

import unittest

from transformers import DebertaVaConfig, 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 (
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
      )
      from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				List[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Any=13						,			UpperCAmelCase							:				List[str]=7						,			UpperCAmelCase							:				Dict=True						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				int=True						,			UpperCAmelCase							:				Union[str, Any]=True						,			UpperCAmelCase							:				List[Any]=99						,			UpperCAmelCase							:				List[str]=32						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Union[str, Any]=4						,			UpperCAmelCase							:				Dict=37						,			UpperCAmelCase							:				str="gelu"						,			UpperCAmelCase							:				Tuple=0.1						,			UpperCAmelCase							:				Any=0.1						,			UpperCAmelCase							:				Tuple=512						,			UpperCAmelCase							:				Dict=16						,			UpperCAmelCase							:				int=2						,			UpperCAmelCase							:				Any=0.02						,			UpperCAmelCase							:				List[str]=False						,			UpperCAmelCase							:				List[str]=True						,			UpperCAmelCase							:				Tuple="None"						,			UpperCAmelCase							:				Optional[int]=3						,			UpperCAmelCase							:				str=4						,			UpperCAmelCase							:				List[str]=None						,			):
            A_						  =				parent
            A_						  =				batch_size
            A_						  =				seq_length
            A_						  =				is_training
            A_						  =				use_input_mask
            A_						  =				use_token_type_ids
            A_						  =				use_labels
            A_						  =				vocab_size
            A_						  =				hidden_size
            A_						  =				num_hidden_layers
            A_						  =				num_attention_heads
            A_						  =				intermediate_size
            A_						  =				hidden_act
            A_						  =				hidden_dropout_prob
            A_						  =				attention_probs_dropout_prob
            A_						  =				max_position_embeddings
            A_						  =				type_vocab_size
            A_						  =				type_sequence_label_size
            A_						  =				initializer_range
            A_						  =				num_labels
            A_						  =				num_choices
            A_						  =				relative_attention
            A_						  =				position_biased_input
            A_						  =				pos_att_type
            A_						  =				scope





      def 		__A	(   self							:				Tuple	):
            A_						  =				ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size	)

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

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

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

            A_						  =				self.get_config()

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





      def 		__A	(   self							:				Optional[int]	):
            return DebertaVaConfig(
                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 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				Tuple	):
            self.parent.assertListEqual(list(result.loss.size()	)						,			[]	)





      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				List[str]	):
            A_						  =				DebertaVaModel(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase	)[0]
            A_						  =				model(UpperCAmelCase						,			token_type_ids=UpperCAmelCase	)[0]
            A_						  =				model(UpperCAmelCase	)[0]

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





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Dict	):
            A_						  =				DebertaVaForMaskedLM(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			labels=UpperCAmelCase	)

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





      def 		__A	(   self							:				int						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Dict	):
            A_						  =				self.num_labels
            A_						  =				DebertaVaForSequenceClassification(UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.parent.assertListEqual(list(result.logits.size()	)						,			[self.batch_size, self.num_labels]	)
            self.check_loss_output(UpperCAmelCase	)





      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Dict	):
            A_						  =				self.num_labels
            A_						  =				DebertaVaForTokenClassification(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			labels=UpperCAmelCase	)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.num_labels)	)





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Any	):
            A_						  =				DebertaVaForQuestionAnswering(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				model(
                UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			start_positions=UpperCAmelCase						,			end_positions=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 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				int	):
            A_						  =				DebertaVaForMultipleChoice(config=UpperCAmelCase	)
            model.to(UpperCAmelCase	)
            model.eval()
            A_						  =				input_ids.unsqueeze(1	).expand(-1						,			self.num_choices						,			-1	).contiguous()
            A_						  =				token_type_ids.unsqueeze(1	).expand(-1						,			self.num_choices						,			-1	).contiguous()
            A_						  =				input_mask.unsqueeze(1	).expand(-1						,			self.num_choices						,			-1	).contiguous()
            A_						  =				model(
                UpperCAmelCase						,			attention_mask=UpperCAmelCase						,			token_type_ids=UpperCAmelCase						,			labels=UpperCAmelCase						,			)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_choices)	)







      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.prepare_config_and_inputs()
            (
                (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     (
                A_
            )  ,     
            )						  =				config_and_inputs
            A_						  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict




@require_torch
class   _a					(			snake_case_     ,				snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       (
          (
              DebertaVaModel,
              DebertaVaForMaskedLM,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaForQuestionAnswering,
              DebertaVaForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      _lowerCamelCase       :			List[str]      		=       (
          {
              'feature-extraction': DebertaVaModel,
              'fill-mask': DebertaVaForMaskedLM,
              'question-answering': DebertaVaForQuestionAnswering,
              'text-classification': DebertaVaForSequenceClassification,
              'token-classification': DebertaVaForTokenClassification,
              'zero-shot': DebertaVaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      _lowerCamelCase       :			Dict      		=       True
      _lowerCamelCase       :			Any      		=       False
      _lowerCamelCase       :			str      		=       False
      _lowerCamelCase       :			Optional[int]      		=       False
      _lowerCamelCase       :			Optional[Any]      		=       False





      def 		__A	(   self							:				Dict	):
            A_						  =				DebertaVaModelTester(self	)
            A_						  =				ConfigTester(self						,			config_class=UpperCAmelCase						,			hidden_size=37	)





      def 		__A	(   self							:				Union[str, Any]	):
            self.config_tester.run_common_tests()





      def 		__A	(   self							:				List[str]	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase	)





      def 		__A	(   self							:				int	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            A_						  =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_multiple_choice(*UpperCAmelCase	)







      @slow
      def 		__A	(   self							:				Optional[int]	):
            for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						  =				DebertaVaModel.from_pretrained(UpperCAmelCase	)
                  self.assertIsNotNone(UpperCAmelCase	)




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




      """simple docstring"""

      @unittest.skip(reason="Model not available yet"	)
      def 		__A	(   self							:				Dict	):
            pass







      @slow
      def 		__A	(   self							:				Tuple	):
            A_						  =				DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"	)

            A_						  =				torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	)
            A_						  =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
            with torch.no_grad():
                  A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	)[0]
            # compare the actual values for a slice.
            A_						  =				torch.tensor(
                [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]	)
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,			UpperCAmelCase						,			atol=1E-4	)						,			f'''{output[:, 1:4, 1:4]}'''	)

'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
