


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 	__lowercase					(	__snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							DiTPipeline
      UpperCamelCase										=							CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      UpperCamelCase										=							PipelineTesterMixin.required_optional_params - {
          '''latents''',
          '''num_images_per_prompt''',
          '''callback''',
          '''callback_steps''',
      }
      UpperCamelCase										=							CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       Optional[Any]							)     -> List[str]:






         """simple docstring"""







         torch.manual_seed(0							)
         UpperCAmelCase      					=  TransformeraDModel(
             sample_size=1_6       ,							num_layers=2       ,							patch_size=4       ,							attention_head_dim=8       ,							num_attention_heads=2       ,							in_channels=4       ,							out_channels=8       ,							attention_bias=__lowerCamelCase       ,							activation_fn="""gelu-approximate"""       ,							num_embeds_ada_norm=1_0_0_0       ,							norm_type="""ada_norm_zero"""       ,							norm_elementwise_affine=__lowerCamelCase       ,							)
         UpperCAmelCase      					=  AutoencoderKL()
         UpperCAmelCase      					=  DDIMScheduler()
         UpperCAmelCase      					=  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
         return components





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Dict=0							)     -> List[Any]:






         """simple docstring"""







         if str(__lowerCamelCase							).startswith("""mps"""							):
            UpperCAmelCase      					=  torch.manual_seed(__lowerCamelCase							)
         else:
            UpperCAmelCase      					=  torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)
         UpperCAmelCase      					=  {
             """class_labels""": [1],
             """generator""": generator,
             """num_inference_steps""": 2,
             """output_type""": """numpy""",
         }
         return inputs





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  """cpu"""

         UpperCAmelCase      					=  self.get_dummy_components()
         UpperCAmelCase      					=  self.pipeline_class(**__lowerCamelCase							)
         pipe.to(__lowerCamelCase							)
         pipe.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase							)
         UpperCAmelCase      					=  pipe(**__lowerCamelCase							).images
         UpperCAmelCase      					=  image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,							(1, 1_6, 1_6, 3)							)
         UpperCAmelCase      					=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]							)
         UpperCAmelCase      					=  np.abs(image_slice.flatten() - expected_slice							).max()
         self.assertLessEqual(__lowerCamelCase       ,							1e-3							)





      def 							_lowercase					(		self    :       Tuple							)     -> Any:






         """simple docstring"""







         self._test_inference_batch_single_identical(relax_max_difference=__lowerCamelCase       ,							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 							_lowercase					(		self    :       Union[str, Any]							)     -> Union[str, Any]:






         """simple docstring"""







         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)






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





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  torch.manual_seed(0							)

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

         UpperCAmelCase      					=  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
         UpperCAmelCase      					=  pipe.get_label_ids(__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(__lowerCamelCase       ,							generator=__lowerCamelCase       ,							num_inference_steps=4_0       ,							output_type="""np"""							).images

         for word, image in zip(__lowerCamelCase       ,							__lowerCamelCase							):
            UpperCAmelCase      					=  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 							_lowercase					(		self    :       List[str]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""							)
         UpperCAmelCase      					=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.to("""cuda"""							)

         UpperCAmelCase      					=  ["""vase""", """umbrella"""]
         UpperCAmelCase      					=  pipe.get_label_ids(__lowerCamelCase							)

         UpperCAmelCase      					=  torch.manual_seed(0							)
         UpperCAmelCase      					=  pipe(__lowerCamelCase       ,							generator=__lowerCamelCase       ,							num_inference_steps=2_5       ,							output_type="""np"""							).images

         for word, image in zip(__lowerCamelCase       ,							__lowerCamelCase							):
            UpperCAmelCase      					=  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







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 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





      def __init__(		self    :       int       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__lowerCamelCase       ,							add_special_tokens=__lowerCamelCase       ,							padding=__lowerCamelCase       ,							truncation=__lowerCamelCase       ,							max_length=__lowerCamelCase       ,							stride=__lowerCamelCase       ,							pad_to_multiple_of=__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							return_overflowing_tokens=__lowerCamelCase       ,							return_special_tokens_mask=__lowerCamelCase       ,							return_offsets_mapping=__lowerCamelCase       ,							return_length=__lowerCamelCase       ,							verbose=__lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



