

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				:






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






    def 	lowercase__ (	self			:    Optional[Any]    )     ->   Dict:
        lowerCAmelCase				:			Dict    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowerCAmelCase				:			List[str]    			=      None
        if self.use_labels:
            lowerCAmelCase				:			Optional[Any]    			=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size    )

        lowerCAmelCase				:			Dict    			=      self.get_config()

        return config, pixel_values, labels






    def 	lowercase__ (	self			:    Optional[int]    )     ->   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=UpperCAmelCase_    ,				initializer_range=self.initializer_range    ,				)






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






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






@require_tf
class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
    lowerCAmelCase_							:		List[str]						=							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
    lowerCAmelCase_							:		Optional[Any]						=							(
        {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCAmelCase_							:		Optional[int]						=							False
    lowerCAmelCase_							:		List[Any]						=							False
    lowerCAmelCase_							:		Any						=							False
    lowerCAmelCase_							:		str						=							False
    lowerCAmelCase_							:		str						=							False






    def 	lowercase__ (	self			:    Dict    )     ->   str:
        lowerCAmelCase				:			str    			=      TFConvNextModelTester(self    )
        lowerCAmelCase				:			List[Any]    			=      ConfigTester(
            self    ,				config_class=UpperCAmelCase_    ,				has_text_modality=UpperCAmelCase_    ,				hidden_size=37    ,				)






    @unittest.skip(reason='ConvNext does not use inputs_embeds'    )
    def 	lowercase__ (	self			:    List[str]    )     ->   List[Any]:
        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 	lowercase__ (	self			:    Optional[Any]    )     ->   str:
        super().test_keras_fit()






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






    def 	lowercase__ (	self			:    str    )     ->   int:
        lowerCAmelCase				:			List[str]    			=      self.model_tester.prepare_config_and_inputs_for_common()

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

            lowerCAmelCase				:			Dict    			=      ['pixel_values']
            self.assertListEqual(arg_names[:1]    ,				UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Any    )     ->   Tuple:
        lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase_    )






    @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 	lowercase__ (	self			:    List[str]    )     ->   Tuple:
        super().test_dataset_conversion()






    def 	lowercase__ (	self			:    int    )     ->   Any:
        def check_hidden_states_output(UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ):
            lowerCAmelCase				:			Optional[int]    			=      model_class(UpperCAmelCase_    )

            lowerCAmelCase				:			Dict    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
            lowerCAmelCase				:			List[Any]    			=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            lowerCAmelCase				:			List[str]    			=      self.model_tester.num_stages
            self.assertEqual(len(UpperCAmelCase_    )    ,				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				:			Optional[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            lowerCAmelCase				:			List[str]    			=      True
            check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )

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

            check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )






    def 	lowercase__ (	self			:    List[str]    )     ->   int:
        lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Union[str, Any]={}    ):
            lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    ,				return_dict=UpperCAmelCase_    ,				**UpperCAmelCase_    )
            lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				return_dict=UpperCAmelCase_    ,				**UpperCAmelCase_    ).to_tuple()

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

                recursive_check(UpperCAmelCase_    ,				UpperCAmelCase_    )

        for model_class in self.all_model_classes:
            lowerCAmelCase				:			Union[str, Any]    			=      model_class(UpperCAmelCase_    )

            lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
            lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
            check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
            lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
            check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )

            lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
            lowerCAmelCase				:			Optional[Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
            check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				{'output_hidden_states': True}    )

            lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
            lowerCAmelCase				:			int    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
            check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				{'output_hidden_states': True}    )






    def 	lowercase__ (	self			:    int    )     ->   Union[str, Any]:
        lowerCAmelCase				:			Any    			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






    @slow
    def 	lowercase__ (	self			:    Union[str, Any]    )     ->   Any:
        lowerCAmelCase				:			Dict    			=      TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'    )
        self.assertIsNotNone(UpperCAmelCase_    )



def 		SCREAMING_SNAKE_CASE__				(							)					-> Union[str, Any]:



    '''simple docstring'''






    lowerCAmelCase				:			Dict    			=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image






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






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






    @slow
    def 	lowercase__ (	self			:    int    )     ->   int:
        lowerCAmelCase				:			Tuple    			=      TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    )

        lowerCAmelCase				:			Optional[Any]    			=      self.default_image_processor
        lowerCAmelCase				:			str    			=      prepare_img()
        lowerCAmelCase				:			Dict    			=      image_processor(images=UpperCAmelCase_    ,				return_tensors='tf'    )

        # forward pass
        lowerCAmelCase				:			List[Any]    			=      model(**UpperCAmelCase_    )

        # verify the logits
        lowerCAmelCase				:			List[Any]    			=      tf.TensorShape((1, 1000)    )
        self.assertEqual(outputs.logits.shape    ,				UpperCAmelCase_    )

        lowerCAmelCase				:			Any    			=      tf.constant([-0.02_60, -0.47_39, 0.19_11]    )

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__A   :     Dict      =			{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     List[Any]      =			['''BarthezTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     List[str]      =			['''BarthezTokenizerFast''']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_barthez import BarthezTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_barthez_fast import BarthezTokenizerFast

else:
 import sys

 __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





