
"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
    )






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=13    ,     snake_case_=7    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=99    ,     snake_case_=32    ,     snake_case_=2    ,     snake_case_=4    ,     snake_case_=37    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=16    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=3    ,     snake_case_=4    ,     snake_case_=None    ,     ):
      _snake_case  :    Tuple            = parent
      _snake_case  :    Any            = 13
      _snake_case  :    Union[str, Any]            = 7
      _snake_case  :    List[str]            = True
      _snake_case  :    List[Any]            = True
      _snake_case  :    Union[str, Any]            = True
      _snake_case  :    str            = True
      _snake_case  :    int            = 99
      _snake_case  :    str            = 32
      _snake_case  :    Dict            = 2
      _snake_case  :    str            = 4
      _snake_case  :    List[str]            = 37
      _snake_case  :    int            = "gelu"
      _snake_case  :    Any            = 0.1
      _snake_case  :    Union[str, Any]            = 0.1
      _snake_case  :    List[str]            = 5_12
      _snake_case  :    List[Any]            = 16
      _snake_case  :    Optional[Any]            = False
      _snake_case  :    List[str]            = -1
      _snake_case  :    Dict            = True
      _snake_case  :    int            = 2
      _snake_case  :    Dict            = 0.02
      _snake_case  :    int            = 3
      _snake_case  :    Dict            = 4
      _snake_case  :    List[Any]            = None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  )

      _snake_case  :    Optional[int]            = None
      if self.use_input_mask:
        _snake_case  :    Optional[int]            = random_attention_mask([self.batch_size, self.seq_length]  )

      _snake_case  :    Dict            = None
      if self.use_token_type_ids:
        _snake_case  :    int            = ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size  )

      _snake_case  :    str            = None
      _snake_case  :    Any            = None
      _snake_case  :    Any            = None
      if self.use_labels:
        _snake_case  :    str            = ids_tensor([self.batch_size]    ,     self.type_sequence_label_size  )
        _snake_case  :    Optional[int]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels  )

      _snake_case  :    List[str]            = DebertaConfig(
          vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     relative_attention=self.relative_attention    ,     max_relative_positions=self.max_relative_positions    ,     position_biased_input=self.position_biased_input    ,     initializer_range=self.initializer_range    ,     return_dict=snake_case_    ,     )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = TFDebertaModel(config=snake_case_  )
      _snake_case  :    Dict            = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      _snake_case  :    Any            = [input_ids, input_mask]
      _snake_case  :    Tuple            = model(snake_case_  )

      _snake_case  :    Any            = model(snake_case_  )

      self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[Any]            = TFDebertaForMaskedLM(config=snake_case_  )
      _snake_case  :    str            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _snake_case  :    int            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[int]            = self.num_labels
      _snake_case  :    str            = TFDebertaForSequenceClassification(config=snake_case_  )
      _snake_case  :    Optional[int]            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _snake_case  :    Optional[Any]            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = self.num_labels
      _snake_case  :    List[str]            = TFDebertaForTokenClassification(config=snake_case_  )
      _snake_case  :    Dict            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _snake_case  :    Tuple            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[str]            = TFDebertaForQuestionAnswering(config=snake_case_  )
      _snake_case  :    Optional[int]            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _snake_case  :    Optional[Any]            = model(snake_case_  )
      self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length)  )
      self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length)  )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.prepare_config_and_inputs()
      (
          (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       
      )  :    Union[str, Any]            = config_and_inputs
      _snake_case  :    Dict            = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict






@require_tf
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			str	=       (
        (
            TFDebertaModel,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __lowercase				:			List[str]	=       (
        {
            """feature-extraction""": TFDebertaModel,
            """fill-mask""": TFDebertaForMaskedLM,
            """question-answering""": TFDebertaForQuestionAnswering,
            """text-classification""": TFDebertaForSequenceClassification,
            """token-classification""": TFDebertaForTokenClassification,
            """zero-shot""": TFDebertaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __lowercase				:			Tuple	=       False
    __lowercase				:			str	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = TFDebertaModelTester(self  )
      _snake_case  :    int            = ConfigTester(self    ,     config_class=snake_case_    ,     hidden_size=37  )
    def        lowerCamelCase__						(     self  ):
      self.config_tester.run_common_tests()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case_  )


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
      self.assertIsNotNone(snake_case_  )






@require_tf
class   _UpperCAmelCase					(       unittest.TestCase):
    @unittest.skip(reason="Model not available yet"  )
    def        lowerCamelCase__						(     self  ):
      pass


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
      _snake_case  :    List[str]            = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _snake_case  :    Optional[Any]            = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      _snake_case  :    List[Any]            = model(snake_case_    ,     attention_mask=snake_case_  )[0]

      _snake_case  :    str            = tf.constant(
          [
              [
                  [-0.59855896, -0.80552566, -0.8462135],
                  [1.4484025, -0.93483794, -0.80593085],
                  [0.3122741, 0.00316059, -1.4131377],
              ]
          ]  )
      tf.debugging.assert_near(output[:, 1:4, 1:4]    ,     snake_case_    ,     atol=1E-4  )





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor



class 							a       (      unittest.TestCase ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=3     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=18     ,						SCREAMING_SNAKE_CASE_       :					List[str]=30     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=400     ,						SCREAMING_SNAKE_CASE_       :					Dict=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						):
        __lowerCamelCase:					List[Any]	 =						size if size is not None else {"""shortest_edge""": 20}
        __lowerCamelCase:					Tuple	 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        __lowerCamelCase:					List[Any]	 =						parent
        __lowerCamelCase:					Optional[int]	 =						batch_size
        __lowerCamelCase:					str	 =						num_channels
        __lowerCamelCase:					Dict	 =						image_size
        __lowerCamelCase:					Tuple	 =						min_resolution
        __lowerCamelCase:					Union[str, Any]	 =						max_resolution
        __lowerCamelCase:					Tuple	 =						do_resize
        __lowerCamelCase:					List[str]	 =						size
        __lowerCamelCase:					int	 =						do_center_crop
        __lowerCamelCase:					Tuple	 =						crop_size







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class 							a       (      _UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	int       =						MobileNetVaImageProcessor if is_vision_available() else None





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						MobileNetVaImageProcessingTester(self				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_resize"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""size"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_center_crop"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""center_crop"""				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					str	 =						self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 20}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 18, """width""": 18}				)

        __lowerCamelCase:					Optional[int]	 =						self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 42}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 84, """width""": 84}				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					List[str]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        __lowerCamelCase:					str	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image				)

        # Test not batched input
        __lowerCamelCase:					Optional[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Tuple	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					Union[str, Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __lowerCamelCase:					Tuple	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						numpify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						np.ndarray				)

        # Test not batched input
        __lowerCamelCase:					List[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[Any]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					str	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __lowerCamelCase:					List[str]	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						torchify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						torch.Tensor				)

        # Test not batched input
        __lowerCamelCase:					Union[str, Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[int]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)


