"""simple docstring"""


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				(		lowerCAmelCase_   ):


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


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)

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

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

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

      _UpperCAmelCase						 =      self.get_config()

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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      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      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  List[str]					):
      self.parent.assertListEqual(list(result.loss.size()					)   ,				[]					)


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

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


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      DebertaVaForMaskedLM(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.num_labels
      _UpperCAmelCase						 =      DebertaVaForSequenceClassification(__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase					)
      self.parent.assertListEqual(list(result.logits.size()					)   ,				[self.batch_size, self.num_labels]					)
      self.check_loss_output(__lowerCAmelCase					)


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


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


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				
      )						 =      config_and_inputs
      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Tuple     							=							(
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    _snake_case     :  List[Any]     							=							(
        {
            'feature-extraction': DebertaVaModel,
            'fill-mask': DebertaVaForMaskedLM,
            'question-answering': DebertaVaForQuestionAnswering,
            'text-classification': DebertaVaForSequenceClassification,
            'token-classification': DebertaVaForTokenClassification,
            'zero-shot': DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    _snake_case     :  Optional[Any]     							=							True
    _snake_case     :  Union[str, Any]     							=							False
    _snake_case     :  Union[str, Any]     							=							False
    _snake_case     :  Optional[int]     							=							False
    _snake_case     :  Optional[Any]     							=							False


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      DebertaVaModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				hidden_size=37					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_multiple_choice(*__lowerCAmelCase					)


    @slow
    def      lowerCAmelCase_  (					self	:  Any					):
      for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase						 =      DebertaVaModel.from_pretrained(__lowerCAmelCase					)
        self.assertIsNotNone(__lowerCAmelCase					)



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


    @unittest.skip(reason="""Model not available yet"""					)
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      pass


    @slow
    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""					)

      _UpperCAmelCase						 =      torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
      _UpperCAmelCase						 =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
      with torch.no_grad():
        _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase					)[0]
      # compare the actual values for a slice.
      _UpperCAmelCase						 =      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]   ,				__lowerCAmelCase   ,				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__":
    UpperCAmelCase__  		=						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.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						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)
