     pixel_mask=_lowercase							)
																				__a		:		List[Any]									=       model(_lowercase							)

																				comm_check_on_output(_lowercase							)

																				__a		:		Dict									=       model(
																				    pixel_values=_lowercase						,     pixel_mask=_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							)

													comm_check_on_output(_lowercase							)

													self.parent.assertTrue(result.loss is not None							)
													self.parent.assertEqual(result.loss.shape						,     torch.Size([1]							)							)







@require_torch
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
						_lowerCAmelCase							=					(
						    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       MaskFormerModelTester(self							)
													__a		:		int									=       ConfigTester(self						,     config_class=_lowercase						,     has_text_modality=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Dict									=       self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskformer_model(_lowercase						,     **_lowercase						,     output_hidden_states=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowercase							)



						@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer is not a generative model"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer does not use token embeddings"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@require_torch_multi_gpu
						@unittest.skip(
						    reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__a		:		Optional[int]									=       model_class(_lowercase							)
																				__a		:		Optional[int]									=       inspect.signature(model.forward							)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__a		:		Union[str, Any]									=       [*signature.parameters.keys()]

																				__a		:		Tuple									=       ["""pixel_values"""]
																				self.assertListEqual(arg_names[:1]						,     _lowercase							)



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for model_name in ["facebook/maskformer-swin-small-coco"]:
																				__a		:		str									=       MaskFormerModel.from_pretrained(_lowercase							)
																				self.assertIsNotNone(_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       (self.model_tester.min_size,) * 2
													__a		:		Optional[int]									=       {
													    """pixel_values""": torch.randn((2, 3, *size)						,     device=_lowercase							),
													    """mask_labels""": torch.randn((2, 10, *size)						,     device=_lowercase							),
													    """class_labels""": torch.zeros(2						,     10						,     device=_lowercase							).long(),
													}

													__a		:		Union[str, Any]									=       MaskFormerForInstanceSegmentation(MaskFormerConfig()							).to(_lowercase							)
													__a		:		Optional[Any]									=       model(**_lowercase							)
													self.assertTrue(outputs.loss is not None							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		str									=       self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskformer_model(_lowercase						,     **_lowercase						,     output_hidden_states=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__a		:		Optional[Any]									=       model_class(_lowercase							).to(_lowercase							)
																				__a		:		int									=       model(**_lowercase						,     output_attentions=_lowercase							)
																				self.assertTrue(outputs.attentions is not None							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													if not self.model_tester.is_training:
																				return
													# only MaskFormerForInstanceSegmentation has the loss
													__a		:		Union[str, Any]									=       self.all_model_classes[1]
													__a							,     __a							,     __a							,     __a							,     __a		:		List[Any]									=       self.model_tester.prepare_config_and_inputs()

													__a		:		Tuple									=       model_class(_lowercase							)
													model.to(_lowercase							)
													model.train()

													__a		:		Optional[Any]									=       model(_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							).loss
													loss.backward()





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.all_model_classes[1]
													__a							,     __a							,     __a							,     __a							,     __a		:		Tuple									=       self.model_tester.prepare_config_and_inputs()
													__a		:		Optional[int]									=       True
													__a		:		Dict									=       True

													__a		:		Union[str, Any]									=       model_class(_lowercase							)
													model.to(_lowercase							)
													model.train()

													__a		:		Optional[int]									=       model(_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							)

													__a		:		str									=       outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													__a		:		List[Any]									=       outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()
													# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
													__a		:		Tuple									=       outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													__a		:		Tuple									=       outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=_lowercase							)

													self.assertIsNotNone(encoder_hidden_states.grad							)
													self.assertIsNotNone(pixel_decoder_hidden_states.grad							)
													self.assertIsNotNone(transformer_decoder_hidden_states.grad							)
													self.assertIsNotNone(attentions.grad							)







lowercase__			   =  1e-4





def       __magic_name__    (   ):
							__a		:		Optional[Any]									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
							return image







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



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return (
													    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    if is_vision_available()
													    else None
													)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""							).to(_lowercase							)
													__a		:		int									=       self.default_image_processor
													__a		:		List[Any]									=       prepare_img()
													__a		:		Any									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		Dict									=       inputs["""pixel_values"""].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
													# check size
													self.assertEqual(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		List[Any]									=       model(**_lowercase							)

													__a		:		Dict									=       torch.tensor(
													    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)

													__a		:		int									=       torch.tensor(
													    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)

													__a		:		List[Any]									=       torch.tensor(
													    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		int									=       self.default_image_processor
													__a		:		Optional[Any]									=       prepare_img()
													__a		:		Optional[Any]									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		Union[str, Any]									=       inputs["""pixel_values"""].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
													# check size
													self.assertEqual(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		Optional[int]									=       model(**_lowercase							)
													# masks_queries_logits
													__a		:		Optional[int]									=       outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,     )
													__a		:		Dict									=       [
													    [-1.373_7124, -1.772_4937, -1.936_4233],
													    [-1.597_7281, -1.986_7939, -2.152_3695],
													    [-1.579_5398, -1.926_9832, -2.09_3942],
													]
													__a		:		Dict									=       torch.tensor(_lowercase							).to(_lowercase							)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)
													# class_queries_logits
													__a		:		Union[str, Any]									=       outputs.class_queries_logits
													self.assertEqual(
													    class_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
													__a		:		List[str]									=       torch.tensor(
													    [
													        [1.6512e00, -5.2572e00, -3.3519e00],
													        [3.6169e-02, -5.9025e00, -2.9313e00],
													        [1.0766e-04, -7.7630e00, -5.1263e00],
													    ]							).to(_lowercase							)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		List[str]									=       self.default_image_processor
													__a		:		Tuple									=       prepare_img()
													__a		:		int									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		str									=       inputs["""pixel_values"""].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
													# check size
													self.assertEqual(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		Tuple									=       model(**_lowercase							)
													# masks_queries_logits
													__a		:		Optional[int]									=       outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,     )
													__a		:		Optional[int]									=       [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
													__a		:		Dict									=       torch.tensor(_lowercase							).to(_lowercase							)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)
													# class_queries_logits
													__a		:		int									=       outputs.class_queries_logits
													self.assertEqual(
													    class_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
													__a		:		Optional[Any]									=       torch.tensor(
													    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]							).to(_lowercase							)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		Optional[Any]									=       self.default_image_processor

													__a		:		Tuple									=       image_processor(
													    [np.zeros((3, 800, 1333)							), np.zeros((3, 800, 1333)							)]						,     segmentation_maps=[np.zeros((384, 384)							).astype(np.floataa							), np.zeros((384, 384)							).astype(np.floataa							)]						,     return_tensors="""pt"""						,     )

													__a		:		Optional[int]									=       inputs["""pixel_values"""].to(_lowercase							)
													__a		:		List[Any]									=       [el.to(_lowercase							) for el in inputs["""mask_labels"""]]
													__a		:		Optional[int]									=       [el.to(_lowercase							) for el in inputs["""class_labels"""]]

													with torch.no_grad():
																				__a		:		Union[str, Any]									=       model(**_lowercase							)

													self.assertTrue(outputs.loss is not None							)






"""simple docstring"""

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowercase__			   =  logging.get_logger(__name__)

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

lowercase__			   =  {
    "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"
        ),
    },
}
lowercase__			   =  {
    "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"
        ),
    },
}
lowercase__			   =  {
    "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"
        ),
    },
}

lowercase__			   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowercase__			   =  {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowercase__			   =  {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


lowercase__			   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__			   =  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__			   =  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_lowerCAmelCase							=					DPRContextEncoderTokenizer







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						_lowerCAmelCase							=					DPRQuestionEncoderTokenizer







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

lowercase__			   =  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


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




@add_start_docstrings(__snake_case      )
class SCREAMING_SNAKE_CASE__					:



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





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 16						,     _lowercase = 64						,     _lowercase = 4						,     ):





													'''simple docstring'''

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

																				__a		:		List[Any]									=       self._get_best_spans(
																				    start_logits=start_logits[doc_id][passage_offset:sequence_len]						,     end_logits=end_logits[doc_id][passage_offset:sequence_len]						,     max_answer_length=_lowercase						,     top_spans=_lowercase						,     )
																				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=_lowercase						,     start_index=_lowercase						,     end_index=_lowercase						,     text=self.decode(sequence_ids[start_index : end_index + 1]							)						,     )							)
																				if len(_lowercase							) >= num_spans:
																											break
													return nbest_spans_predictions[:num_spans]





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       []
													for start_index, start_score in enumerate(_lowercase							):
																				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)							)
													__a		:		str									=       sorted(_lowercase						,     key=lambda _lowercase							: x[1]						,     reverse=_lowercase							)
													__a		:		Union[str, Any]									=       []
													for (start_index, end_index), score in scores:
																				assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
																				__a		:		List[str]									=       end_index - start_index + 1
																				assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
																				if any(
																				    start_index <= prev_start_index <= prev_end_index <= end_index
																				    or prev_start_index <= start_index <= end_index <= prev_end_index
																				    for (prev_start_index, prev_end_index) in chosen_span_intervals							):
																											continue
																				chosen_span_intervals.append((start_index, end_index)							)

																				if len(_lowercase							) == top_spans:
																											break
													return chosen_span_intervals







@add_end_docstrings(__snake_case      )
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case      ):
						_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"]
						_lowerCAmelCase							=					DPRReaderTokenizer

