



from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
      import tensorflow as tf

      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				str					)->int:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.config_class(**self.inputs_dict					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"hidden_sizes"					)					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"neck_hidden_sizes"					)					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"num_attention_heads"					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Optional[Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str]=1_3  ,	UpperCamelCase			:				Tuple=3_2  ,	UpperCamelCase			:				Any=2  ,	UpperCamelCase			:				Any=3  ,	UpperCamelCase			:				Optional[Any]=6_4_0  ,	UpperCamelCase			:				Tuple=4  ,	UpperCamelCase			:				Optional[int]="silu"  ,	UpperCamelCase			:				int=3  ,	UpperCamelCase			:				Optional[Any]=3_2  ,	UpperCamelCase			:				List[Any]=0.1  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Tuple=0.1  ,	UpperCamelCase			:				Dict=0.0_2  ,	UpperCamelCase			:				Any=True  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Any=1_0  ,	UpperCamelCase			:				str=None  ,	)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_size
      __SCREAMING_SNAKE_CASE							:     int							=						patch_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						last_hidden_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     str							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     Dict							=						conv_kernel_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						output_stride
      __SCREAMING_SNAKE_CASE							:     Dict							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Dict							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						classifier_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Any							=						use_labels
      __SCREAMING_SNAKE_CASE							:     Tuple							=						is_training
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						num_labels
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scope





     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      __SCREAMING_SNAKE_CASE							:     str							=						None
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						None
      if self.use_labels:
       __SCREAMING_SNAKE_CASE							:     Any							=						ids_tensor([self.batch_size]  ,	self.num_labels					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,	self.num_labels					)

      __SCREAMING_SNAKE_CASE							:     str							=						self.get_config()

      return config, pixel_values, labels, pixel_labels





     def 						__snake_case     (							self			:				Any					)->Any:
      return MobileViTConfig(
          image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	num_attention_heads=self.num_attention_heads  ,	hidden_act=self.hidden_act  ,	conv_kernel_size=self.conv_kernel_size  ,	output_stride=self.output_stride  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	classifier_dropout_prob=self.classifier_dropout_prob  ,	initializer_range=self.initializer_range  ,	)





     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Tuple					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						TFMobileViTModel(config=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						model(UpperCamelCase  ,	training=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,	(self.batch_size, self.last_hidden_size, expected_height, expected_width)					)





     def 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Any					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     str							=						TFMobileViTForImageClassification(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(UpperCamelCase  ,	labels=UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)					)





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				Union[str, Any]					)->Any:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     List[str]							=						TFMobileViTForSemanticSegmentation(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_size // self.output_stride

      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(
          result.logits.shape  ,	(self.batch_size, self.num_labels, expected_height, expected_width)					)

      __SCREAMING_SNAKE_CASE							:     int							=						model(UpperCamelCase  ,	labels=UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(
          result.logits.shape  ,	(self.batch_size, self.num_labels, expected_height, expected_width)					)






     def 						__snake_case     (							self			:				Dict					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						config_and_inputs
      __SCREAMING_SNAKE_CASE							:     int							=						{"pixel_values": pixel_values}
      return config, inputs_dict






@require_tf
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      (
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     lowerCAmelCase       =      (
         {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						TFMobileViTModelTester(self					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						TFMobileViTConfigTester(self  ,	config_class=UpperCamelCase  ,	has_text_modality=UpperCamelCase					)





     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      self.config_tester.run_common_tests()





     @unittest.skip(reason="MobileViT does not use inputs_embeds"					)
     def 						__snake_case     (							self			:				Any					)->Dict:
      pass





     @unittest.skip(reason="MobileViT does not support input and output embeddings"					)
     def 						__snake_case     (							self			:				Dict					)->str:
      pass





     @unittest.skip(reason="MobileViT does not output attentions"					)
     def 						__snake_case     (							self			:				Dict					)->str:
      pass





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						["pixel_values"]
       self.assertListEqual(arg_names[:1]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				Dict					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Union[str, Any]:
      def check_hidden_states_output(UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				str					):
       __SCREAMING_SNAKE_CASE							:     int							=						model_class(UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     int							=						model(**self._prepare_for_class(UpperCamelCase  ,	UpperCamelCase					)					)

       __SCREAMING_SNAKE_CASE							:     Tuple							=						outputs.hidden_states

       __SCREAMING_SNAKE_CASE							:     List[Any]							=						5
       self.assertEqual(len(UpperCamelCase					)  ,	UpperCamelCase					)

       # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
       # with the width and height being successively divided by 2.
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						2
       for i in range(len(UpperCamelCase					)					):
        self.assertListEqual(
            list(hidden_states[i].shape[-2:]					)  ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,	)
        divisor *= 2

       self.assertEqual(self.model_tester.output_stride  ,	divisor // 2					)

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

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     int							=						True
       check_hidden_states_output(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase					)

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

       check_hidden_states_output(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				Any					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[int]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*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 						__snake_case     (							self			:				str					)->List[str]:
      super().test_dataset_conversion()





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				int=2E-1  ,	UpperCamelCase			:				Optional[Any]=2E-1					)->List[Any]:
      self.assertTrue(np.allclose(UpperCamelCase  ,	UpperCamelCase  ,	atol=UpperCamelCase  ,	rtol=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."  ,	)
     @slow
     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
       if model_class.__name__ != "TFMobileViTModel":
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						model_class(UpperCamelCase					)
        if getattr(UpperCamelCase  ,	"hf_compute_loss"  ,	UpperCamelCase					):
         super().test_keras_fit()





     def 						__snake_case     (							self			:				str					)->int:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       # set an ignore index to correctly test the masked loss used in
       # `TFMobileViTForSemanticSegmentation`.
       if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
        __SCREAMING_SNAKE_CASE							:     List[str]							=						5

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model_class(UpperCamelCase					)
       if getattr(UpperCamelCase  ,	"hf_compute_loss"  ,	UpperCamelCase					):
        # The number of elements in the loss should be the same as the number of elements in the label
        __SCREAMING_SNAKE_CASE							:     Tuple							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()  ,	reverse=UpperCamelCase					)[0]
        ]
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        __SCREAMING_SNAKE_CASE							:     Dict							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{"input_ids", "pixel_values", "input_features"}
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						possible_input_names.intersection(set(UpperCamelCase					)					).pop()
        __SCREAMING_SNAKE_CASE							:     List[str]							=						prepared_for_class.pop(UpperCamelCase					)

        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model(UpperCamelCase  ,	**UpperCamelCase					)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

        # Test that model correctly compute the loss when we mask some positions
        __SCREAMING_SNAKE_CASE							:     Tuple							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Any							=						{"input_ids", "pixel_values", "input_features"}
        __SCREAMING_SNAKE_CASE							:     Dict							=						possible_input_names.intersection(set(UpperCamelCase					)					).pop()
        __SCREAMING_SNAKE_CASE							:     Dict							=						prepared_for_class.pop(UpperCamelCase					)
        if "labels" in prepared_for_class:
         __SCREAMING_SNAKE_CASE							:     List[str]							=						prepared_for_class["labels"].numpy()
         if len(labels.shape					) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          __SCREAMING_SNAKE_CASE							:     Tuple							=						tf.convert_to_tensor(UpperCamelCase					)
          __SCREAMING_SNAKE_CASE							:     Tuple							=						model(UpperCamelCase  ,	**UpperCamelCase					)[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
          self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Tuple							=						model(UpperCamelCase					)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

        # Test that model correctly compute the loss with a tuple
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)

        # Get keys that were added with the _prepare_for_class function
        __SCREAMING_SNAKE_CASE							:     int							=						prepared_for_class.keys() - inputs_dict.keys()
        __SCREAMING_SNAKE_CASE							:     int							=						inspect.signature(model.call					).parameters
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						list(signature.keys()					)

        # Create a dictionary holding the location of the tensors in the tuple
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						{0: input_name}
        for label_key in label_keys:
         __SCREAMING_SNAKE_CASE							:     List[Any]							=						signature_names.index(UpperCamelCase					)
         __SCREAMING_SNAKE_CASE							:     str							=						label_key
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sorted(tuple_index_mapping.items()					)
        # Initialize a list with their default values, update the values and convert to a tuple
        __SCREAMING_SNAKE_CASE							:     Any							=						[]

        for name in signature_names:
         if name != "kwargs":
          list_input.append(signature[name].default					)

        for index, value in sorted_tuple_index_mapping:
         __SCREAMING_SNAKE_CASE							:     Any							=						prepared_for_class[value]

        __SCREAMING_SNAKE_CASE							:     List[Any]							=						tuple(UpperCamelCase					)

        # Send to model
        __SCREAMING_SNAKE_CASE							:     List[str]							=						model(tuple_input[:-1]					)[0]

        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)






     @slow
     def 						__snake_case     (							self			:				str					)->List[Any]:
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						TFMobileViTModel.from_pretrained(UpperCamelCase					)
       self.assertIsNotNone(UpperCamelCase					)






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
 return image






@require_tf
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     @slow
     def 						__snake_case     (							self			:				Union[str, Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						prepare_img()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_processor(images=UpperCamelCase  ,	return_tensors="tf"					)

      # forward pass
      __SCREAMING_SNAKE_CASE							:     str							=						model(**UpperCamelCase  ,	training=UpperCamelCase					)

      # verify the logits
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tf.TensorShape((1, 1_0_0_0)					)
      self.assertEqual(outputs.logits.shape  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     int							=						tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]					)

      tf.debugging.assert_near(outputs.logits[0, :3]  ,	UpperCamelCase  ,	atol=1E-4  ,	rtol=1E-04					)






     @slow
     def 						__snake_case     (							self			:				Dict					)->Optional[Any]:
      # `from_pt` will be removed
      __SCREAMING_SNAKE_CASE							:     Dict							=						TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_img()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(images=UpperCamelCase  ,	return_tensors="tf"					)

      # forward pass
      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(inputs.pixel_values  ,	training=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

      # verify the logits
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						tf.TensorShape((1, 2_1, 3_2, 3_2)					)
      self.assertEqual(logits.shape  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						tf.constant(
          [
              [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
              [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
              [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
          ]					)

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














'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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