"""simple docstring"""







import warnings
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	(    lowercase_						):
   lowercase							=						['image_processor', 'tokenizer']
   lowercase							=						'LayoutLMv2ImageProcessor'
   lowercase							=						('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'  ,__UpperCamelCase  ,)
             lowercase_			:       Tuple									=			kwargs.pop('feature_extractor'    )

        lowercase_			:       str									=			image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'    )
        if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'    )

        super().__init__(__UpperCamelCase  ,__UpperCamelCase    )
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = True  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = 0  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchEncoding:


        '''simple docstring'''


        if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 'You cannot provide bounding boxes '
                 'if you initialized the image processor with apply_ocr set to True.'    )

        if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'    )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'    )

        # first, apply the image processor
        lowercase_			:       List[Any]									=			self.image_processor(images=__UpperCamelCase  ,return_tensors=__UpperCamelCase    )

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                  lowercase_			:       List[Any]									=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowercase_			:       List[str]									=			features['words']

        lowercase_			:       str									=			self.tokenizer(
            text=text if text is not None else features['words']  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features['boxes']  ,word_labels=__UpperCamelCase  ,add_special_tokens=__UpperCamelCase  ,padding=__UpperCamelCase  ,truncation=__UpperCamelCase  ,max_length=__UpperCamelCase  ,stride=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_token_type_ids=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,return_overflowing_tokens=__UpperCamelCase  ,return_special_tokens_mask=__UpperCamelCase  ,return_offsets_mapping=__UpperCamelCase  ,return_length=__UpperCamelCase  ,verbose=__UpperCamelCase  ,return_tensors=__UpperCamelCase  ,**__UpperCamelCase  ,)

        # add pixel values
        lowercase_			:       Optional[Any]									=			features.pop('pixel_values'    )
        if return_overflowing_tokens is True:
             lowercase_			:       Tuple									=			self.get_overflowing_images(__UpperCamelCase  ,encoded_inputs['overflow_to_sample_mapping']    )
        lowercase_			:       List[Any]									=			images

        return encoded_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			[]
        for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]    )

        if len(__UpperCamelCase    ) != len(__UpperCamelCase    ):
             raise ValueError(
                 'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                 f''' {len(__UpperCamelCase    )} and {len(__UpperCamelCase    )}'''    )

        return images_with_overflow
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        return self.tokenizer.decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   @property
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        return ["input_ids", "bbox", "attention_mask", "image"]
   @property
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,__UpperCamelCase  ,)
        return self.image_processor_class







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,__UpperCamelCase  ,)
        return self.image_processor


"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						IFImgaImgSuperResolutionPipeline
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}						)
   lowercase							=						PipelineTesterMixin.required_optional_params - {'latents'}
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._get_superresolution_dummy_components()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       Any									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       int									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       List[str]									=			floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'original_image': original_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    )			->   Optional[Any]:


        '''simple docstring'''


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != 'cuda'  ,reason='float16 requires CUDA'    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        super().test_save_load_floataa(expected_max_diff=1e-1    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_save_load_local()







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


        '''simple docstring'''


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

