

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 							A_				(	snake_case_      ):


     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Dict:
      __lowerCAmelCase:     Optional[int]     			=   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 							A_				:


     def __init__(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Any=1_3    ,						UpperCAmelCase    :	List[Any]=3_2    ,						UpperCAmelCase    :	Tuple=2    ,						UpperCAmelCase    :	List[Any]=3    ,						UpperCAmelCase    :	Optional[int]=6_4_0    ,						UpperCAmelCase    :	Optional[Any]=4    ,						UpperCAmelCase    :	Tuple="silu"    ,						UpperCAmelCase    :	List[str]=3    ,						UpperCAmelCase    :	List[str]=3_2    ,						UpperCAmelCase    :	List[Any]=0.1    ,						UpperCAmelCase    :	int=0.1    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	List[str]=0.02    ,						UpperCAmelCase    :	List[Any]=True    ,						UpperCAmelCase    :	Optional[Any]=True    ,						UpperCAmelCase    :	int=1_0    ,						UpperCAmelCase    :	int=None    ,						)							->			List[Any]:
      __lowerCAmelCase:     Optional[int]     			=   parent
      __lowerCAmelCase:     Optional[int]     			=   batch_size
      __lowerCAmelCase:     int     			=   image_size
      __lowerCAmelCase:     List[str]     			=   patch_size
      __lowerCAmelCase:     Any     			=   num_channels
      __lowerCAmelCase:     Dict     			=   last_hidden_size
      __lowerCAmelCase:     Union[str, Any]     			=   num_attention_heads
      __lowerCAmelCase:     Any     			=   hidden_act
      __lowerCAmelCase:     Optional[Any]     			=   conv_kernel_size
      __lowerCAmelCase:     Tuple     			=   output_stride
      __lowerCAmelCase:     int     			=   hidden_dropout_prob
      __lowerCAmelCase:     Any     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Union[str, Any]     			=   classifier_dropout_prob
      __lowerCAmelCase:     int     			=   use_labels
      __lowerCAmelCase:     List[Any]     			=   is_training
      __lowerCAmelCase:     int     			=   num_labels
      __lowerCAmelCase:     Optional[Any]     			=   initializer_range
      __lowerCAmelCase:     Dict     			=   scope


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

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

      __lowerCAmelCase:     List[str]     			=   self.get_config()

      return config, pixel_values, labels, pixel_labels


     def 						UpperCAmelCase						(							self    :	List[str]      )							->			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 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Union[str, Any]      )							->			Optional[Any]:
      __lowerCAmelCase:     List[str]     			=   TFMobileViTModel(config=UpperCAmelCase      )
      __lowerCAmelCase:     List[Any]     			=   model(UpperCAmelCase    ,						training=UpperCAmelCase      )
      __lowerCAmelCase:     int     			=   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 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	int      )							->			Dict:
      __lowerCAmelCase:     Optional[int]     			=   self.num_labels
      __lowerCAmelCase:     Optional[int]     			=   TFMobileViTForImageClassification(UpperCAmelCase      )
      __lowerCAmelCase:     int     			=   model(UpperCAmelCase    ,						labels=UpperCAmelCase    ,						training=UpperCAmelCase      )
      self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)      )


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			Optional[int]:
      __lowerCAmelCase:     List[str]     			=   self.num_labels
      __lowerCAmelCase:     Optional[int]     			=   TFMobileViTForSemanticSegmentation(UpperCAmelCase      )
      __lowerCAmelCase:     Any     			=   self.image_size // self.output_stride

      __lowerCAmelCase:     int     			=   model(UpperCAmelCase    ,						training=UpperCAmelCase      )
      self.parent.assertEqual(
          result.logits.shape    ,						(self.batch_size, self.num_labels, expected_height, expected_width)      )

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						labels=UpperCAmelCase    ,						training=UpperCAmelCase      )
      self.parent.assertEqual(
          result.logits.shape    ,						(self.batch_size, self.num_labels, expected_height, expected_width)      )


     def 						UpperCAmelCase						(							self    :	List[str]      )							->			List[str]:
      __lowerCAmelCase:     str     			=   self.prepare_config_and_inputs()
      __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     int     			=   config_and_inputs
      __lowerCAmelCase:     Dict     			=   {'pixel_values': pixel_values}
      return config, inputs_dict







@require_tf
class 							A_				(	snake_case_							,			snake_case_							,			unittest.TestCase      ):
     _lowercase    :  Any          =			(
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     _lowercase    :  Optional[Any]          =			(
         {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _lowercase    :  Optional[Any]          =			False
     _lowercase    :  Dict          =			False
     _lowercase    :  Tuple          =			False
     _lowercase    :  Dict          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	int      )							->			List[str]:
      __lowerCAmelCase:     Union[str, Any]     			=   TFMobileViTModelTester(self      )
      __lowerCAmelCase:     List[Any]     			=   TFMobileViTConfigTester(self    ,						config_class=UpperCAmelCase    ,						has_text_modality=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Union[str, Any]:
      self.config_tester.run_common_tests()


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


     @unittest.skip(reason='MobileViT does not support input and output embeddings'      )
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			int:
      pass


     @unittest.skip(reason='MobileViT does not output attentions'      )
     def 						UpperCAmelCase						(							self    :	str      )							->			List[Any]:
      pass


     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Union[str, Any]:
      __lowerCAmelCase			,							__lowerCAmelCase:     List[Any]     			=   self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase:     Optional[Any]     			=   ['pixel_values']
       self.assertListEqual(arg_names[:1]    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[Any]:
      __lowerCAmelCase:     Optional[Any]     			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Optional[Any]:
      def check_hidden_states_output(UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Tuple      ):
       __lowerCAmelCase:     Any     			=   model_class(UpperCAmelCase      )

       __lowerCAmelCase:     List[str]     			=   model(**self._prepare_for_class(UpperCAmelCase    ,						UpperCAmelCase      )      )

       __lowerCAmelCase:     str     			=   outputs.hidden_states

       __lowerCAmelCase:     Dict     			=   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.
       __lowerCAmelCase:     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      )

      __lowerCAmelCase			,							__lowerCAmelCase:     Union[str, Any]     			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:     Optional[Any]     			=   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[Any]     			=   True

       check_hidden_states_output(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Optional[int]:
      __lowerCAmelCase:     Optional[int]     			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			List[Any]:
      __lowerCAmelCase:     Optional[int]     			=   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 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Any:
      super().test_dataset_conversion()


     def 						UpperCAmelCase						(							self    :	Any    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Optional[Any]=2E-1    ,						UpperCAmelCase    :	Dict=2E-1      )							->			List[str]:
      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 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      __lowerCAmelCase			,							__lowerCAmelCase:     Any     			=   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":
        __lowerCAmelCase:     int     			=   model_class(UpperCAmelCase      )
        if getattr(UpperCAmelCase    ,						'hf_compute_loss'    ,						UpperCAmelCase      ):
         super().test_keras_fit()


     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[Any]:
      __lowerCAmelCase			,							__lowerCAmelCase:     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":
        __lowerCAmelCase:     Dict     			=   5

       __lowerCAmelCase:     List[Any]     			=   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
        __lowerCAmelCase:     Tuple     			=   self._prepare_for_class(inputs_dict.copy()    ,						UpperCAmelCase    ,						return_labels=UpperCAmelCase      )
        __lowerCAmelCase:     List[Any]     			=   prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()    ,						reverse=UpperCAmelCase      )[0]
        ]
        __lowerCAmelCase:     str     			=   added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        __lowerCAmelCase:     List[str]     			=   self._prepare_for_class(inputs_dict.copy()    ,						UpperCAmelCase    ,						return_labels=UpperCAmelCase      )
        __lowerCAmelCase:     Tuple     			=   {'input_ids', 'pixel_values', 'input_features'}
        __lowerCAmelCase:     Optional[int]     			=   possible_input_names.intersection(set(UpperCAmelCase      )      ).pop()
        __lowerCAmelCase:     int     			=   prepared_for_class.pop(UpperCAmelCase      )

        __lowerCAmelCase:     int     			=   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
        __lowerCAmelCase:     Optional[Any]     			=   self._prepare_for_class(inputs_dict.copy()    ,						UpperCAmelCase    ,						return_labels=UpperCAmelCase      )
        __lowerCAmelCase:     List[Any]     			=   {'input_ids', 'pixel_values', 'input_features'}
        __lowerCAmelCase:     Optional[Any]     			=   possible_input_names.intersection(set(UpperCAmelCase      )      ).pop()
        __lowerCAmelCase:     Optional[Any]     			=   prepared_for_class.pop(UpperCAmelCase      )
        if "labels" in prepared_for_class:
         __lowerCAmelCase:     List[str]     			=   prepared_for_class['labels'].numpy()
         if len(labels.shape      ) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          __lowerCAmelCase:     Union[str, Any]     			=   tf.convert_to_tensor(UpperCAmelCase      )
          __lowerCAmelCase:     Any     			=   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
        __lowerCAmelCase:     List[str]     			=   self._prepare_for_class(inputs_dict.copy()    ,						UpperCAmelCase    ,						return_labels=UpperCAmelCase      )
        __lowerCAmelCase:     Optional[Any]     			=   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
        __lowerCAmelCase:     int     			=   self._prepare_for_class(inputs_dict.copy()    ,						UpperCAmelCase    ,						return_labels=UpperCAmelCase      )

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

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

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

        for index, value in sorted_tuple_index_mapping:
         __lowerCAmelCase:     str     			=   prepared_for_class[value]

        __lowerCAmelCase:     List[Any]     			=   tuple(UpperCAmelCase      )

        # Send to model
        __lowerCAmelCase:     Optional[int]     			=   model(tuple_input[:-1]      )[0]

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


     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:     Optional[int]     			=   TFMobileViTModel.from_pretrained(UpperCAmelCase      )
       self.assertIsNotNone(UpperCAmelCase      )







def _a				( )					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     List[str]     			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image






@require_tf
class 							A_				(	unittest.TestCase      ):


     @slow
     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[Any]:
      __lowerCAmelCase:     List[str]     			=   TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'      )

      __lowerCAmelCase:     int     			=   MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'      )
      __lowerCAmelCase:     Optional[int]     			=   prepare_img()
      __lowerCAmelCase:     Optional[int]     			=   image_processor(images=UpperCAmelCase    ,						return_tensors='tf'      )

      # forward pass
      __lowerCAmelCase:     int     			=   model(**UpperCAmelCase    ,						training=UpperCAmelCase      )

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

      __lowerCAmelCase:     Optional[int]     			=   tf.constant([-1.9364, -1.2327, -0.4653]      )

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


     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Union[str, Any]:
      # `from_pt` will be removed
      __lowerCAmelCase:     int     			=   TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'      )

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

      __lowerCAmelCase:     Dict     			=   prepare_img()
      __lowerCAmelCase:     Tuple     			=   image_processor(images=UpperCAmelCase    ,						return_tensors='tf'      )

      # forward pass
      __lowerCAmelCase:     List[Any]     			=   model(inputs.pixel_values    ,						training=UpperCAmelCase      )
      __lowerCAmelCase:     Optional[Any]     			=   outputs.logits

      # verify the logits
      __lowerCAmelCase:     str     			=   tf.TensorShape((1, 2_1, 3_2, 3_2)      )
      self.assertEqual(logits.shape    ,						UpperCAmelCase      )

      __lowerCAmelCase:     Union[str, Any]     			=   tf.constant(
          [
              [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
              [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
              [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
          ]      )

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








from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class 							A_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size      )
      __lowerCAmelCase:     Optional[int]     			=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,						1      )
      __lowerCAmelCase:     List[Any]     			=   tf.concat([input_ids, eos_tensor]    ,						axis=1      )

      __lowerCAmelCase:     str     			=   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size      )

      __lowerCAmelCase:     Optional[int]     			=   self.config_cls(
          vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:     int     			=   ids_tensor((self.batch_size, 3)    ,						config.vocab_size      )
      __lowerCAmelCase:     Tuple     			=   tf.cast(ids_tensor((self.batch_size, 3)    ,						2      )    ,						tf.inta      )

      # append to next input_ids and
      __lowerCAmelCase:     Any     			=   tf.concat([input_ids, next_tokens]    ,						axis=-1      )
      __lowerCAmelCase:     Optional[int]     			=   tf.concat([attention_mask, next_attn_mask]    ,						axis=-1      )

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase      )[0]

      self.parent.assertEqual(next_tokens.shape[1]    ,						output_from_past.shape[1]      )

      # select random slice
      __lowerCAmelCase:     Optional[int]     			=   int(ids_tensor((1,)    ,						output_from_past.shape[-1]      )      )
      __lowerCAmelCase:     Tuple     			=   output_from_no_past[:, -3:, random_slice_idx]
      __lowerCAmelCase:     Dict     			=   output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(UpperCAmelCase    ,						UpperCAmelCase    ,						rtol=1E-3      )







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      config.pad_token_id	)					,      tf.inta	)
 if decoder_attention_mask is None:
  __lowerCAmelCase:     Tuple     			=   tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape					,      dtype=tf.inta	),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id	)					,      tf.inta	),
      ]					,      axis=-1					,      )
 if head_mask is None:
  __lowerCAmelCase:     List[str]     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __lowerCAmelCase:     List[Any]     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }






@require_tf
class 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Any:
      self.config_tester.run_common_tests()


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase      )







@require_tf
class 							A_				(	unittest.TestCase      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     int     			=   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


     @cached_property
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name      )


     @property
     def 						UpperCAmelCase						(							self    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   model.config
      self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]      )
      self.assertEqual(c.max_length    ,						5_1_2      )
      self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id      )
      return model


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'mt'
     _lowercase    :  Union[str, Any]          =			'en'
     _lowercase    :  str          =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     _lowercase    :  str          =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Optional[Any]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'      )
      __lowerCAmelCase:     Tuple     			=   pipeline(self.src_text      )
      self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]      )





