'''simple docstring'''




import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		Union[str, Any]  ,    a		:		List[Any]  ,    a		:		List[Any]=13  ,    a		:		List[str]=7  ,    a		:		Optional[Any]=True  ,    a		:		int=True  ,    a		:		Union[str, Any]=True  ,    a		:		Any=True  ,    a		:		Any=99  ,    a		:		Tuple=32  ,    a		:		int=5  ,    a		:		Union[str, Any]=4  ,    a		:		Tuple=37  ,    a		:		Optional[int]="gelu"  ,    a		:		Any=0.1  ,    a		:		List[Any]=0.1  ,    a		:		int=5_12  ,    a		:		Union[str, Any]=16  ,    a		:		Union[str, Any]=2  ,    a		:		List[Any]=0.02  ,    a		:		Dict=False  ,    a		:		Any=True  ,    a		:		Optional[Any]="None"  ,    a		:		Optional[Any]=3  ,    a		:		Optional[int]=4  ,    a		:		Tuple=None  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  parent
         __lowerCamelCase          =  batch_size
         __lowerCamelCase          =  seq_length
         __lowerCamelCase          =  is_training
         __lowerCamelCase          =  use_input_mask
         __lowerCamelCase          =  use_token_type_ids
         __lowerCamelCase          =  use_labels
         __lowerCamelCase          =  vocab_size
         __lowerCamelCase          =  hidden_size
         __lowerCamelCase          =  num_hidden_layers
         __lowerCamelCase          =  num_attention_heads
         __lowerCamelCase          =  intermediate_size
         __lowerCamelCase          =  hidden_act
         __lowerCamelCase          =  hidden_dropout_prob
         __lowerCamelCase          =  attention_probs_dropout_prob
         __lowerCamelCase          =  max_position_embeddings
         __lowerCamelCase          =  type_vocab_size
         __lowerCamelCase          =  type_sequence_label_size
         __lowerCamelCase          =  initializer_range
         __lowerCamelCase          =  num_labels
         __lowerCamelCase          =  num_choices
         __lowerCamelCase          =  relative_attention
         __lowerCamelCase          =  position_biased_input
         __lowerCamelCase          =  pos_att_type
         __lowerCamelCase          =  scope






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



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

         __lowerCamelCase          =  None
         if self.use_input_mask:
             __lowerCamelCase          =  ids_tensor([self.batch_size, self.seq_length]  ,    vocab_size=2   )

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

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

         __lowerCamelCase          =  self.get_config()

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return 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  ,    initializer_range=self.initializer_range  ,    relative_attention=self.relative_attention  ,    position_biased_input=self.position_biased_input  ,    pos_att_type=self.pos_att_type  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_config()
         __lowerCamelCase          =  3_00
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		List[Any]   ):





         """simple docstring"""



         self.parent.assertListEqual(list(result.loss.size()   )  ,    []   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		Union[str, Any]  ,    a		:		int  ,    a		:		Tuple  ,    a		:		str  ,    a		:		List[Any]  ,    a		:		Tuple  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaModel(config=a   )
         model.to(a   )
         model.eval()
         __lowerCamelCase          =  model(a  ,    attention_mask=a  ,    token_type_ids=a   )[0]
         __lowerCamelCase          =  model(a  ,    token_type_ids=a   )[0]
         __lowerCamelCase          =  model(a   )[0]

         self.parent.assertListEqual(list(sequence_output.size()   )  ,    [self.batch_size, self.seq_length, self.hidden_size]   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]  ,    a		:		int  ,    a		:		List[str]  ,    a		:		List[Any]  ,    a		:		List[Any]  ,    a		:		Optional[Any]  ,    a		:		List[str]  ,    a		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaForMaskedLM(config=a   )
         model.to(a   )
         model.eval()
         __lowerCamelCase          =  model(a  ,    attention_mask=a  ,    token_type_ids=a  ,    labels=a   )

         self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		Optional[Any]  ,    a		:		int  ,    a		:		str  ,    a		:		Union[str, Any]  ,    a		:		Any  ,    a		:		Dict  ,    a		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  self.num_labels
         __lowerCamelCase          =  DebertaForSequenceClassification(a   )
         model.to(a   )
         model.eval()
         __lowerCamelCase          =  model(a  ,    attention_mask=a  ,    token_type_ids=a  ,    labels=a   )
         self.parent.assertListEqual(list(result.logits.size()   )  ,    [self.batch_size, self.num_labels]   )
         self.check_loss_output(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		Dict  ,    a		:		int  ,    a		:		Any  ,    a		:		Dict  ,    a		:		Optional[int]  ,    a		:		List[str]  ,    a		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  self.num_labels
         __lowerCamelCase          =  DebertaForTokenClassification(config=a   )
         model.to(a   )
         model.eval()
         __lowerCamelCase          =  model(a  ,    attention_mask=a  ,    token_type_ids=a  ,    labels=a   )
         self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.num_labels)   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		Optional[Any]  ,    a		:		Any  ,    a		:		List[str]  ,    a		:		Tuple  ,    a		:		str  ,    a		:		Optional[int]  ,    a		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaForQuestionAnswering(config=a   )
         model.to(a   )
         model.eval()
         __lowerCamelCase          =  model(
             a  ,    attention_mask=a  ,    token_type_ids=a  ,    start_positions=a  ,    end_positions=a  ,    )
         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		:		Union[str, Any]   ):





         """simple docstring"""



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




@require_torch
class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__							,       unittest.TestCase			):
     lowerCamelCase  :					Union[str, Any]							=(
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase  :					str							=(
         {
             "feature-extraction": DebertaModel,
             "fill-mask": DebertaForMaskedLM,
             "question-answering": DebertaForQuestionAnswering,
             "text-classification": DebertaForSequenceClassification,
             "token-classification": DebertaForTokenClassification,
             "zero-shot": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     lowerCamelCase  :					int							=True
     lowerCamelCase  :					List[str]							=False
     lowerCamelCase  :					Optional[Any]							=False
     lowerCamelCase  :					int							=False
     lowerCamelCase  :					List[Any]							=False






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaModelTester(self   )
         __lowerCamelCase          =  ConfigTester(self  ,    config_class=a  ,    hidden_size=37   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         self.config_tester.run_common_tests()






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*a   )






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCamelCase          =  DebertaModel.from_pretrained(a   )
             self.assertIsNotNone(a   )




@require_torch
@require_sentencepiece
@require_tokenizers
class    a__				(       unittest.TestCase			):






     @unittest.skip(reason='''Model not available yet'''   )
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         pass






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaModel.from_pretrained('''microsoft/deberta-base'''   )

         __lowerCamelCase          =  torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
         __lowerCamelCase          =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
         with torch.no_grad():
             __lowerCamelCase          =  model(a  ,    attention_mask=a   )[0]
         # compare the actual values for a slice.
         __lowerCamelCase          =  torch.tensor(
             [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]   )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,    a  ,    atol=1e-4   )  ,    f"""{output[:, 1:4, 1:4]}"""   )

'''simple docstring'''




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):






     @register_to_config
     def __init__(			self		:		Any  ,    *,
         a		:		int = 4  ,    a		:		int = 7_68  ,    a		:		int  ,    a		:		Optional[int]  ,    ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase          =  nn.Parameter(torch.zeros(a   )   )

         # parameters for additional clip time embeddings
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )

         # parameters for encoder hidden states
         __lowerCamelCase          =  clip_extra_context_tokens
         __lowerCamelCase          =  nn.Linear(
             a  ,    self.clip_extra_context_tokens * cross_attention_dim   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.LayerNorm(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    *, a		:		Union[str, Any]  ,    a		:		Any  ,    a		:		str  ,    a		:		Any   ):





         """simple docstring"""



         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __lowerCamelCase          =  image_embeddings.shape[0]
             __lowerCamelCase          =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0   )
             __lowerCamelCase          =  classifier_free_guidance_embeddings.expand(
                 a  ,    -1   )
             __lowerCamelCase          =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]  ,    dim=0   )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __lowerCamelCase          =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __lowerCamelCase          =  self.embedding_proj(a   )
         __lowerCamelCase          =  self.clip_image_embeddings_project_to_time_embeddings(a   )
         __lowerCamelCase          =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __lowerCamelCase          =  self.clip_extra_context_tokens_proj(a   )
         __lowerCamelCase          =  clip_extra_context_tokens.reshape(a  ,    -1  ,    self.clip_extra_context_tokens   )
         __lowerCamelCase          =  clip_extra_context_tokens.permute(0  ,    2  ,    1   )

         __lowerCamelCase          =  self.encoder_hidden_states_proj(a   )
         __lowerCamelCase          =  self.text_encoder_hidden_states_norm(a   )
         __lowerCamelCase          =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  ,    dim=1   )

         return text_encoder_hidden_states, additive_clip_time_embeddings
