



"""simple docstring"""


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
				from PIL import Image

				from transformers import ConvNextImageProcessor







class  A_     :



						"""simple docstring"""






						def __init__(   self					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Tuple=13  ,    lowercase_					:Any=32  ,    lowercase_					:Dict=3  ,    lowercase_					:Union[str, Any]=4  ,    lowercase_					:Union[str, Any]=[10, 20, 30, 40]  ,    lowercase_					:Dict=[2, 2, 3, 2]  ,    lowercase_					:int=True  ,    lowercase_					:List[str]=True  ,    lowercase_					:Optional[int]=37  ,    lowercase_					:Tuple="gelu"  ,    lowercase_					:int=10  ,    lowercase_					:Union[str, Any]=0.02  ,    lowercase_					:Dict=3  ,    lowercase_					:Tuple=None  ,    )							->     int:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					num_stages
												UpperCAmelCase			 =					hidden_sizes
												UpperCAmelCase			 =					depths
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					type_sequence_label_size
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					scope






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												UpperCAmelCase			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												UpperCAmelCase			 =					self.get_config()

												return config, pixel_values, labels






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     str:
												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=lowercase_  ,    initializer_range=self.initializer_range  ,    )






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Dict  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[Any]   )							->     Any:
												UpperCAmelCase			 =					TFConvNextModel(config=lowercase_   )
												UpperCAmelCase			 =					model(lowercase_  ,    training=lowercase_   )
												# 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       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Optional[Any]   )							->     Optional[int]:
												UpperCAmelCase			 =					self.type_sequence_label_size
												UpperCAmelCase			 =					TFConvNextForImageClassification(lowercase_   )
												UpperCAmelCase			 =					model(lowercase_  ,    labels=lowercase_  ,    training=lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)   )






						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					config_and_inputs
												UpperCAmelCase			 =					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_tf
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
						__UpperCamelCase													=				(
						    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
						    if is_tf_available()
						    else {}
						)

						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     str:
												UpperCAmelCase			 =					TFConvNextModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(
												    self  ,    config_class=lowercase_  ,    has_text_modality=lowercase_  ,    hidden_size=37  ,    )






						@unittest.skip(reason='ConvNext does not use inputs_embeds'   )
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[int]:
												pass






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('GPU'   )   ) == 0  ,    reason='TF does not support backprop for grouped convolutions on CPU.'  ,    )
						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[int]:
												super().test_keras_fit()






						@unittest.skip(reason='ConvNext does not support input and output embeddings'   )
						def       UpperCAmelCase__	(   self					:List[str]   )							->     List[Any]:
												pass






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase			 =					['pixel_values']
																		self.assertListEqual(arg_names[:1]  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_   )






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('GPU'   )   ) == 0  ,    reason='TF does not support backprop for grouped convolutions on CPU.'  ,    )
						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												super().test_dataset_conversion()






						def       UpperCAmelCase__	(   self					:Dict   )							->     Any:
												def check_hidden_states_output(lowercase_					:Dict  ,    lowercase_					:str  ,    lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					model_class(lowercase_   )

																		UpperCAmelCase			 =					model(**self._prepare_for_class(lowercase_  ,    lowercase_   )   )
																		UpperCAmelCase			 =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		UpperCAmelCase			 =					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]  ,    )

												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase			 =					True
																		check_hidden_states_output(lowercase_  ,    lowercase_  ,    lowercase_   )

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

																		check_hidden_states_output(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Tuple={}   ):
																		UpperCAmelCase			 =					model(lowercase_  ,    return_dict=lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					model(lowercase_  ,    return_dict=lowercase_  ,    **lowercase_   ).to_tuple()

																		def recursive_check(lowercase_					:Optional[int]  ,    lowercase_					:Union[str, Any]   ):
																								if isinstance(lowercase_  ,    (List, Tuple)   ):
																														for tuple_iterable_value, dict_iterable_value in zip(lowercase_  ,    lowercase_   ):
																																				recursive_check(lowercase_  ,    lowercase_   )
																								elif tuple_object is None:
																														return
																								else:
																														self.assertTrue(
																														    all(tf.equal(lowercase_  ,    lowercase_   )   )  ,    msg=(
																														        'Tuple and dict output are not equal. Difference:'
																														        f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}"""
																														    )  ,    )

																								recursive_check(lowercase_  ,    lowercase_   )

												for model_class in self.all_model_classes:
																		UpperCAmelCase			 =					model_class(lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_  ,    {'output_hidden_states': True}   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_  ,    {'output_hidden_states': True}   )






						def       UpperCAmelCase__	(   self					:Tuple   )							->     List[str]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowercase_   )






						@slow
						def       UpperCAmelCase__	(   self					:Tuple   )							->     Union[str, Any]:
												UpperCAmelCase			 =					TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'   )
												self.assertIsNotNone(lowercase_   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
						return image







@require_tf
@require_vision
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@cached_property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Dict:
												return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'   ) if is_vision_available() else None






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'   )

												UpperCAmelCase			 =					self.default_image_processor
												UpperCAmelCase			 =					prepare_img()
												UpperCAmelCase			 =					image_processor(images=lowercase_  ,    return_tensors='tf'   )

												# forward pass
												UpperCAmelCase			 =					model(**lowercase_   )

												# verify the logits
												UpperCAmelCase			 =					tf.TensorShape((1, 10_00)   )
												self.assertEqual(outputs.logits.shape  ,    lowercase_   )

												UpperCAmelCase			 =					tf.constant([-0.0260, -0.4739, 0.1911]   )

												tf.debugging.assert_near(outputs.logits[0, :3]  ,    lowercase_  ,    atol=1E-4   )










"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

snake_case_								=			{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

snake_case_								=			{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask""", """token_type_ids"""]
						__UpperCamelCase													=				DebertaTokenizer






						def __init__(   self					:Optional[Any]  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[Any]=None  ,    lowercase_					:Optional[Any]="replace"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Union[str, Any]="[SEP]"  ,    lowercase_					:int="[SEP]"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Optional[int]="[UNK]"  ,    lowercase_					:int="[PAD]"  ,    lowercase_					:List[Any]="[MASK]"  ,    lowercase_					:Union[str, Any]=False  ,    **lowercase_					:int  ,    )							->     Tuple:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    errors=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    unk_token=lowercase_  ,    sep_token=lowercase_  ,    cls_token=lowercase_  ,    pad_token=lowercase_  ,    mask_token=lowercase_  ,    add_prefix_space=lowercase_  ,    **lowercase_  ,    )
												UpperCAmelCase			 =					kwargs.pop('add_bos_token'  ,    lowercase_   )

												UpperCAmelCase			 =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'  ,    lowercase_   ) != add_prefix_space:
																		UpperCAmelCase			 =					getattr(lowercase_  ,    pre_tok_state.pop('type'   )   )
																		UpperCAmelCase			 =					add_prefix_space
																		UpperCAmelCase			 =					pre_tok_class(**lowercase_   )

												UpperCAmelCase			 =					add_prefix_space






						@property
						def       UpperCAmelCase__	(   self					:str   )							->     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       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Any   )							->     Any:
												UpperCAmelCase			 =					AddedToken(lowercase_  ,    lstrip=lowercase_  ,    rstrip=lowercase_   ) if isinstance(lowercase_  ,    lowercase_   ) else value
												UpperCAmelCase			 =					value






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]
												UpperCAmelCase			 =					[self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[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   ) * [0] + len(token_ids_a + sep   ) * [1]






						def       UpperCAmelCase__	(   self					:int  ,    *lowercase_					:str  ,    **lowercase_					:Optional[Any]   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:Any   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:"Conversation"   )							->     List[int]:
												UpperCAmelCase			 =					[]
												for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(lowercase_  ,    add_special_tokens=lowercase_   ) + [self.eos_token_id]   )

												if len(lowercase_   ) > self.model_max_length:
																		UpperCAmelCase			 =					input_ids[-self.model_max_length :]
												return input_ids





