'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_A								=   logging.get_logger(__name__)


_A								=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_A								=   {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A								=   {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Optional[int]     =    VOCAB_FILES_NAMES
   A							:       Optional[Any]     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       Optional[int]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       List[str]     =    ['''input_ids''', '''attention_mask''']
   A							:       int     =    BartTokenizer
   def __init__(self       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a="replace"       ,				_a="<s>"       ,				_a="</s>"       ,				_a="</s>"       ,				_a="<s>"       ,				_a="<unk>"       ,				_a="<pad>"       ,				_a="<mask>"       ,				_a=False       ,				_a=True       ,				**_a       ,				)    ->     Any:
        super().__init__(
            _a       ,				_a       ,				tokenizer_file=_a       ,				errors=_a       ,				bos_token=_a       ,				eos_token=_a       ,				sep_token=_a       ,				cls_token=_a       ,				unk_token=_a       ,				pad_token=_a       ,				mask_token=_a       ,				add_prefix_space=_a       ,				trim_offsets=_a       ,				**_a       ,				)

        lowercase_					:       List[Any]     				=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
        if pre_tok_state.get('add_prefix_space'       ,				_a				) != add_prefix_space:
             lowercase_					:       Dict     				=					getattr(_a       ,				pre_tok_state.pop('type'				)				)
             lowercase_					:       str     				=					add_prefix_space
             lowercase_					:       int     				=					pre_tok_class(**_a				)

        lowercase_					:       Dict     				=					add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_					:       Optional[int]     				=					'post_processor'
        lowercase_					:       Dict     				=					getattr(self.backend_tokenizer       ,				_a       ,				_a				)
        if tokenizer_component_instance:
             lowercase_					:       Dict     				=					json.loads(tokenizer_component_instance.__getstate__()				)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                  lowercase_					:       List[str]     				=					tuple(state['sep']				)
             if "cls" in state:
                  lowercase_					:       Any     				=					tuple(state['cls']				)

             lowercase_					:       Dict     				=					False

             if state.get('add_prefix_space'       ,				_a				) != add_prefix_space:
                  lowercase_					:       Union[str, Any]     				=					add_prefix_space
                  lowercase_					:       Optional[Any]     				=					True

             if state.get('trim_offsets'       ,				_a				) != trim_offsets:
                  lowercase_					:       Dict     				=					trim_offsets
                  lowercase_					:       Dict     				=					True

             if changes_to_apply:
                  lowercase_					:       List[str]     				=					getattr(_a       ,				state.pop('type'				)				)
                  lowercase_					:       Dict     				=					component_class(**_a				)
                  setattr(self.backend_tokenizer       ,				_a       ,				_a				)
   @property
   def 		_lowerCamelCase (self				)    ->     str:
        if self._mask_token is None:
             if self.verbose:
                  logger.error('Using mask_token, but it is not set yet.'				)
             return None
        return str(self._mask_token				)
   @mask_token.setter
   def 		_lowerCamelCase (self       ,				_a				)    ->     Dict:
        lowercase_					:       Any     				=					AddedToken(_a       ,				lstrip=_a       ,				rstrip=_a				) if isinstance(_a       ,				_a				) else value
        lowercase_					:       List[str]     				=					value
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     BatchEncoding:
        lowercase_					:       Tuple     				=					kwargs.get('is_split_into_words'       ,				_a				)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                 'to use it with pretokenized inputs.'				)

        return super()._batch_encode_plus(*_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     BatchEncoding:
        lowercase_					:       List[Any]     				=					kwargs.get('is_split_into_words'       ,				_a				)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                 'to use it with pretokenized inputs.'				)

        return super()._encode_plus(*_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        lowercase_					:       Tuple     				=					self._tokenizer.model.save(_a       ,				name=_a				)
        return tuple(_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     str:
        lowercase_					:       Any     				=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        lowercase_					:       List[str]     				=					[self.sep_token_id]
        lowercase_					:       Optional[int]     				=					[self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep				) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]





'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
 from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class       UpperCAmelCase__  :






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

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

        lowercase_					:       str     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return ConvNextVaConfig(
            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=_a       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Optional[int]     				=					ConvNextVaModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       List[Any]     				=					model(_a				)
        # 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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Any:
        lowercase_					:       int     				=					ConvNextVaForImageClassification(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Dict     				=					model(_a       ,				labels=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # 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
        lowercase_					:       int     				=					None
        lowercase_					:       List[str]     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # 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 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       str     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'pixel_values': pixel_values}
        return config, inputs_dict





   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					config_and_inputs
        lowercase_					:       Dict     				=					{'pixel_values': pixel_values, 'labels': labels}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    (
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A							:       Optional[int]     =    (
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A							:       Optional[int]     =    False
   A							:       str     =    False
   A							:       int     =    False
   A							:       Dict     =    False
   A							:       Optional[int]     =    False
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					ConvNextVaModelTester(self				)
        lowercase_					:       List[str]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     int:
        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 		_lowerCamelCase (self				)    ->     List[Any]:
        return
   @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        pass
   @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Dict     				=					True

             if model_class.__name__ in [
                 *get_values(_a				),
                 *get_values(_a				),
             ]:
                  continue

             lowercase_					:       str     				=					model_class(_a				)
             model.to(_a				)
             model.train()
             lowercase_					:       Tuple     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       List[Any]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Optional[int]     				=					False
             lowercase_					:       Union[str, Any]     				=					True

             if (
                 model_class.__name__
                 in [*get_values(_a				), *get_values(_a				)]
                 or not model_class.supports_gradient_checkpointing
             ):
                  continue

             lowercase_					:       Union[str, Any]     				=					model_class(_a				)
             model.to(_a				)
             model.gradient_checkpointing_enable()
             model.train()
             lowercase_					:       List[Any]     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       Optional[int]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_					:       List[str]     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       Tuple     				=					model_class(_a				)
             model.to(_a				)
             model.eval()

             with torch.no_grad():
                  lowercase_					:       str     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

             lowercase_					:       str     				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_					:       Optional[int]     				=					self.model_tester.num_stages
             self.assertEqual(len(_a				)       ,				expected_num_stages + 1				)

             # ConvNextV2'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]       ,				)

        lowercase_				,lowercase_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Optional[Any]     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

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

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       Optional[int]     				=					ConvNextVaModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






@require_torch
@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     str:
        return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'				) if is_vision_available() else None





   @slow
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[Any]     				=					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'				).to(_a				)

        lowercase_					:       Any     				=					self.default_image_processor
        lowercase_					:       Union[str, Any]     				=					prepare_img()
        lowercase_					:       List[str]     				=					preprocessor(images=_a       ,				return_tensors='pt'				).to(_a				)

        # forward pass
        with torch.no_grad():
             lowercase_					:       List[Any]     				=					model(**_a				)

        # verify the logits
        lowercase_					:       str     				=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       Tuple     				=					torch.tensor([0.99_96, 0.19_66, -0.43_86]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)




