

"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE_    ):



    '''simple docstring'''




    _lowerCamelCase					=   ['''image_processor''', '''tokenizer''']
    _lowerCamelCase					=   '''AutoImageProcessor'''
    _lowerCamelCase					=   '''AutoTokenizer'''




    def __init__( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead.""" ,__a ,)
                A				  =		kwargs.pop("""feature_extractor"""    )

          A				  =		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__(__a ,__a    )
          A				  =		self.image_processor
          A				  =		False




    def __call__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      Dict:
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*__a ,**__a    )

          A				  =		kwargs.pop("""images""" ,__a    )
          A				  =		kwargs.pop("""text""" ,__a    )
          if len(__a    ) > 0:
                A				  =		args[0]
                A				  =		args[1:]

          if images is None and text is None:
                raise ValueError("""You need to specify either an `images` or `text` input to process."""    )

          if images is not None:
                A				  =		self.image_processor(__a ,*__a ,**__a    )
          if text is not None:
                A				  =		self.tokenizer(__a ,**__a    )

          if text is None:
                return inputs
          elif images is None:
                return encodings
          else:
                A				  =		encodings['input_ids']
                return inputs




    def UpperCamelCase__		( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      List[str]:
          return self.tokenizer.batch_decode(*__a ,**__a    )




    def UpperCamelCase__		( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      List[Any]:
          return self.tokenizer.decode(*__a ,**__a    )




    @contextmanager
    def UpperCamelCase__		( self    )    ->      List[Any]:
          warnings.warn(
              """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
              """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
              """your images inputs, or in a separate call."""    )
          A				  =		True
          A				  =		self.tokenizer
          yield
          A				  =		self.image_processor
          A				  =		False




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=None    )    ->      List[str]:
          if added_vocab is None:
                A				  =		self.tokenizer.get_added_vocab()

          A				  =		{}

          while tokens:
                A				  =		re.search(r"""<s_(.*?)>""" ,__a ,re.IGNORECASE    )
                if start_token is None:
                      break
                A				  =		start_token.group(1    )
                A				  =		re.search(rf'</s_{key}>' ,__a ,re.IGNORECASE    )
                A				  =		start_token.group()
                if end_token is None:
                      A				  =		tokens.replace(__a ,""""""    )
                else:
                      A				  =		end_token.group()
                      A				  =		re.escape(__a    )
                      A				  =		re.escape(__a    )
                      A				  =		re.search(f'{start_token_escaped}(.*?){end_token_escaped}' ,__a ,re.IGNORECASE    )
                      if content is not None:
                            A				  =		content.group(1    ).strip()
                            if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                  A				  =		self.tokenajson(__a ,is_inner_value=__a ,added_vocab=__a    )
                                  if value:
                                        if len(__a    ) == 1:
                                              A				  =		value[0]
                                        A				  =		value
                            else:  # leaf nodes
                                  A				  =		[]
                                  for leaf in content.split(r"""<sep/>"""    ):
                                        A				  =		leaf.strip()
                                        if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                              A				  =		leaf[1:-2]  # for categorical special tokens
                                        output[key].append(__a    )
                                  if len(output[key]    ) == 1:
                                        A				  =		output[key][0]

                      A				  =		tokens[tokens.find(__a    ) + len(__a    ) :].strip()
                      if tokens[:6] == r"<sep/>":  # non-leaf nodes
                            return [output] + self.tokenajson(tokens[6:] ,is_inner_value=__a ,added_vocab=__a    )

          if len(__a    ):
                return [output] if is_inner_value else output
          else:
                return [] if is_inner_value else {"text_sequence": tokens}




    @property
    def UpperCamelCase__		( self    )    ->      Any:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,__a ,)
          return self.image_processor_class




    @property
    def UpperCamelCase__		( self    )    ->      Optional[int]:
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,__a ,)
          return self.image_processor









'''simple docstring'''





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  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

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





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

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



