






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        snake_case_				:







   """simple docstring"""



   def __init__(     self:						str ,							snake_case__:						str ,							snake_case__:						Tuple=13 ,							snake_case__:						List[Any]=7 ,							snake_case__:						int=True ,							snake_case__:						Union[str, Any]=True ,							snake_case__:						Optional[Any]=True ,							snake_case__:						str=True ,							snake_case__:						int=99 ,							snake_case__:						Tuple=32 ,							snake_case__:						Tuple=2 ,							snake_case__:						List[str]=4 ,							snake_case__:						Optional[Any]=37 ,							snake_case__:						Any="gelu" ,							snake_case__:						int=0.1 ,							snake_case__:						List[str]=0.1 ,							snake_case__:						int=512 ,							snake_case__:						Tuple=16 ,							snake_case__:						Any=2 ,							snake_case__:						Optional[int]=0.02 ,							snake_case__:						Dict=3 ,							snake_case__:						List[str]=4 ,							snake_case__:						int=None ,							)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						parent
       _lowercase     			=						13
       _lowercase     			=						7
       _lowercase     			=						True
       _lowercase     			=						True
       _lowercase     			=						True
       _lowercase     			=						True
       _lowercase     			=						99
       _lowercase     			=						32
       _lowercase     			=						2
       _lowercase     			=						4
       _lowercase     			=						37
       _lowercase     			=						'gelu'
       _lowercase     			=						0.1
       _lowercase     			=						0.1
       _lowercase     			=						512
       _lowercase     			=						16
       _lowercase     			=						False
       _lowercase     			=						-1
       _lowercase     			=						True
       _lowercase     			=						2
       _lowercase     			=						0.02
       _lowercase     			=						3
       _lowercase     			=						4
       _lowercase     			=						None

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size						)

       _lowercase     			=						None
       if self.use_input_mask:
           _lowercase     			=						random_attention_mask([self.batch_size, self.seq_length]						)

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

       _lowercase     			=						None
       _lowercase     			=						None
       _lowercase     			=						None
       if self.use_labels:
           _lowercase     			=						ids_tensor([self.batch_size] ,							self.type_sequence_label_size						)
           _lowercase     			=						ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels						)

       _lowercase     			=						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=_UpperCAmelCase ,							)

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Optional[int] ,							snake_case__:						int ,							snake_case__:						List[str] ,							snake_case__:						Optional[int] ,							snake_case__:						List[str] ,							snake_case__:						List[Any] ,							snake_case__:						Optional[int]						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						TFDebertaModel(config=_UpperCAmelCase						)
       _lowercase     			=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       _lowercase     			=						[input_ids, input_mask]
       _lowercase     			=						model(_UpperCAmelCase						)

       _lowercase     			=						model(_UpperCAmelCase						)

       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							snake_case__:						Dict ,							snake_case__:						Union[str, Any] ,							snake_case__:						List[str] ,							snake_case__:						List[str] ,							snake_case__:						str ,							snake_case__:						List[str] ,							snake_case__:						Optional[int]						)	->		List[Any]:


       """simple docstring"""



       _lowercase     			=						TFDebertaForMaskedLM(config=_UpperCAmelCase						)
       _lowercase     			=						{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       _lowercase     			=						model(_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						Optional[Any] ,							snake_case__:						Dict ,							snake_case__:						int ,							snake_case__:						Any ,							snake_case__:						List[Any] ,							snake_case__:						Dict ,							snake_case__:						Tuple						)	->		int:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						TFDebertaForSequenceClassification(config=_UpperCAmelCase						)
       _lowercase     			=						{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       _lowercase     			=						model(_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						List[str] ,							snake_case__:						Optional[Any] ,							snake_case__:						Any ,							snake_case__:						Optional[Any] ,							snake_case__:						Optional[Any] ,							snake_case__:						str ,							snake_case__:						Union[str, Any]						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						TFDebertaForTokenClassification(config=_UpperCAmelCase						)
       _lowercase     			=						{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       _lowercase     			=						model(_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						Tuple ,							snake_case__:						List[Any] ,							snake_case__:						Optional[int] ,							snake_case__:						Optional[Any] ,							snake_case__:						str ,							snake_case__:						Optional[Any] ,							snake_case__:						Dict						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						TFDebertaForQuestionAnswering(config=_UpperCAmelCase						)
       _lowercase     			=						{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       _lowercase     			=						model(_UpperCAmelCase						)
       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       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						self.prepare_config_and_inputs()
       (
           (
           _lowercase       
       )			,     (
           _lowercase       
       )			,     (
           _lowercase       
       )			,     (
           _lowercase       
       )			,     (
           _lowercase       
       )			,     (
           _lowercase       
       )			,     (
           _lowercase       
       )			,            
       )     			=						config_and_inputs
       _lowercase     			=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict






@require_tf
class        snake_case_				(							lowerCamelCase__   ,      lowerCamelCase__   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    (
       (
           TFDebertaModel,
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase  =    (
       {
           """feature-extraction""": TFDebertaModel,
           """fill-mask""": TFDebertaForMaskedLM,
           """question-answering""": TFDebertaForQuestionAnswering,
           """text-classification""": TFDebertaForSequenceClassification,
           """token-classification""": TFDebertaForTokenClassification,
           """zero-shot""": TFDebertaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase  =    False
   __lowerCAmelCase  =    False

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						TFDebertaModelTester(self						)
       _lowercase     			=						ConfigTester(self ,							config_class=_UpperCAmelCase ,							hidden_size=37						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		List[str]:


       """simple docstring"""



       self.config_tester.run_common_tests()

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase						)

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		List[Any]:


       """simple docstring"""



       _lowercase     			=						TFDebertaModel.from_pretrained('kamalkraj/deberta-base'						)
       self.assertIsNotNone(_UpperCAmelCase						)






@require_tf
class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



   @unittest.skip(reason='Model not available yet'						)
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Optional[int]:


       """simple docstring"""



       pass

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		str:


       """simple docstring"""



       _lowercase     			=						TFDebertaModel.from_pretrained('kamalkraj/deberta-base'						)
       _lowercase     			=						tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
       _lowercase     			=						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       _lowercase     			=						model(_UpperCAmelCase ,							attention_mask=_UpperCAmelCase						)[0]

       _lowercase     			=						tf.constant(
           [
               [
                   [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                   [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                   [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
               ]
           ]						)
       tf.debugging.assert_near(output[:, 1:4, 1:4] ,							_UpperCAmelCase ,							atol=1E-4						)




import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPTextConfig(
           bos_token_id=0						, eos_token_id=2						, hidden_size=self.text_embedder_hidden_size						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, layer_norm_eps=1E-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=10_00						, )
       return CLIPTextModelWithProjection(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]						, image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]						, resample=3						, size=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=_UpperCAmelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

       snake_case_			= image[0, -10:]
       snake_case_			= image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2



 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase						, relax_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )