import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class     _A   (    unittest.TestCase							):

  '''simple docstring'''





  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      tempfile.mkdtemp()

       UpperCamelCase__										=      BlipImageProcessor()
       UpperCamelCase__										=      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''	)

       UpperCamelCase__										=      BlipaProcessor(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       processor.save_pretrained(self.tmpdirname	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       return AutoProcessor.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	).tokenizer






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       return AutoProcessor.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	).image_processor






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       shutil.rmtree(self.tmpdirname	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      [np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta	)]

       UpperCamelCase__										=      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_   ,				0   ,				-1	)	) for x in image_inputs]

       return image_inputs






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      BlipaProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()	)
       processor.save_pretrained(self.tmpdirname	)

       UpperCamelCase__										=      self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''	)
       UpperCamelCase__										=      self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_   ,				padding_value=1.0	)

       UpperCamelCase__										=      BlipaProcessor.from_pretrained(
           self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=SCREAMING_SNAKE_CASE_   ,				padding_value=1.0	)

       self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()	)
       self.assertIsInstance(processor.tokenizer   ,				SCREAMING_SNAKE_CASE_	)

       self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string()	)
       self.assertIsInstance(processor.image_processor   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)
       UpperCamelCase__										=      processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()   ,				input_processor[key].sum()   ,				delta=1E-2	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				return_token_type_ids=SCREAMING_SNAKE_CASE_	)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(list(inputs.keys()	)   ,				['''pixel_values''', '''input_ids''', '''attention_mask''']	)

       # test if it raises when no input is passed
       with pytest.raises(SCREAMING_SNAKE_CASE_	):
            processor()






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__										=      processor.batch_decode(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

       # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
       self.assertListEqual(list(inputs.keys()	)   ,				['''pixel_values''', '''input_ids''', '''attention_mask''']	)




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)  # pylint: disable=invalid-name



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

       UpperCamelCase__										=      CLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      PaintByExampleMapper(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.LayerNorm(config.hidden_size	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				self.proj_size	)

       # uncondition for scaling
       UpperCamelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				activation_fn='''gelu'''   ,				attention_bias=SCREAMING_SNAKE_CASE_	)
               for _ in range(SCREAMING_SNAKE_CASE_	)
           ]	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



