





import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						int=1_3   ,    lowerCAmelCase_					:						str=7   ,    lowerCAmelCase_					:						Dict=True   ,    lowerCAmelCase_					:						List[Any]=True   ,    lowerCAmelCase_					:						List[Any]=True   ,    lowerCAmelCase_					:						str=True   ,    lowerCAmelCase_					:						str=9_9   ,    lowerCAmelCase_					:						str=3_2   ,    lowerCAmelCase_					:						Dict=5   ,    lowerCAmelCase_					:						int=4   ,    lowerCAmelCase_					:						Union[str, Any]=3_7   ,    lowerCAmelCase_					:						List[str]="gelu"   ,    lowerCAmelCase_					:						List[str]=0.1   ,    lowerCAmelCase_					:						Optional[Any]=0.1   ,    lowerCAmelCase_					:						Dict=5_1_2   ,    lowerCAmelCase_					:						Optional[int]=1_6   ,    lowerCAmelCase_					:						List[str]=2   ,    lowerCAmelCase_					:						List[str]=0.02   ,    lowerCAmelCase_					:						Optional[Any]=4   ,    )				->		Optional[Any]:
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      seq_length
     __lowerCAmelCase				      =      is_training
     __lowerCAmelCase				      =      use_attention_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_choices






  def 							lowercase						(							self					:						List[Any]	)				->		Dict:
     __lowerCAmelCase				      =      ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size	)

     __lowerCAmelCase				      =      None
     if self.use_attention_mask:
        __lowerCAmelCase				      =      random_attention_mask([self.batch_size, self.seq_length]	)

     __lowerCAmelCase				      =      DistilBertConfig(
         vocab_size=self.vocab_size   ,    dim=self.hidden_size   ,    n_layers=self.num_hidden_layers   ,    n_heads=self.num_attention_heads   ,    hidden_dim=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    initializer_range=self.initializer_range   ,    tie_weights_=lowerCAmelCase_   ,    )

     return config, input_ids, attention_mask






  def 							lowercase						(							self					:						Any	)				->		int:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      config_and_inputs
     __lowerCAmelCase				      =      {'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict





@require_flax
class 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					(
      (
          FlaxDistilBertModel,
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 							lowercase						(							self					:						int	)				->		int:
     __lowerCAmelCase				      =      FlaxDistilBertModelTester(self	)






  @slow
  def 							lowercase						(							self					:						str	)				->		str:
     for model_class_name in self.all_model_classes:
        __lowerCAmelCase				      =      model_class_name.from_pretrained('distilbert-base-uncased'	)
        __lowerCAmelCase				      =      model(np.ones((1, 1)	)	)
        self.assertIsNotNone(lowerCAmelCase_	)





@require_flax
class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  @slow
  def 							lowercase						(							self					:						Optional[Any]	)				->		Tuple:
     __lowerCAmelCase				      =      FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'	)
     __lowerCAmelCase				      =      np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
     __lowerCAmelCase				      =      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_	)[0]
     __lowerCAmelCase				      =      (1, 1_1, 7_6_8)
     self.assertEqual(output.shape   ,    lowerCAmelCase_	)
     __lowerCAmelCase				      =      np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]	)

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








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					A       :   List[Any]					= 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.''',
					)

					A       :   List[Any]					= parser.parse_args()

					A       :   int					= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					A       :   Tuple					= CLIPImageProcessor()
					A       :   int					= CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

					A       :   Tuple					= 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)



