import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   lowerCamelCase__   = False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,       reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''',       )
@require_vision
@require_torch
class  _UpperCAmelCase       (			unittest.TestCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					Any)		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase				=  tempfile.mkdtemp()

   _UpperCamelCase				=  PixaStructImageProcessor()
   _UpperCamelCase				=  TaTokenizer.from_pretrained("t5-small")

   _UpperCamelCase				=  PixaStructProcessor(lowercase_				,      lowercase_)

   processor.save_pretrained(self.tmpdirname)







 def 						__UpperCAmelCase							(						self							:					List[str]				,      **lowercase_							:					Union[str, Any])		->     List[Any]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Any				,      **lowercase_							:					int)		->     Optional[int]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[str]:


   """simple docstring"""







   shutil.rmtree(self.tmpdirname)







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Optional[int]:


   """simple docstring"""







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

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

   return image_inputs







 def 						__UpperCAmelCase							(						self							:					str)		->     Any:


   """simple docstring"""







   _UpperCamelCase				=  PixaStructProcessor(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=lowercase_				,      padding_value=1.0)

   _UpperCamelCase				=  PixaStructProcessor.from_pretrained(
       self.tmpdirname				,      bos_token="(BOS)"				,      eos_token="(EOS)"				,      do_normalize=lowercase_				,      padding_value=1.0)

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

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







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Dict:


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

   _UpperCamelCase				=  self.prepare_image_inputs()

   _UpperCamelCase				=  image_processor(lowercase_				,      return_tensors="np")
   _UpperCamelCase				=  processor(images=lowercase_				,      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 						__UpperCAmelCase							(						self							:					int)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

   _UpperCamelCase				=  "lower newer"

   _UpperCamelCase				=  processor(text=lowercase_)

   _UpperCamelCase				=  tokenizer(lowercase_				,      return_token_type_ids=lowercase_				,      add_special_tokens=lowercase_)

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







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

   _UpperCamelCase				=  "lower newer"
   _UpperCamelCase				=  self.prepare_image_inputs()

   _UpperCamelCase				=  processor(text=lowercase_				,      images=lowercase_)

   self.assertListEqual(
       list(inputs.keys())				,      ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"])

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







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

   _UpperCamelCase				=  "lower newer"
   _UpperCamelCase				=  self.prepare_image_inputs()

   _UpperCamelCase				=  processor(text=lowercase_				,      images=lowercase_)

   _UpperCamelCase				=  [512, 1024, 2048, 4096]
   _UpperCamelCase				=  [770, 770, 770, 770]
   # with text
   for i, max_patch in enumerate(lowercase_):
     _UpperCamelCase				=  processor(text=lowercase_				,      images=lowercase_				,      max_patches=lowercase_)
     self.assertEqual(inputs["flattened_patches"][0].shape[0]				,      lowercase_)
     self.assertEqual(inputs["flattened_patches"][0].shape[1]				,      expected_hidden_size[i])

   # without text input
   for i, max_patch in enumerate(lowercase_):
     _UpperCamelCase				=  processor(images=lowercase_				,      max_patches=lowercase_)
     self.assertEqual(inputs["flattened_patches"][0].shape[0]				,      lowercase_)
     self.assertEqual(inputs["flattened_patches"][0].shape[1]				,      expected_hidden_size[i])







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


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

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

   _UpperCamelCase				=  processor.batch_decode(lowercase_)
   _UpperCamelCase				=  tokenizer.batch_decode(lowercase_)

   self.assertListEqual(lowercase_				,      lowercase_)







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


   """simple docstring"""







   _UpperCamelCase				=  self.get_image_processor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  PixaStructProcessor(tokenizer=lowercase_				,      image_processor=lowercase_)

   _UpperCamelCase				=  "lower newer"
   _UpperCamelCase				=  self.prepare_image_inputs()

   _UpperCamelCase				=  processor(text=lowercase_				,      images=lowercase_)

   # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
   self.assertListEqual(
       list(inputs.keys())				,      ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"])

   _UpperCamelCase				=  processor(text=lowercase_)

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





import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  _UpperCAmelCase       (			lowerCAmelCase,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = KandinskyVaaPriorEmbaEmbPipeline
 __A           = ['''prompt''', '''image''']
 __A           = ['''prompt''', '''image''']
 __A           = [
     '''num_images_per_prompt''',
     '''strength''',
     '''generator''',
     '''num_inference_steps''',
     '''latents''',
     '''negative_prompt''',
     '''guidance_scale''',
     '''output_type''',
     '''return_dict''',
 ]
 __A           = False







 @property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     List[str]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					Any)		->     Optional[int]:


   """simple docstring"""







   return self.time_input_dim







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     int:


   """simple docstring"""







   return self.time_input_dim * 4







 @property
 def 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







   return 100







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
   return tokenizer







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Optional[int]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPTextConfig(
       bos_token_id=0				,      eos_token_id=2				,      hidden_size=self.text_embedder_hidden_size				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      layer_norm_eps=1e-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
   return CLIPTextModelWithProjection(lowercase_)







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     List[str]:


   """simple docstring"""







   torch.manual_seed(0)

   _UpperCamelCase				=  {
       "num_attention_heads": 2,
       "attention_head_dim": 12,
       "embedding_dim": self.text_embedder_hidden_size,
       "num_layers": 1,
   }

   _UpperCamelCase				=  PriorTransformer(**lowercase_)
   # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
   _UpperCamelCase				=  nn.Parameter(torch.ones(model.clip_std.shape))
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					str)		->     Union[str, Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPVisionConfig(
       hidden_size=self.text_embedder_hidden_size				,      image_size=224				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      num_attention_heads=4				,      num_channels=3				,      num_hidden_layers=5				,      patch_size=14				,      )

   _UpperCamelCase				=  CLIPVisionModelWithProjection(lowercase_)
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPImageProcessor(
       crop_size=224				,      do_center_crop=lowercase_				,      do_normalize=lowercase_				,      do_resize=lowercase_				,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,      resample=3				,      size=224				,      )

   return image_processor







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.dummy_prior
   _UpperCamelCase				=  self.dummy_image_encoder
   _UpperCamelCase				=  self.dummy_text_encoder
   _UpperCamelCase				=  self.dummy_tokenizer
   _UpperCamelCase				=  self.dummy_image_processor

   _UpperCamelCase				=  UnCLIPScheduler(
       variance_type="fixed_small_log"				,      prediction_type="sample"				,      num_train_timesteps=1000				,      clip_sample=lowercase_				,      clip_sample_range=10.0				,      )

   _UpperCamelCase				=  {
       "prior": prior,
       "image_encoder": image_encoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "scheduler": scheduler,
       "image_processor": image_processor,
   }

   return components







 def 						__UpperCAmelCase							(						self							:					List[str]				,      lowercase_							:					Optional[int]				,      lowercase_							:					Union[str, Any]=0)		->     Tuple:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase				=  torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase				=  torch.Generator(device=lowercase_).manual_seed(lowercase_)

   _UpperCamelCase				=  floats_tensor((1, 3, 64, 64)				,      rng=random.Random(lowercase_)).to(lowercase_)
   _UpperCamelCase				=  image.cpu().permute(0				,      2				,      3				,      1)[0]
   _UpperCamelCase				=  Image.fromarray(np.uinta(lowercase_)).convert("RGB").resize((256, 256))

   _UpperCamelCase				=  {
       "prompt": "horse",
       "image": init_image,
       "strength": 0.5,
       "generator": generator,
       "guidance_scale": 4.0,
       "num_inference_steps": 2,
       "output_type": "np",
   }
   return inputs







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


   """simple docstring"""







   _UpperCamelCase				=  "cpu"

   _UpperCamelCase				=  self.get_dummy_components()

   _UpperCamelCase				=  self.pipeline_class(**lowercase_)
   _UpperCamelCase				=  pipe.to(lowercase_)

   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase				=  pipe(**self.get_dummy_inputs(lowercase_))
   _UpperCamelCase				=  output.image_embeds

   _UpperCamelCase				=  pipe(
       **self.get_dummy_inputs(lowercase_)				,      return_dict=lowercase_				,      )[0]

   _UpperCamelCase				=  image[0, -10:]
   _UpperCamelCase				=  image_from_tuple[0, -10:]

   assert image.shape == (1, 32)

   _UpperCamelCase				=  np.array(
       [
           0.1_07_12_84,
           1.3_33_02_71,
           0.61_26_02_23,
           -0.6_69_10_65,
           -0.3_84_68_52,
           -1.0_30_36_61,
           0.22_71_61_11,
           0.03_34_89_01,
           0.30_04_06_75,
           -0.24_80_50_29,
       ])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  True
   _UpperCamelCase				=  False

   self._test_inference_batch_single_identical(
       test_max_difference=lowercase_				,      relax_max_difference=lowercase_				,      test_mean_pixel_difference=lowercase_				,      )







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  False

   self._test_attention_slicing_forward_pass(
       test_max_difference=lowercase_				,      test_mean_pixel_difference=lowercase_				,      )




