
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 					_SCREAMING_SNAKE_CASE  (      _snake_case       ,					unittest.TestCase ):
      _A						:		str    	=     DiTPipeline
      _A						:		Optional[int]    	=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      _A						:		Any    	=     PipelineTesterMixin.required_optional_params - {
          """latents""",
          """num_images_per_prompt""",
          """callback""",
          """callback_steps""",
      }
      _A						:		Optional[int]    	=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      _A						:		Optional[int]    	=     False
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				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__						=				AutoencoderKL()
          snake_case__						=				DDIMScheduler()
          snake_case__						=				{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
          return components
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0 ):
          if str(snake_case_ ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(snake_case_ )
          else:
              snake_case__						=				torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
          snake_case__						=				{
              "class_labels": [1],
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs
      def A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()
          snake_case__						=				self.pipeline_class(**snake_case_ )
          pipe.to(snake_case_ )
          pipe.set_progress_bar_config(disable=snake_case_ )

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

          self.assertEqual(image.shape		,	(1, 16, 16, 3) )
          snake_case__						=				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] )
          snake_case__						=				np.abs(image_slice.flatten() - expected_slice ).max()
          self.assertLessEqual(snake_case_		,	1e-3 )
      def A_			(			self ):
          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 A_			(			self ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )





@require_torch_gpu
@slow
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def A_			(			self ):
          snake_case__						=				torch.manual_seed(0 )

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

          snake_case__						=				["vase", "umbrella", "white shark", "white wolf"]
          snake_case__						=				pipe.get_label_ids(snake_case_ )

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

          snake_case__						=				["vase", "umbrella"]
          snake_case__						=				pipe.get_label_ids(snake_case_ )

          snake_case__						=				torch.manual_seed(0 )
          snake_case__						=				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__						=				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 List, Optional, Tuple

import sentencepiece as spm

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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



