





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   lowerCAmelCase				(   __UpperCamelCase,    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 	A_      ( self    : Dict      )     ->      Union[str, Any]:
     torch.manual_seed(0      )
     lowerCamelCase__       : List[Any]   					=						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=UpperCAmelCase     ,	activation_fn='gelu-approximate'     ,	num_embeds_ada_norm=1000     ,	norm_type='ada_norm_zero'     ,	norm_elementwise_affine=UpperCAmelCase     ,	)
     lowerCamelCase__       : Optional[int]   					=						AutoencoderKL()
     lowerCamelCase__       : Any   					=						DDIMScheduler()
     lowerCamelCase__       : List[Any]   					=						{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
     return components
 def 	A_      ( self    : str     ,	UpperCAmelCase    : Dict     ,	UpperCAmelCase    : Tuple=0      )     ->      Optional[Any]:
     if str(UpperCAmelCase      ).startswith('mps'      ):
         lowerCamelCase__       : Tuple   					=						torch.manual_seed(UpperCAmelCase      )
     else:
         lowerCamelCase__       : Dict   					=						torch.Generator(device=UpperCAmelCase      ).manual_seed(UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						{
         'class_labels': [1],
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs
 def 	A_      ( self    : List[Any]      )     ->      Any:
     lowerCamelCase__       : List[Any]   					=						'cpu'

     lowerCamelCase__       : Any   					=						self.get_dummy_components()
     lowerCamelCase__       : Tuple   					=						self.pipeline_class(**UpperCAmelCase      )
     pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						self.get_dummy_inputs(UpperCAmelCase      )
     lowerCamelCase__       : List[Any]   					=						pipe(**UpperCAmelCase      ).images
     lowerCamelCase__       : Optional[int]   					=						image[0, -3:, -3:, -1]

     self.assertEqual(image.shape     ,	(1, 16, 16, 3)      )
     lowerCamelCase__       : List[str]   					=						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__       : List[str]   					=						np.abs(image_slice.flatten() - expected_slice      ).max()
     self.assertLessEqual(UpperCAmelCase     ,	1e-3      )
 def 	A_      ( self    : int      )     ->      Optional[int]:
     self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase     ,	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    : Dict      )     ->      int:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )


@require_torch_gpu
@slow
class   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : List[str]      )     ->      Dict:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	A_      ( self    : Any      )     ->      int:
     lowerCamelCase__       : Tuple   					=						torch.manual_seed(0      )

     lowerCamelCase__       : Union[str, Any]   					=						DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'      )
     pipe.to('cuda'      )

     lowerCamelCase__       : str   					=						['vase', 'umbrella', 'white shark', 'white wolf']
     lowerCamelCase__       : Any   					=						pipe.get_label_ids(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						pipe(UpperCAmelCase     ,	generator=UpperCAmelCase     ,	num_inference_steps=40     ,	output_type='np'      ).images

     for word, image in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
         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    : Optional[Any]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'      )
     lowerCamelCase__       : Optional[int]   					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
     pipe.to('cuda'      )

     lowerCamelCase__       : Any   					=						['vase', 'umbrella']
     lowerCamelCase__       : Dict   					=						pipe.get_label_ids(UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						torch.manual_seed(0      )
     lowerCamelCase__       : List[str]   					=						pipe(UpperCAmelCase     ,	generator=UpperCAmelCase     ,	num_inference_steps=25     ,	output_type='np'      ).images

     for word, image in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
         lowerCamelCase__       : 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase							:			List[str]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			List[str]        =							{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase							:			Tuple        =							{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

_UpperCAmelCase							:			List[str]        =							{"""bert_for_seq_generation""": 5_12}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						VOCAB_FILES_NAMES
 UpperCAmelCase__						=						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__						=						[]
 UpperCAmelCase__						=						["""input_ids""", """attention_mask"""]
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : int="<s>"     ,	UpperCAmelCase    : Any="</s>"     ,	UpperCAmelCase    : List[Any]="<unk>"     ,	UpperCAmelCase    : Any="<pad>"     ,	UpperCAmelCase    : Dict="<::::>"     ,	UpperCAmelCase    : Optional[Dict[str, Any]] = None     ,	**UpperCAmelCase    : Any     ,	)     ->      None:
     lowerCamelCase__       : str   					=						{} if sp_model_kwargs is None else sp_model_kwargs

     # Add extra_ids to the special token list
     super().__init__(
         bos_token=UpperCAmelCase     ,	eos_token=UpperCAmelCase     ,	unk_token=UpperCAmelCase     ,	pad_token=UpperCAmelCase     ,	sep_token=UpperCAmelCase     ,	sp_model_kwargs=self.sp_model_kwargs     ,	**UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						vocab_file

     lowerCamelCase__       : List[str]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(UpperCAmelCase      )
 @property
 def 	A_      ( self    : List[str]      )     ->      Optional[int]:
     return self.sp_model.get_piece_size()
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						{self.convert_ids_to_tokens(UpperCAmelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab
 def __getstate__( self    : Union[str, Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : List[str]   					=						self.__dict__.copy()
     lowerCamelCase__       : List[Any]   					=						None
     return state
 def __setstate__( self    : Dict     ,	UpperCAmelCase    : Optional[int]      )     ->      List[Any]:
     lowerCamelCase__       : Optional[int]   					=						d

     # for backward compatibility
     if not hasattr(self     ,	'sp_model_kwargs'      ):
         lowerCamelCase__       : Dict   					=						{}

     lowerCamelCase__       : Optional[int]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(self.vocab_file      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : str      )     ->      List[str]:
     return self.sp_model.encode(UpperCAmelCase     ,	out_type=UpperCAmelCase      )
 def 	A_      ( self    : str     ,	UpperCAmelCase    : Tuple      )     ->      Union[str, Any]:
     return self.sp_model.piece_to_id(UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : Tuple      )     ->      Dict:
     lowerCamelCase__       : str   					=						self.sp_model.IdToPiece(UpperCAmelCase      )
     return token
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : int      )     ->      Union[str, Any]:
     lowerCamelCase__       : str   					=						[]
     lowerCamelCase__       : List[str]   					=						''
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             out_string += self.sp_model.decode(UpperCAmelCase      ) + token
             lowerCamelCase__       : Tuple   					=						[]
         else:
             current_sub_tokens.append(UpperCAmelCase      )
     out_string += self.sp_model.decode(UpperCAmelCase      )
     return out_string.strip()







 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[str] = None      )     ->      Tuple[str]:
     if not os.path.isdir(UpperCAmelCase      ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
         return
     lowerCamelCase__       : Any   					=						os.path.join(
         UpperCAmelCase     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
         copyfile(self.vocab_file     ,	UpperCAmelCase      )
     elif not os.path.isfile(self.vocab_file      ):
         with open(UpperCAmelCase     ,	'wb'      ) as fi:
             lowerCamelCase__       : int   					=						self.sp_model.serialized_model_proto()
             fi.write(UpperCAmelCase      )

     return (out_vocab_file,)





