'''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      lowerCamelCase_     (_a   ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple     =						{
          '''sld_warmup_steps''': 1_5,
          '''sld_guidance_scale''': 2_0,
          '''sld_threshold''': 0.0,
          '''sld_momentum_scale''': 0.0,
          '''sld_mom_beta''': 0.0,
      }
      __UpperCamelCase:  Optional[int]     =						{
          '''sld_warmup_steps''': 1_0,
          '''sld_guidance_scale''': 1_0_0_0,
          '''sld_threshold''': 0.01,
          '''sld_momentum_scale''': 0.3,
          '''sld_mom_beta''': 0.4,
      }
      __UpperCamelCase:  List[Any]     =						{
          '''sld_warmup_steps''': 7,
          '''sld_guidance_scale''': 2_0_0_0,
          '''sld_threshold''': 0.025,
          '''sld_momentum_scale''': 0.5,
          '''sld_mom_beta''': 0.7,
      }
      __UpperCamelCase:  Tuple     =						{
          '''sld_warmup_steps''': 0,
          '''sld_guidance_scale''': 5_0_0_0,
          '''sld_threshold''': 1.0,
          '''sld_momentum_scale''': 0.5,
          '''sld_mom_beta''': 0.7,
      }

@dataclass
class      lowerCamelCase_     (_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


'''simple docstring'''




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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

             _UpperCAmelCase					:  Optional[Any]											=   [Image.fromarray(np.moveaxis(A						, 0						, -1	)	) for x in image_inputs]

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.from_pretrained(
                 self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=A						, padding_value=1.0	)

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

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




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, 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						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

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




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

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

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




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

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

