

'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
  import torch


if is_vision_available():
  from PIL import Image

  from transformers import MgpstrProcessor, ViTImageProcessor







@require_torch
@require_vision
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = ViTImageProcessor if is_vision_available() else None
 @property
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					List[Any]:


      """simple docstring"""




      return self.image_processor_tester.prepare_image_processor_dict()
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (3, 32, 128)
      __SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE      = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      __SCREAMING_SNAKE_CASE      = dict(zip(__SCREAMING_SNAKE_CASE   ,				range(len(__SCREAMING_SNAKE_CASE     )     )     )     )

      __SCREAMING_SNAKE_CASE      = os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
      with open(self.vocab_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
           fp.write(json.dumps(__SCREAMING_SNAKE_CASE     ) + """\n"""     )

      __SCREAMING_SNAKE_CASE      = {
          """do_normalize""": False,
          """do_resize""": True,
          """image_processor_type""": """ViTImageProcessor""",
          """resample""": 3,
          """size""": {"""height""": 32, """width""": 128},
      }
      __SCREAMING_SNAKE_CASE      = os.path.join(self.tmpdirname   ,				__SCREAMING_SNAKE_CASE     )
      with open(self.image_processor_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
           json.dump(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					str:


      """simple docstring"""




      return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Dict:


      """simple docstring"""




      return ViTImageProcessor.from_pretrained(self.tmpdirname   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      shutil.rmtree(self.tmpdirname     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta     )

      __SCREAMING_SNAKE_CASE      = Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE   ,				0   ,				-1     )     )

      return image_input
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_image_processor()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )
      processor.save_pretrained(self.tmpdirname     )
      __SCREAMING_SNAKE_CASE      = MgpstrProcessor.from_pretrained(self.tmpdirname   ,				use_fast=__SCREAMING_SNAKE_CASE     )

      self.assertEqual(processor.char_tokenizer.get_vocab()   ,				tokenizer.get_vocab()     )
      self.assertIsInstance(processor.char_tokenizer   ,				__SCREAMING_SNAKE_CASE     )

      self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor.to_json_string()     )
      self.assertIsInstance(processor.image_processor   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_image_processor()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )
      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=__SCREAMING_SNAKE_CASE   ,				padding_value=1.0     )

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor.from_pretrained(
          self.tmpdirname   ,				bos_token="""(BOS)"""   ,				eos_token="""(EOS)"""   ,				do_normalize=__SCREAMING_SNAKE_CASE   ,				padding_value=1.0     )

      self.assertEqual(processor.char_tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()     )
      self.assertIsInstance(processor.char_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  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      = image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )
      __SCREAMING_SNAKE_CASE      = processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )

      for key in input_image_proc.keys():
           self.assertAlmostEqual(input_image_proc[key].sum()   ,				input_processor[key].sum()   ,				delta=1E-2     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = """test"""

      __SCREAMING_SNAKE_CASE      = processor(text=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = tokenizer(__SCREAMING_SNAKE_CASE     )
      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = """test"""
      __SCREAMING_SNAKE_CASE      = self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      = processor(text=__SCREAMING_SNAKE_CASE   ,				images=__SCREAMING_SNAKE_CASE     )

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

      # test if it raises when no input is passed
      with pytest.raises(__SCREAMING_SNAKE_CASE     ):
           processor()
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = processor.char_decode(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = [seq.replace(""" """   ,				""""""     ) for seq in decoded_tok]

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      = processor(text=__SCREAMING_SNAKE_CASE   ,				images=__SCREAMING_SNAKE_CASE     )

      self.assertListEqual(list(inputs.keys()     )   ,				processor.model_input_names     )


 def  UpperCAmelCase__						(						self   :			Any     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_image_processor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,				image_processor=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.randn(1   ,				27   ,				38     )
      __SCREAMING_SNAKE_CASE      = torch.randn(1   ,				27   ,				50_257     )
      __SCREAMING_SNAKE_CASE      = torch.randn(1   ,				27   ,				30_522     )

      __SCREAMING_SNAKE_CASE      = processor.batch_decode([char_input, bpe_input, wp_input]     )

      self.assertListEqual(list(results.keys()     )   ,				["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]     )









'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


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


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






