
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   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		Optional[int]						     =     DiTPipeline
   A__					:		Any						     =     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   A__					:		Optional[Any]						     =     PipelineTesterMixin.required_optional_params - {
       "latents",
       "num_images_per_prompt",
       "callback",
       "callback_steps",
   }
   A__					:		List[Any]						     =     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   A__					:		Union[str, Any]						     =     False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          torch.manual_seed(0 )
          snake_case       :   Optional[int]							=      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=snake_case__    ,						activation_fn="gelu-approximate"    ,						num_embeds_ada_norm=10_00    ,						norm_type="ada_norm_zero"    ,						norm_elementwise_affine=snake_case__    ,						)
          snake_case       :   List[Any]							=      AutoencoderKL()
          snake_case       :   Dict							=      DDIMScheduler()
          snake_case       :   Optional[Any]							=      {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
          return components



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Dict=0 )		->	int:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      "cpu"

          snake_case       :   str							=      self.get_dummy_components()
          snake_case       :   Any							=      self.pipeline_class(**snake_case__ )
          pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Union[str, Any]							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   Optional[int]							=      pipe(**snake_case__ ).images
          snake_case       :   List[str]							=      image[0, -3:, -3:, -1]

          self.assertEqual(image.shape    ,						(1, 16, 16, 3) )
          snake_case       :   Union[str, Any]							=      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
          snake_case       :   Tuple							=      np.abs(image_slice.flatten() - expected_slice ).max()
          self.assertLessEqual(snake_case__    ,						1e-3 )



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


          '''simple docstring'''


          self._test_inference_batch_single_identical(relax_max_difference=snake_case__    ,						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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )






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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	str:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          snake_case       :   str							=      torch.manual_seed(0 )

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

          snake_case       :   List[Any]							=      ["vase", "umbrella", "white shark", "white wolf"]
          snake_case       :   Dict							=      pipe.get_label_ids(snake_case__ )

          snake_case       :   Optional[Any]							=      pipe(snake_case__    ,						generator=snake_case__    ,						num_inference_steps=40    ,						output_type="np" ).images

          for word, image in zip(snake_case__    ,						snake_case__ ):
                 snake_case       :   Union[str, Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Dict							=      DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
          snake_case       :   List[str]							=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
          pipe.to("cuda" )

          snake_case       :   Optional[Any]							=      ["vase", "umbrella"]
          snake_case       :   Optional[Any]							=      pipe.get_label_ids(snake_case__ )

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

          for word, image in zip(snake_case__    ,						snake_case__ ):
                 snake_case       :   Any							=      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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





