



"""simple docstring"""


import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
    )
    from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class        a				:


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  List[str]=13   ,				__lowerCAmelCase	:  Any=7   ,				__lowerCAmelCase	:  Dict=True   ,				__lowerCAmelCase	:  Dict=True   ,				__lowerCAmelCase	:  int=True   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				__lowerCAmelCase	:  Dict=99   ,				__lowerCAmelCase	:  List[str]=16   ,				__lowerCAmelCase	:  Dict=36   ,				__lowerCAmelCase	:  List[Any]=6   ,				__lowerCAmelCase	:  Optional[int]=6   ,				__lowerCAmelCase	:  str=6   ,				__lowerCAmelCase	:  Any=37   ,				__lowerCAmelCase	:  List[str]="gelu"   ,				__lowerCAmelCase	:  Union[str, Any]=0.1   ,				__lowerCAmelCase	:  Union[str, Any]=0.1   ,				__lowerCAmelCase	:  Optional[Any]=512   ,				__lowerCAmelCase	:  int=16   ,				__lowerCAmelCase	:  List[str]=2   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Optional[int]=3   ,				__lowerCAmelCase	:  str=4   ,				__lowerCAmelCase	:  Dict=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						 =      embedding_size
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_hidden_groups
      _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						 =      scope


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

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

      _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	:  Optional[int]					):
      return AlbertConfig(
          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   ,				num_hidden_groups=self.num_hidden_groups   ,				)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      AlbertModel(config=__A					)
      model.to(__A					)
      model.eval()
      _UpperCAmelCase						 =      model(__A   ,				attention_mask=__A   ,				token_type_ids=__A					)
      _UpperCAmelCase						 =      model(__A   ,				token_type_ids=__A					)
      _UpperCAmelCase						 =      model(__A					)
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)
      self.parent.assertEqual(result.pooler_output.shape   ,				(self.batch_size, self.hidden_size)					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      AlbertForPreTraining(config=__A					)
      model.to(__A					)
      model.eval()
      _UpperCAmelCase						 =      model(
          __A   ,				attention_mask=__A   ,				token_type_ids=__A   ,				labels=__A   ,				sentence_order_label=__A   ,				)
      self.parent.assertEqual(result.prediction_logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)
      self.parent.assertEqual(result.sop_logits.shape   ,				(self.batch_size, config.num_labels)					)


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


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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      (
          _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     :  Optional[int]     							=							(
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    _snake_case     :  Union[str, Any]     							=							(
        {
            'feature-extraction': AlbertModel,
            'fill-mask': AlbertForMaskedLM,
            'question-answering': AlbertForQuestionAnswering,
            'text-classification': AlbertForSequenceClassification,
            'token-classification': AlbertForTokenClassification,
            'zero-shot': AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case     :  List[str]     							=							True


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple=False					):
      _UpperCAmelCase						 =      super()._prepare_for_class(__A   ,				__A   ,				return_labels=__A					)

      if return_labels:
        if model_class in get_values(__A					):
          _UpperCAmelCase						 =      torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)   ,				dtype=torch.long   ,				device=__A					)
          _UpperCAmelCase						 =      torch.zeros(
              self.model_tester.batch_size   ,				dtype=torch.long   ,				device=__A					)
      return inputs_dict


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      AlbertModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__A   ,				hidden_size=37					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__A					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__A					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__A					)


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


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__A					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__A					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        _UpperCAmelCase						 =      type
        self.model_tester.create_and_check_model(*__A					)


    @slow
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase						 =      AlbertModel.from_pretrained(__A					)
        self.assertIsNotNone(__A					)



@require_torch
class        a				(		unittest.TestCase   ):


    @slow
    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      AlbertModel.from_pretrained("""albert-base-v2"""					)
      _UpperCAmelCase						 =      torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
      _UpperCAmelCase						 =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
      with torch.no_grad():
        _UpperCAmelCase						 =      model(__A   ,				attention_mask=__A					)[0]
      _UpperCAmelCase						 =      torch.Size((1, 11, 768)					)
      self.assertEqual(output.shape   ,				__A					)
      _UpperCAmelCase						 =      torch.tensor(
          [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]					)

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,				__A   ,				atol=1e-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)
