
"""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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							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:
								A     = self.tokenizer
								A     = 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
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = 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:
								A     = 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:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

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

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp

@slow
@require_flax
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:int						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								return F'gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__							) for s in shape]							)}.npy'
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=0						, __magic_name__		:List[Any]=(4, 4, 64, 64)						, __magic_name__		:Optional[int]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return image
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=False						, __magic_name__		:List[str]="CompVis/stable-diffusion-v1-4"							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				"""bf16""" if fpaa else None

								a							,					a									=				FlaxUNetaDConditionModel.from_pretrained(
								    __magic_name__						, subfolder="""unet"""						, dtype=__magic_name__						, revision=__magic_name__							)
								return model, params
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Union[str, Any]=0						, __magic_name__		:Any=(4, 77, 768)						, __magic_name__		:Optional[Any]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return hidden_states
							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
							        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
							        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
							        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
							        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
							        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
							        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[Any]						, __magic_name__		:int						, __magic_name__		:str							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, shape=(4, 4, 96, 96)						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, shape=(4, 77, 1024)						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)

