





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__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  ['''image_processor''', '''tokenizer''']
     __UpperCamelCase            =  '''Pix2StructImageProcessor'''
     __UpperCamelCase            =  ('''T5Tokenizer''', '''T5TokenizerFast''')



     def __init__(				self							:Optional[Any]      ,   snake_case							:Dict      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      False
        super().__init__(snake_case      ,   snake_case	)



     def __call__(				self							:Tuple      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,   snake_case							:bool = True      ,   snake_case							:Union[bool, str, PaddingStrategy] = False      ,   snake_case							:Union[bool, str, TruncationStrategy] = None      ,   snake_case							:Optional[int] = None      ,   snake_case							:Optional[int] = 2_048      ,   snake_case							:int = 0      ,   snake_case							:Optional[int] = None      ,   snake_case							:Optional[bool] = None      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = True      ,   snake_case							:Optional[Union[str, TensorType]] = None      ,   **snake_case							:int      ,   ):





        '''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:
           A_       :					Any        =      self.tokenizer
           A_       :					Tuple        =      self.tokenizer(
               text=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_token_type_ids=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )
           return text_encoding

        if not self.image_processor.is_vqa:
           # add pixel_values
           A_       :					Optional[Any]        =      self.image_processor(
               snake_case      ,   return_tensors=snake_case      ,   max_patches=snake_case      ,   **snake_case	)
        else:
           # add pixel_values and bbox
           A_       :					Optional[Any]        =      self.image_processor(
               snake_case      ,   return_tensors=snake_case      ,   max_patches=snake_case      ,   header_text=snake_case      ,   **snake_case	)

        if text is not None and not self.image_processor.is_vqa:
           A_       :					List[Any]        =      self.tokenizer(
               text=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_token_type_ids=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

           if "attention_mask" in text_encoding:
              A_       :					Optional[int]        =      text_encoding.pop("attention_mask"	)
           if "input_ids" in text_encoding:
              A_       :					Union[str, Any]        =      text_encoding.pop("input_ids"	)
        else:
           A_       :					Optional[Any]        =      None

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

        return encoding_image_processor



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   *snake_case							:Union[str, Any]      ,   **snake_case							:List[Any]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   *snake_case							:Any      ,   **snake_case							:int	):





        '''simple docstring'''




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





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					Dict        =      self.tokenizer.model_input_names
        A_       :					Optional[int]        =      self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)







import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        super().tearDown()
        gc.collect()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[str]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





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





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[Any]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
