



"""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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

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


lowerCAmelCase_   						=   {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase_   						=   {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase_   						=   {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class        __A       (	a__    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Tuple   	=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					int   	=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					Optional[int]   	=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class        __A       (	a__    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Dict   	=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					Optional[Any]   	=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					Any   	=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

lowerCAmelCase_   						=   collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


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







		'''simple docstring'''





		def __call__(	self						:  str			,_snake_case						:  str			,_snake_case						:  Tuple = None			,_snake_case						:  Optional[int] = None			,_snake_case						:  Any = False			,_snake_case						:  Dict = False			,_snake_case						:  List[Any] = None			,_snake_case						:  List[Any] = None			,_snake_case						:  Tuple = None			,**_snake_case						:  Optional[int]			,)					->		BatchEncoding:






				"""simple docstring"""



				if titles is None and texts is None:
						return super().__call__(
						    SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,truncation=SCREAMING_SNAKE_CASE_			,max_length=SCREAMING_SNAKE_CASE_			,return_tensors=SCREAMING_SNAKE_CASE_			,return_attention_mask=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_			,)
				elif titles is None or texts is None:
						lowercase__   :    Optional[int]				=			titles if texts is None else texts
						return super().__call__(
						    SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,truncation=SCREAMING_SNAKE_CASE_			,max_length=SCREAMING_SNAKE_CASE_			,return_tensors=SCREAMING_SNAKE_CASE_			,return_attention_mask=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_			,)
				lowercase__   :    List[Any]				=			titles if not isinstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_						) else [titles]
				lowercase__   :    int				=			texts if not isinstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_						) else [texts]
				lowercase__   :    Optional[Any]				=			len(SCREAMING_SNAKE_CASE_						)
				lowercase__   :    int				=			questions if not isinstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_						) else [questions] * n_passages
				if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
						raise ValueError(
						    f"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_						)} titles and {len(SCREAMING_SNAKE_CASE_						)} texts."""						)
				lowercase__   :    int				=			super().__call__(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,truncation=SCREAMING_SNAKE_CASE_						)['input_ids']
				lowercase__   :    Tuple				=			super().__call__(SCREAMING_SNAKE_CASE_			,add_special_tokens=SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,truncation=SCREAMING_SNAKE_CASE_						)['input_ids']
				lowercase__   :    str				=			{
				    'input_ids': [
				        (encoded_question_and_title + encoded_text)[:max_length]
				        if max_length is not None and truncation
				        else encoded_question_and_title + encoded_text
				        for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_						)
				    ]
				}
				if return_attention_mask is not False:
						lowercase__   :    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]						)
						lowercase__   :    str				=			attention_mask
				return self.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,max_length=SCREAMING_SNAKE_CASE_			,return_tensors=SCREAMING_SNAKE_CASE_						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  Optional[int]			,_snake_case						:  Any = 16			,_snake_case						:  Tuple = 64			,_snake_case						:  Optional[Any] = 4			,)					->		List[DPRSpanPrediction]:






				"""simple docstring"""



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

						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=SCREAMING_SNAKE_CASE_			,top_spans=SCREAMING_SNAKE_CASE_			,)
						for start_index, end_index in best_spans:
								start_index += passage_offset
								end_index += passage_offset
								nbest_spans_predictions.append(
								    DPRSpanPrediction(
								        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]			,relevance_score=relevance_logits[doc_id]			,doc_id=SCREAMING_SNAKE_CASE_			,start_index=SCREAMING_SNAKE_CASE_			,end_index=SCREAMING_SNAKE_CASE_			,text=self.decode(sequence_ids[start_index : end_index + 1]						)			,)						)
						if len(SCREAMING_SNAKE_CASE_						) >= num_spans:
								break
				return nbest_spans_predictions[:num_spans]







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Any]			,_snake_case						:  Tuple			,_snake_case						:  str			,_snake_case						:  str			,)					->		List[DPRSpanPrediction]:






				"""simple docstring"""



				lowercase__   :    int				=			[]
				for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_						):
						for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
								scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
				lowercase__   :    str				=			sorted(SCREAMING_SNAKE_CASE_			,key=lambda _snake_case						: x[1]			,reverse=SCREAMING_SNAKE_CASE_						)
				lowercase__   :    int				=			[]
				for (start_index, end_index), score in scores:
						if start_index > end_index:
								raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""						)
						lowercase__   :    str				=			end_index - start_index + 1
						if length > max_answer_length:
								raise ValueError(f"""Span is too long: {length} > {max_answer_length}"""						)
						if any(
						    start_index <= prev_start_index <= prev_end_index <= end_index
						    or prev_start_index <= start_index <= end_index <= prev_end_index
						    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
								continue
						chosen_span_intervals.append((start_index, end_index)						)

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





@add_end_docstrings(a__    )
class        __A       (	a__     ,a__    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Tuple   	=				READER_PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					Tuple   	=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					Optional[Any]   	=				READER_PRETRAINED_INIT_CONFIGURATION
		lowerCAmelCase			:					Optional[int]   	=				["input_ids", "attention_mask"]





import inspect
import unittest

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

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


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=[10, 20, 30, 40],			SCREAMING_SNAKE_CASE_=[2, 2, 3, 2],			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=10,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"],			SCREAMING_SNAKE_CASE_=[2, 3, 4],			SCREAMING_SNAKE_CASE_=None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	parent
          UpperCamelCase    :  Optional[Any]							=	batch_size
          UpperCamelCase    :  Union[str, Any]							=	image_size
          UpperCamelCase    :  Union[str, Any]							=	num_channels
          UpperCamelCase    :  List[Any]							=	num_stages
          UpperCamelCase    :  Any							=	hidden_sizes
          UpperCamelCase    :  Optional[int]							=	depths
          UpperCamelCase    :  Optional[int]							=	is_training
          UpperCamelCase    :  List[str]							=	use_labels
          UpperCamelCase    :  Dict							=	intermediate_size
          UpperCamelCase    :  List[Any]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	num_labels
          UpperCamelCase    :  str							=	initializer_range
          UpperCamelCase    :  List[str]							=	out_features
          UpperCamelCase    :  List[str]							=	out_indices
          UpperCamelCase    :  str							=	scope
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  int							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          UpperCamelCase    :  Any							=	None
          if self.use_labels:
             UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size],			self.num_labels       )

          UpperCamelCase    :  List[Any]							=	self.get_config()
          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		Any:
          return ConvNextConfig(
              num_channels=self.num_channels,			hidden_sizes=self.hidden_sizes,			depths=self.depths,			num_stages=self.num_stages,			hidden_act=self.hidden_act,			is_decoder=SCREAMING_SNAKE_CASE_,			initializer_range=self.initializer_range,			out_features=self.out_features,			out_indices=self.out_indices,			num_labels=self.num_labels,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase    :  Dict							=	ConvNextModel(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  str							=	model(SCREAMING_SNAKE_CASE_       )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[int]							=	ConvNextForImageClassification(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Dict:
          UpperCamelCase    :  int							=	ConvNextBackbone(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Any							=	model(SCREAMING_SNAKE_CASE_       )

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps       ),			len(config.out_features       )       )
          self.parent.assertListEqual(list(result.feature_maps[0].shape       ),			[self.batch_size, self.hidden_sizes[1], 4, 4]       )

          # verify channels
          self.parent.assertEqual(len(model.channels       ),			len(config.out_features       )       )
          self.parent.assertListEqual(model.channels,			config.hidden_sizes[1:]       )

          # verify backbone works with out_features=None
          UpperCamelCase    :  Any							=	None
          UpperCamelCase    :  List[str]							=	ConvNextBackbone(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()
          UpperCamelCase    :  Tuple							=	model(SCREAMING_SNAKE_CASE_       )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps       ),			1       )
          self.parent.assertListEqual(list(result.feature_maps[0].shape       ),			[self.batch_size, self.hidden_sizes[-1], 1, 1]       )

          # verify channels
          self.parent.assertEqual(len(model.channels       ),			1       )
          self.parent.assertListEqual(model.channels,			[config.hidden_sizes[-1]]       )






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	self.prepare_config_and_inputs()
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	config_and_inputs
          UpperCamelCase    :  str							=	{'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					List[Any]        =  (
           (
               ConvNextModel,
               ConvNextForImageClassification,
               ConvNextBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__				:					Union[str, Any]        =  (
           {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase__				:					Dict        =  True
       UpperCAmelCase__				:					List[str]        =  False
       UpperCAmelCase__				:					Tuple        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       UpperCAmelCase__				:					Tuple        =  False
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Dict							=	ConvNextModelTester(self       )
          UpperCamelCase    :  int							=	ConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_,			has_text_modality=SCREAMING_SNAKE_CASE_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		List[str]:
          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 					snake_case_       (			self       )     ->		Union[str, Any]:
          return
       @unittest.skip(reason='ConvNext does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		List[str]:
          pass
       @unittest.skip(reason='ConvNext does not support input and output embeddings'       )
       def 					snake_case_       (			self       )     ->		str:
          pass
       @unittest.skip(reason='ConvNext does not use feedforward chunking'       )
       def 					snake_case_       (			self       )     ->		str:
          pass
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  int							=	inspect.signature(model.forward       )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase    :  List[Any]							=	[*signature.parameters.keys()]

             UpperCamelCase    :  Union[str, Any]							=	['pixel_values']
             self.assertListEqual(arg_names[:1],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[int]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  List[Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             model.to(SCREAMING_SNAKE_CASE_       )
             model.eval()

             with torch.no_grad():
                UpperCamelCase    :  List[Any]							=	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )

             UpperCamelCase    :  List[str]							=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase    :  int							=	self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			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],			)

          UpperCamelCase	,     UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[int]							=	True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase    :  str							=	True

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		Tuple:
          for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Union[str, Any]							=	ConvNextModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_torch
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'       ) if is_vision_available() else None






       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Dict							=	ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	self.default_image_processor
          UpperCamelCase    :  Any							=	prepare_img()
          UpperCamelCase    :  List[str]							=	image_processor(images=SCREAMING_SNAKE_CASE_,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )

          # forward pass
          with torch.no_grad():
             UpperCamelCase    :  Optional[Any]							=	model(**SCREAMING_SNAKE_CASE_       )

          # verify the logits
          UpperCamelCase    :  List[Any]							=	torch.Size((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	torch.tensor([-0.02_60, -0.47_39, 0.19_11]       ).to(SCREAMING_SNAKE_CASE_       )

          self.assertTrue(torch.allclose(outputs.logits[0, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-4       )       )






@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase       ,    a__							):
       UpperCAmelCase__				:					Tuple        =  (ConvNextBackbone,) if is_torch_available() else ()
       UpperCAmelCase__				:					List[str]        =  ConvNextConfig

       UpperCAmelCase__				:					Tuple        =  False
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	ConvNextModelTester(self       )



