




"""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 							lowercase_			(	__UpperCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      DiTPipeline
 UpperCAmelCase       :	int     =      CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 UpperCAmelCase       :	Union[str, Any]     =      PipelineTesterMixin.required_optional_params - {
     '''latents''',
     '''num_images_per_prompt''',
     '''callback''',
     '''callback_steps''',
 }
 UpperCAmelCase       :	Optional[Any]     =      CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 UpperCAmelCase       :	Optional[Any]     =      False

 def      lowerCAmelCase_				(					self			:			str						):
       torch.manual_seed(0						)
       _A     =  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=lowerCamelCase__       ,				activation_fn='gelu-approximate'       ,				num_embeds_ada_norm=1_000       ,				norm_type='ada_norm_zero'       ,				norm_elementwise_affine=lowerCamelCase__       ,				)
       _A     =  AutoencoderKL()
       _A     =  DDIMScheduler()
       _A     =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			List[Any]=0						):
       if str(lowerCamelCase__						).startswith('mps'						):
             _A     =  torch.manual_seed(lowerCamelCase__						)
       else:
             _A     =  torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)
       _A     =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  """cpu"""

       _A     =  self.get_dummy_components()
       _A     =  self.pipeline_class(**lowerCamelCase__						)
       pipe.to(lowerCamelCase__						)
       pipe.set_progress_bar_config(disable=lowerCamelCase__						)

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

       self.assertEqual(image.shape       ,				(1, 16, 16, 3)						)
       _A     =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
       _A     =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(lowerCamelCase__       ,				1E-3						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       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      lowerCAmelCase_				(					self			:			Tuple						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)





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



 '''simple docstring'''

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

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  torch.manual_seed(0						)

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

       _A     =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _A     =  pipe.get_label_ids(lowerCamelCase__						)

       _A     =  pipe(lowerCamelCase__       ,				generator=lowerCamelCase__       ,				num_inference_steps=40       ,				output_type='np'						).images

       for word, image in zip(lowerCamelCase__       ,				lowerCamelCase__						):
             _A     =  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      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
       _A     =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to('cuda'						)

       _A     =  ["""vase""", """umbrella"""]
       _A     =  pipe.get_label_ids(lowerCamelCase__						)

       _A     =  torch.manual_seed(0						)
       _A     =  pipe(lowerCamelCase__       ,				generator=lowerCamelCase__       ,				num_inference_steps=25       ,				output_type='np'						).images

       for word, image in zip(lowerCamelCase__       ,				lowerCamelCase__						):
             _A     =  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





"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						tempfile.mkdtemp()

    lowercase__       :       List[Any]				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       str				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

    lowercase__       :       Optional[Any]				=						{
        """do_resize""": True,
        """size""": 20,
        """do_center_crop""": True,
        """crop_size""": 18,
        """do_normalize""": True,
        """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
        """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    lowercase__       :       Any				=						os.path.join(self.tmpdirname ,     lowerCamelCase__						)
    with open(self.image_processor_file ,     """w""" ,     encoding="""utf-8"""						) as fp:
     json.dump(lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizerFast.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    shutil.rmtree(self.tmpdirname						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						[np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta						)]
    lowercase__       :       Any				=						[Image.fromarray(np.moveaxis(lowerCamelCase__ ,     0 ,     -1						)						) for x in image_inputs]
    return image_inputs


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()
    lowercase__       :       Union[str, Any]				=						self.get_rust_tokenizer()
    lowercase__       :       Any				=						self.get_image_processor()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_slow.save_pretrained(self.tmpdirname						)
    lowercase__       :       Tuple				=						AlignProcessor.from_pretrained(self.tmpdirname ,     use_fast=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_fast.save_pretrained(self.tmpdirname						)
    lowercase__       :       str				=						AlignProcessor.from_pretrained(self.tmpdirname						)

    self.assertEqual(processor_slow.tokenizer.get_vocab() ,     tokenizer_slow.get_vocab()						)
    self.assertEqual(processor_fast.tokenizer.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertEqual(tokenizer_slow.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertIsInstance(processor_slow.tokenizer ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor_slow.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertEqual(processor_fast.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertIsInstance(processor_slow.image_processor ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=self.get_tokenizer() ,     image_processor=self.get_image_processor()						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__       :       Dict				=						self.get_tokenizer(bos_token="""(BOS)""" ,     eos_token="""(EOS)"""						)
    lowercase__       :       Any				=						self.get_image_processor(do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    lowercase__       :       Union[str, Any]				=						AlignProcessor.from_pretrained(
        self.tmpdirname ,     bos_token="""(BOS)""" ,     eos_token="""(EOS)""" ,     do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    self.assertEqual(processor.tokenizer.get_vocab() ,     tokenizer_add_kwargs.get_vocab()						)
    self.assertIsInstance(processor.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor.image_processor.to_json_string() ,     image_processor_add_kwargs.to_json_string()						)
    self.assertIsInstance(processor.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Dict				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						self.prepare_image_inputs()

    lowercase__       :       Tuple				=						image_processor(lowerCamelCase__ ,     return_tensors="""np"""						)
    lowercase__       :       str				=						processor(images=lowerCamelCase__ ,     return_tensors="""np"""						)

    for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum() ,     input_processor[key].sum() ,     delta=1E-2						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       Tuple				=						self.get_tokenizer()

    lowercase__       :       List[str]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Any				=						"""lower newer"""

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__						)

    lowercase__       :       Tuple				=						tokenizer(lowerCamelCase__ ,     padding="""max_length""" ,     max_length=64						)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,     encoded_processor[key]						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						self.get_image_processor()
    lowercase__       :       Any				=						self.get_tokenizer()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						"""lower newer"""
    lowercase__       :       List[str]				=						self.prepare_image_inputs()

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)

    # test if it raises when no input is passed
    with pytest.raises(lowerCamelCase__						):
     processor()


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       int				=						self.get_image_processor()
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Union[str, Any]				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    lowercase__       :       Tuple				=						processor.batch_decode(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						tokenizer.batch_decode(lowerCamelCase__						)

    self.assertListEqual(lowerCamelCase__ ,     lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       List[str]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       str				=						"""lower newer"""
    lowercase__       :       List[Any]				=						self.prepare_image_inputs()

    lowercase__       :       int				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     processor.model_input_names						)