


"""simple docstring"""



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 					__magic_name__		(			UpperCamelCase__     ):
							__A			:      Union[str, Any]        =   ["image_processor", "tokenizer"]
							__A			:      Optional[Any]        =   "Pix2StructImageProcessor"
							__A			:      Union[str, Any]        =   ("T5Tokenizer", "T5TokenizerFast")






							def __init__(   self  :    List[str]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Dict				):

														'''simple docstring'''






														lowercase		:Dict       							=				False
														super().__init__(_a  ,     _a				)






							def __call__(   self  :    str  ,     snake_case__  :    int=None  ,     snake_case__  :    List[str] = None  ,     snake_case__  :    Union[str, Any] = True  ,     snake_case__  :    List[str] = False  ,     snake_case__  :    str = None  ,     snake_case__  :    Optional[Any] = None  ,     snake_case__  :    Union[str, Any] = 2_0_4_8  ,     snake_case__  :    Tuple = 0  ,     snake_case__  :    Dict = None  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    Any = False  ,     snake_case__  :    List[str] = False  ,     snake_case__  :    List[Any] = False  ,     snake_case__  :    Optional[Any] = False  ,     snake_case__  :    Union[str, Any] = False  ,     snake_case__  :    Any = True  ,     snake_case__  :    str = None  ,     **snake_case__  :    List[str]  ,     ):

														'''simple docstring'''






														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:
																					lowercase		:List[str]       							=				self.tokenizer
																					lowercase		:List[Any]       							=				self.tokenizer(
																					    text=_a  ,     add_special_tokens=_a  ,     padding=_a  ,     truncation=_a  ,     max_length=_a  ,     stride=_a  ,     pad_to_multiple_of=_a  ,     return_attention_mask=_a  ,     return_overflowing_tokens=_a  ,     return_special_tokens_mask=_a  ,     return_offsets_mapping=_a  ,     return_token_type_ids=_a  ,     return_length=_a  ,     verbose=_a  ,     return_tensors=_a  ,     **_a  ,     )
																					return text_encoding

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

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

																					if "attention_mask" in text_encoding:
																												lowercase		:List[str]       							=				text_encoding.pop('''attention_mask'''				)
																					if "input_ids" in text_encoding:
																												lowercase		:str       							=				text_encoding.pop('''input_ids'''				)
														else:
																					lowercase		:Union[str, Any]       							=				None

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

														return encoding_image_processor






							def  __snake_case						(   self  :    Union[str, Any]  ,     *snake_case__  :    Optional[Any]  ,     **snake_case__  :    int				):

														'''simple docstring'''






														return self.tokenizer.batch_decode(*_a  ,     **_a				)






							def  __snake_case						(   self  :    str  ,     *snake_case__  :    str  ,     **snake_case__  :    Any				):

														'''simple docstring'''






														return self.tokenizer.decode(*_a  ,     **_a				)







							@property
							def  __snake_case						(   self  :    Dict				):

														'''simple docstring'''






														lowercase		:Union[str, Any]       							=				self.tokenizer.model_input_names
														lowercase		:Dict       							=				self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





"""simple docstring"""



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
 import jax
@require_flax
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   FlaxAutoencoderKL






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				3
              lowercase		:Any       							=				(3_2, 3_2)

              lowercase		:Optional[int]       							=				jax.random.PRNGKey(0				)
              lowercase		:Any       							=				jax.random.uniform(snake_case__  ,     ((batch_size, num_channels) + sizes)				)

              return {"sample": image, "prng_key": prng_key}







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowercase		:Optional[int]       							=				self.dummy_input
              return init_dict, inputs_dict

