
'''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 import BertTokenizer


_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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				str									=       VOCAB_FILES_NAMES
						_lowercase		:				Tuple									=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				Optional[int]									=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				Optional[int]									=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Any									=       VOCAB_FILES_NAMES
						_lowercase		:				Union[str, Any]									=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				Optional[Any]									=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				Any									=       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






_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"""
    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(_SCREAMING_SNAKE_CASE			)
class   UpperCAmelCase_					:






						'''simple docstring'''






						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:
																		_lowerCAmelCase  						=			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   ,       )
												_lowerCAmelCase  						=			titles if not isinstance(_lowercase   ,       _lowercase				) else [titles]
												_lowerCAmelCase  						=			texts if not isinstance(_lowercase   ,       _lowercase				) else [texts]
												_lowerCAmelCase  						=			len(_lowercase				)
												_lowerCAmelCase  						=			questions if not isinstance(_lowercase   ,       _lowercase				) else [questions] * n_passages
												if len(_lowercase				) != len(_lowercase				):
																		raise ValueError(
																		    F'There should be as many titles than texts but got {len(_lowercase				)} titles and {len(_lowercase				)} texts.'				)
												_lowerCAmelCase  						=			super().__call__(_lowercase   ,       _lowercase   ,       padding=_lowercase   ,       truncation=_lowercase				)["""input_ids"""]
												_lowerCAmelCase  						=			super().__call__(_lowercase   ,       add_special_tokens=_lowercase   ,       padding=_lowercase   ,       truncation=_lowercase				)["""input_ids"""]
												_lowerCAmelCase  						=			{
												    """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:
																		_lowerCAmelCase  						=			[]
																		for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
																		_lowerCAmelCase  						=			attention_mask
												return self.pad(_lowercase   ,       padding=_lowercase   ,       max_length=_lowercase   ,       return_tensors=_lowercase				)




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

												"""simple docstring"""



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

																		_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			[]
												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)				)
												_lowerCAmelCase  						=			sorted(_lowercase   ,       key=lambda _lowercase				: x[1]   ,       reverse=_lowercase				)
												_lowerCAmelCase  						=			[]
												for (start_index, end_index), score in scores:
																		if start_index > end_index:
																								raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'				)
																		_lowerCAmelCase  						=			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(_lowercase				) == top_spans:
																								break
												return chosen_span_intervals






@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       VOCAB_FILES_NAMES
						_lowercase		:				str									=       READER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				List[Any]									=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				Optional[Any]									=       READER_PRETRAINED_INIT_CONFIGURATION
						_lowercase		:				int									=       ['''input_ids''', '''attention_mask''']


'''simple docstring'''



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import UperNetForSemanticSegmentation
						from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=13   ,       _lowercase=32   ,       _lowercase=3   ,       _lowercase=4   ,       _lowercase=[10, 20, 30, 40]   ,       _lowercase=[2, 2, 3, 2]   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=37   ,       _lowercase="gelu"   ,       _lowercase=10   ,       _lowercase=0.02   ,       _lowercase=["stage2", "stage3", "stage4"]   ,       _lowercase=3   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			image_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			num_stages
												_lowerCAmelCase  						=			hidden_sizes
												_lowerCAmelCase  						=			depths
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			type_sequence_label_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			out_features
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			scope
												_lowerCAmelCase  						=			num_stages




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

												_lowerCAmelCase  						=			None
												if self.use_labels:
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.type_sequence_label_size				)

												_lowerCAmelCase  						=			self.get_config()

												return config, pixel_values, labels




						def  _lowercase     ( self				):

												"""simple docstring"""



												return ConvNextConfig(
												    num_channels=self.num_channels   ,       num_stages=self.num_stages   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       is_training=self.is_training   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       out_features=self.out_features   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												return UperNetConfig(
												    backbone_config=self.get_backbone_config()   ,       hidden_size=512   ,       pool_scales=[1, 2, 3, 6]   ,       use_auxiliary_head=_lowercase   ,       auxiliary_loss_weight=0.4   ,       auxiliary_in_channels=40   ,       auxiliary_channels=256   ,       auxiliary_num_convs=1   ,       auxiliary_concat_input=_lowercase   ,       loss_ignore_index=255   ,       num_labels=self.num_labels   ,       )




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

												"""simple docstring"""



												_lowerCAmelCase  						=			UperNetForSemanticSegmentation(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase				)
												self.parent.assertEqual(
												    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												(
												    (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   
												)  						=			config_and_inputs
												_lowerCAmelCase  						=			{"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       (UperNetForSemanticSegmentation,) if is_torch_available() else ()
						_lowercase		:				Dict									=       {'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
						_lowercase		:				Dict									=       False
						_lowercase		:				Optional[Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Union[str, Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				List[Any]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UperNetModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       has_text_modality=_lowercase   ,       hidden_size=37				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




						def  _lowercase     ( self				):

												"""simple docstring"""



												return




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		_lowerCAmelCase  						=			inspect.signature(model.forward				)
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		_lowerCAmelCase  						=			[*signature.parameters.keys()]

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase				)




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

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not support input and output embeddings"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not have a base model"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not have a base model"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		model.to(_lowercase				)
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**self._prepare_for_class(_lowercase   ,       _lowercase				)				)

																		_lowerCAmelCase  						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		_lowerCAmelCase  						=			self.model_tester.num_stages
																		self.assertEqual(len(_lowercase				)   ,       expected_num_stages + 1				)

																		# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]				)   ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,       )

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			True
																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_lowerCAmelCase  						=			True

																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												_lowerCAmelCase  						=			_config_zero_init(_lowercase				)
												_lowerCAmelCase  						=			_config_zero_init(configs_no_init.backbone_config				)
												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(config=_lowercase				)
																		for name, param in model.named_parameters():
																								if param.requires_grad:
																														self.assertIn(
																														    ((param.data.mean() * 1e9).round() / 1e9).item()   ,       [0.0, 1.0]   ,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,       )




						@unittest.skip(reason="""UperNet does not have tied weights"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained(_lowercase				)
																		self.assertIsNotNone(_lowercase				)






def A				():
						_lowerCAmelCase  						=			hf_hub_download(
						    repo_id="""hf-internal-testing/fixtures_ade20k"""			,     repo_type="""dataset"""			,     filename="""ADE_val_00000001.jpg"""		)
						_lowerCAmelCase  						=			Image.open(__lowerCamelCase		).convert("""RGB"""		)
						return image





@require_torch
@require_vision
@slow
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

												_lowerCAmelCase  						=			torch.tensor(
												    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]				).to(_lowercase				)
												self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       _lowercase   ,       atol=1e-4				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

												_lowerCAmelCase  						=			torch.tensor(
												    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]				).to(_lowercase				)
												self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       _lowercase   ,       atol=1e-4				)				)
