"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Any	      =	["image_processor", "tokenizer"]
     _UpperCamelCase							:    Any	      =	"AutoImageProcessor"
     _UpperCamelCase							:    Union[str, Any]	      =	"AutoTokenizer"






     def __init__(	self       ,  a__=None       ,  a__=None       ,  **a__						):
       _lowerCAmelCase		: int						    =  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__       ,  )
         _lowerCAmelCase		: List[Any]						    =  kwargs.pop("""feature_extractor"""						)

       _lowerCAmelCase		: List[Any]						    =  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__						)
       _lowerCAmelCase		: Optional[int]						    =  self.image_processor
       _lowerCAmelCase		: Optional[int]						    =  False






     def __call__(	self       ,  *a__       ,  **a__						):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*a__       ,  **a__						)

       _lowerCAmelCase		: str						    =  kwargs.pop("""images"""       ,  a__						)
       _lowerCAmelCase		: int						    =  kwargs.pop("""text"""       ,  a__						)
       if len(a__						) > 0:
         _lowerCAmelCase		: str						    =  args[0]
         _lowerCAmelCase		: List[Any]						    =  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:
         _lowerCAmelCase		: Optional[int]						    =  self.image_processor(a__       ,  *a__       ,  **a__						)
       if text is not None:
         _lowerCAmelCase		: Any						    =  self.tokenizer(a__       ,  **a__						)

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         _lowerCAmelCase		: int						    =  encodings["""input_ids"""]
         return inputs






     def 					__A						(	self       ,  *a__       ,  **a__						):
       return self.tokenizer.batch_decode(*a__       ,  **a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       return self.tokenizer.decode(*a__       ,  **a__						)






     @contextmanager
     def 					__A						(	self						):
       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."""						)
       _lowerCAmelCase		: List[str]						    =  True
       _lowerCAmelCase		: Optional[int]						    =  self.tokenizer
       yield
       _lowerCAmelCase		: Dict						    =  self.image_processor
       _lowerCAmelCase		: str						    =  False






     def 					__A						(	self       ,  a__       ,  a__=False       ,  a__=None						):
       if added_vocab is None:
         _lowerCAmelCase		: str						    =  self.tokenizer.get_added_vocab()

       _lowerCAmelCase		: Dict						    =  {}

       while tokens:
         _lowerCAmelCase		: Any						    =  re.search(r"""<s_(.*?)>"""       ,  a__       ,  re.IGNORECASE						)
         if start_token is None:
           break
         _lowerCAmelCase		: List[str]						    =  start_token.group(1						)
         _lowerCAmelCase		: List[str]						    =  re.search(rF"</s_{key}>"       ,  a__       ,  re.IGNORECASE						)
         _lowerCAmelCase		: str						    =  start_token.group()
         if end_token is None:
           _lowerCAmelCase		: Optional[int]						    =  tokens.replace(a__       ,  """"""						)
         else:
           _lowerCAmelCase		: Tuple						    =  end_token.group()
           _lowerCAmelCase		: List[Any]						    =  re.escape(a__						)
           _lowerCAmelCase		: Any						    =  re.escape(a__						)
           _lowerCAmelCase		: Dict						    =  re.search(F"{start_token_escaped}(.*?){end_token_escaped}"       ,  a__       ,  re.IGNORECASE						)
           if content is not None:
             _lowerCAmelCase		: List[str]						    =  content.group(1						).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               _lowerCAmelCase		: int						    =  self.tokenajson(a__       ,  is_inner_value=a__       ,  added_vocab=a__						)
               if value:
                 if len(a__						) == 1:
                   _lowerCAmelCase		: int						    =  value[0]
                 _lowerCAmelCase		: Optional[int]						    =  value
             else:  # leaf nodes
               _lowerCAmelCase		: Tuple						    =  []
               for leaf in content.split(r"""<sep/>"""						):
                 _lowerCAmelCase		: Optional[int]						    =  leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   _lowerCAmelCase		: Dict						    =  leaf[1:-2]  # for categorical special tokens
                 output[key].append(a__						)
               if len(output[key]						) == 1:
                 _lowerCAmelCase		: List[str]						    =  output[key][0]

           _lowerCAmelCase		: Tuple						    =  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 					__A						(	self						):
       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 					__A						(	self						):
       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

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







@require_torch_gpu
@slow
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




