


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCamelCase_     (			UpperCamelCase__):

	"""simple docstring"""





	snake_case__    :  Optional[int]    =      ["image_processor", "tokenizer"]
	snake_case__    :  Tuple    =      "Pix2StructImageProcessor"
	snake_case__    :  List[Any]    =      ("T5Tokenizer", "T5TokenizerFast")




	def __init__(			self      :    int      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    List[Any]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    False
						super().__init__(__lowerCamelCase      ,      __lowerCamelCase       )




	def __call__(			self      :    int      ,      UpperCAmelCase__      :    Dict=None      ,      UpperCAmelCase__      :    Tuple = None      ,      UpperCAmelCase__      :    Optional[int] = True      ,      UpperCAmelCase__      :    int = False      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    List[str] = None      ,      UpperCAmelCase__      :    List[Any] = 2_0_4_8      ,      UpperCAmelCase__      :    Union[str, Any] = 0      ,      UpperCAmelCase__      :    Optional[int] = None      ,      UpperCAmelCase__      :    Dict = None      ,      UpperCAmelCase__      :    Union[str, Any] = False      ,      UpperCAmelCase__      :    str = False      ,      UpperCAmelCase__      :    List[Any] = False      ,      UpperCAmelCase__      :    List[Any] = False      ,      UpperCAmelCase__      :    Optional[int] = False      ,      UpperCAmelCase__      :    List[str] = True      ,      UpperCAmelCase__      :    Any = None      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     int:
						if images is None and text is None:
											raise ValueError("You have to specify either images or text."       )

						# Get only text
						if images is None and not self.image_processor.is_vqa:
											__SCREAMING_SNAKE_CASE						       =    self.tokenizer
											__SCREAMING_SNAKE_CASE						       =    self.tokenizer(
											    text=__lowerCamelCase      ,      add_special_tokens=__lowerCamelCase      ,      padding=__lowerCamelCase      ,      truncation=__lowerCamelCase      ,      max_length=__lowerCamelCase      ,      stride=__lowerCamelCase      ,      pad_to_multiple_of=__lowerCamelCase      ,      return_attention_mask=__lowerCamelCase      ,      return_overflowing_tokens=__lowerCamelCase      ,      return_special_tokens_mask=__lowerCamelCase      ,      return_offsets_mapping=__lowerCamelCase      ,      return_token_type_ids=__lowerCamelCase      ,      return_length=__lowerCamelCase      ,      verbose=__lowerCamelCase      ,      return_tensors=__lowerCamelCase      ,      **__lowerCamelCase      ,      )
											return text_encoding

						if not self.image_processor.is_vqa:
											# add pixel_values
											__SCREAMING_SNAKE_CASE						       =    self.image_processor(
											    __lowerCamelCase      ,      return_tensors=__lowerCamelCase      ,      max_patches=__lowerCamelCase      ,      **__lowerCamelCase       )
						else:
											# add pixel_values and bbox
											__SCREAMING_SNAKE_CASE						       =    self.image_processor(
											    __lowerCamelCase      ,      return_tensors=__lowerCamelCase      ,      max_patches=__lowerCamelCase      ,      header_text=__lowerCamelCase      ,      **__lowerCamelCase       )

						if text is not None and not self.image_processor.is_vqa:
											__SCREAMING_SNAKE_CASE						       =    self.tokenizer(
											    text=__lowerCamelCase      ,      add_special_tokens=__lowerCamelCase      ,      padding=__lowerCamelCase      ,      truncation=__lowerCamelCase      ,      max_length=__lowerCamelCase      ,      stride=__lowerCamelCase      ,      pad_to_multiple_of=__lowerCamelCase      ,      return_attention_mask=__lowerCamelCase      ,      return_overflowing_tokens=__lowerCamelCase      ,      return_special_tokens_mask=__lowerCamelCase      ,      return_offsets_mapping=__lowerCamelCase      ,      return_token_type_ids=__lowerCamelCase      ,      return_length=__lowerCamelCase      ,      verbose=__lowerCamelCase      ,      return_tensors=__lowerCamelCase      ,      **__lowerCamelCase      ,      )

											if "attention_mask" in text_encoding:
																__SCREAMING_SNAKE_CASE						       =    text_encoding.pop("attention_mask"       )
											if "input_ids" in text_encoding:
																__SCREAMING_SNAKE_CASE						       =    text_encoding.pop("input_ids"       )
						else:
											__SCREAMING_SNAKE_CASE						       =    None

						if text_encoding is not None:
											encoding_image_processor.update(__lowerCamelCase       )

						return encoding_image_processor




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      *UpperCAmelCase__      :    Any      ,      **UpperCAmelCase__      :    Union[str, Any]       )	->     int:
						return self.tokenizer.batch_decode(*__lowerCamelCase      ,      **__lowerCamelCase       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      *UpperCAmelCase__      :    List[Any]      ,      **UpperCAmelCase__      :    str       )	->     int:
						return self.tokenizer.decode(*__lowerCamelCase      ,      **__lowerCamelCase       )




	@property
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.tokenizer.model_input_names
						__SCREAMING_SNAKE_CASE						       =    self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 			UpperCamelCase_     (			UpperCamelCase      ,  UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  str    =      IFInpaintingSuperResolutionPipeline
	snake_case__    :  Optional[Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	snake_case__    :  Union[str, Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
	snake_case__    :  Dict    =      PipelineTesterMixin.required_optional_params - {"latents"}




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Optional[int]:
						return self._get_superresolution_dummy_components()




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[str]=0       )	->     List[str]:
						if str(UpperCAmelCase__       ).startswith("mps"       ):
											__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(UpperCAmelCase__       )
						else:
											__SCREAMING_SNAKE_CASE						       =    torch.Generator(device=UpperCAmelCase__       ).manual_seed(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 1_6, 1_6)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "original_image": original_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       )	->     Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Tuple:
						self._test_save_load_optional_components()




	@unittest.skipIf(torch_device != "cuda"      ,      reason="float16 requires CUDA"       )
	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Tuple:
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1E-1       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     List[Any]:
						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )




	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						self._test_save_load_local()




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Optional[Any]:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2      ,      )
