
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:
   __UpperCAmelCase   						=     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	)				->				int:
          SCREAMING_SNAKE_CASE_	=      tempfile.mkdtemp()

          SCREAMING_SNAKE_CASE_	=      PixaStructImageProcessor()
          SCREAMING_SNAKE_CASE_	=      TaTokenizer.from_pretrained('''t5-small'''	)

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(__lowercase ,     __lowercase	)

          processor.save_pretrained(self.tmpdirname	)



       def     _UpperCamelCase	(							self ,     **_A	)				->				int:
          return AutoProcessor.from_pretrained(self.tmpdirname ,     **__lowercase	).tokenizer



       def     _UpperCamelCase	(							self ,     **_A	)				->				Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname ,     **__lowercase	).image_processor



       def     _UpperCamelCase	(							self	)				->				Dict:
          shutil.rmtree(self.tmpdirname	)



       def     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      [np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta	)]

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

          return image_inputs



       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=self.get_tokenizer() ,     image_processor=self.get_image_processor()	)
          processor.save_pretrained(self.tmpdirname	)

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

          SCREAMING_SNAKE_CASE_	=      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[str]:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

          SCREAMING_SNAKE_CASE_	=      self.prepare_image_inputs()

          SCREAMING_SNAKE_CASE_	=      image_processor(__lowercase ,     return_tensors='''np'''	)
          SCREAMING_SNAKE_CASE_	=      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	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

          SCREAMING_SNAKE_CASE_	=      '''lower newer'''

          SCREAMING_SNAKE_CASE_	=      processor(text=__lowercase	)

          SCREAMING_SNAKE_CASE_	=      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	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

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

          SCREAMING_SNAKE_CASE_	=      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	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

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

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

          SCREAMING_SNAKE_CASE_	=      [512, 1024, 2048, 4096]
          SCREAMING_SNAKE_CASE_	=      [770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(__lowercase	):
             SCREAMING_SNAKE_CASE_	=      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	):
             SCREAMING_SNAKE_CASE_	=      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	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

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

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

          self.assertListEqual(__lowercase ,     __lowercase	)




       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.get_image_processor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      PixaStructProcessor(tokenizer=__lowercase ,     image_processor=__lowercase	)

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

          SCREAMING_SNAKE_CASE_	=      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''']	)

          SCREAMING_SNAKE_CASE_	=      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''']	)






'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

